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992b0925-6c32-43a7-9100-9d48f54bcd54	deep-inductive-logic-reasoning-for-multi-hop	null	null	https://aclanthology.org/2022.acl-long.343	https://aclanthology.org/2022.acl-long.343.pdf	Deep Inductive Logic Reasoning for Multi-Hop Reading Comprehension	Multi-hop reading comprehension requires an ability to reason across multiple documents. On the one hand, deep learning approaches only implicitly encode query-related information into distributed embeddings which fail to uncover the discrete relational reasoning process to infer the correct answer. On the other hand, logic-based approaches provide interpretable rules to infer the target answer, but mostly work on structured data where entities and relations are well-defined. In this paper, we propose a deep-learning based inductive logic reasoning method that firstly extracts query-related (candidate-related) information, and then conducts logic reasoning among the filtered information by inducing feasible rules that entail the target relation. The reasoning process is accomplished via attentive memories with novel differentiable logic operators. To demonstrate the effectiveness of our model, we evaluate it on two reading comprehension datasets, namely WikiHop and MedHop.	['Sinno Pan', 'Wenya Wang']	null	null	null	null	acl-2022-5	['relational-reasoning', 'multi-hop-reading-comprehension']	['natural-language-processing', 'natural-language-processing']	[ 7.51251504e-02 7.54693091e-01 -3.13571721e-01 -6.98204637e-01 -5.76413453e-01 -5.43612242e-01 3.38173658e-01 8.81447971e-01 -2.54237652e-01 5.60897768e-01 5.19086421e-01 -6.56978607e-01 -8.80560338e-01 -1.53194582e+00 -9.72002864e-01 9.21302363e-02 -4.32931148e-02 8.93504381e-01 2.08315313e-01 -3.24333578e-01 2.79346369e-02 -1.43943084e-02 -1.49881387e+00 5.86351514e-01 1.33475924e+00 9.91642118e-01 -1.82051271e-01 4.97455537e-01 -5.63313246e-01 1.56298792e+00 -1.45979315e-01 -8.82677019e-01 -2.76511878e-01 -3.16413850e-01 -1.39151740e+00 -7.12771058e-01 4.09499019e-01 -6.69280052e-01 -4.57687467e-01 1.17053878e+00 -9.18452814e-02 1.51685104e-01 5.96184969e-01 -8.09125960e-01 -1.34618092e+00 1.30756342e+00 1.16253950e-01 1.21014826e-01 9.17266905e-01 -2.12395340e-01 1.77468002e+00 -8.93623054e-01 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048a1b7a-7814-4528-b075-f792d5b3b024	adapting-bert-to-implicit-discourse-relation	null	null	https://aclanthology.org/2020.lrec-1.145	https://aclanthology.org/2020.lrec-1.145.pdf	Adapting BERT to Implicit Discourse Relation Classification with a Focus on Discourse Connectives	BERT, a neural network-based language model pre-trained on large corpora, is a breakthrough in natural language processing, significantly outperforming previous state-of-the-art models in numerous tasks. However, there have been few reports on its application to implicit discourse relation classification, and it is not clear how BERT is best adapted to the task. In this paper, we test three methods of adaptation. (1) We perform additional pre-training on text tailored to discourse classification. (2) In expectation of knowledge transfer from explicit discourse relations to implicit discourse relations, we add a task named explicit connective prediction at the additional pre-training step. (3) To exploit implicit connectives given by treebank annotators, we add a task named implicit connective prediction at the fine-tuning step. We demonstrate that these three techniques can be combined straightforwardly in a single training pipeline. Through comprehensive experiments, we found that the first and second techniques provide additional gain while the last one did not.	['Yugo Murawaki', 'Yudai Kishimoto', 'Sadao Kurohashi']	2020-05-01	null	null	null	lrec-2020-5	['implicit-discourse-relation-classification']	['natural-language-processing']	[ 4.41894025e-01 1.14003336e+00 -3.33307981e-01 -4.25717205e-01 -5.08569419e-01 -6.00225866e-01 8.90634120e-01 5.19617915e-01 -6.11511230e-01 9.77848172e-01 4.33705807e-01 -7.60166168e-01 9.88788307e-02 -6.41129911e-01 -4.64435130e-01 -2.34668061e-01 -2.20924258e-01 8.27585995e-01 5.31156421e-01 -4.23466474e-01 2.56344080e-01 1.67630255e-01 -9.16477740e-01 7.89810240e-01 9.26381290e-01 7.95248449e-01 -6.06837217e-03 5.46165764e-01 -4.03042585e-01 1.54045033e+00 -7.13694394e-01 -4.56442624e-01 -1.67531118e-01 -3.32356095e-01 -1.44432020e+00 -2.65286416e-01 8.17266945e-03 -2.87882954e-01 -4.66398112e-02 5.50763726e-01 2.04430759e-01 1.31057367e-01 6.65884018e-01 -7.40212977e-01 -7.86086380e-01 1.13670492e+00 -1.52363852e-01 1.83026791e-01 3.87221336e-01 -1.11089244e-01 1.22377002e+00 -7.22763419e-01 8.47016156e-01 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d2a7f9ea-8a98-408b-8aac-cab3c1fc30f8	tripartite-information-mining-and-integration	null	null	http://openaccess.thecvf.com//content/ICCV2021/html/Liu_Tripartite_Information_Mining_and_Integration_for_Image_Matting_ICCV_2021_paper.html	http://openaccess.thecvf.com//content/ICCV2021/papers/Liu_Tripartite_Information_Mining_and_Integration_for_Image_Matting_ICCV_2021_paper.pdf	Tripartite Information Mining and Integration for Image Matting	With the development of deep convolutional neural networks, image matting has ushered in a new phase. Regarding the nature of image matting, most researches have focused on solutions for transition regions. However, we argue that many existing approaches are excessively focused on transition-dominant local fields and ignored the inherent coordination between global information and transition optimisation. In this paper, we propose the Tripartite Information Mining and Integration Network (TIMI-Net) to harmonize the coordination between global and local attributes formally. Specifically, we resort to a novel 3-branch encoder to accomplish comprehensive mining of the input information, which can supplement the neglected coordination between global and local fields. In order to achieve effective and complete interaction between such multi-branches information, we develop the Tripartite Information Integration (TI^2) Module to transform and integrate the interconnections between the different branches. In addition, we built a large-scale human matting dataset (Human-2K) to advance human image matting, which consists of 2100 high-precision human images (2000 images for training and 100 images for test). Finally, we conduct extensive experiments to prove the performance of our proposed TIMI-Net, which demonstrates that our method performs favourably against the SOTA approaches on the alphamatting.com (Rank First), Composition-1K (MSE-0.006, Grad-11.5), Distinctions-646 and our Human-2K. Also, we have developed an online evaluation website to perform natural image matting. Project page: https://wukaoliu.github.io/TIMI-Net.	['Xin Yang', 'Yong Tang', 'Yujie Huang', 'Yu Qiao', 'Xiao Shi', 'Jiake Xie', 'Yuhao Liu']	2021-01-01	null	null	null	iccv-2021-1	['image-matting']	['computer-vision']	[ 5.44732548e-02 -1.89108998e-01 -1.99547783e-02 -3.09480190e-01 -5.04737496e-01 -1.45670339e-01 4.41048622e-01 -2.61249393e-01 -3.59033376e-01 5.58219254e-01 3.98947299e-02 -3.78074229e-01 -1.71401069e-01 -9.31317449e-01 -9.47023213e-01 -5.68253517e-01 -3.35124396e-02 3.20287228e-01 3.44273373e-02 -2.81385183e-01 1.55972406e-01 3.93760242e-02 -1.49638343e+00 5.43157160e-01 1.25543737e+00 1.13886464e+00 3.52890253e-01 4.31784123e-01 -1.55804291e-01 6.67171359e-01 -5.18817723e-01 -5.82187891e-01 4.77312088e-01 -3.29321027e-01 -8.96141946e-01 1.55163094e-01 4.12230819e-01 -3.48876506e-01 -2.46937051e-01 1.22602582e+00 4.94901776e-01 -2.04433486e-01 4.02442485e-01 -1.31683481e+00 -7.30458319e-01 7.09889293e-01 -8.44482780e-01 3.44434939e-02 4.19709459e-02 5.23831904e-01 7.69109666e-01 -8.66178751e-01 5.26449621e-01 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-7.07008898e-01 -3.21570754e-01 -1.11328435e+00 -3.77767652e-01 4.43579793e-01 -1.44245446e-01 -5.06468415e-01 -1.43791035e-01 8.88578817e-02]	[10.641104698181152, -0.8977043032646179]
cf66c6db-12ff-4540-94f3-d2676c6c41c4	user-assisted-shadow-removal	null	null	https://www.sciencedirect.com/science/article/pii/S0262885617300744?via%3Dihub	https://www.sciencedirect.com/science/article/pii/S0262885617300744?via%3Dihub	User-Assisted Shadow Removal	This paper presents a novel user-aided method for texture-preserving shadow removal from single images requiring simple user input. Compared with the state-of-the-art, our algorithm offers the most flexible user interaction to date and produces more accurate and robust shadow removal under thorough quantitative evaluation. Shadow masks are first detected by analysing user specified shadow feature strokes. Sample intensity profiles with variable interval and length around the shadow boundary are detected next, which avoids artefacts raised from uneven boundaries. Texture noise in samples is then removed by applying local group bilateral filtering, and initial sparse shadow scales are estimated by fitting a piecewise curve to intensity samples. The remaining errors in estimated sparse scales are removed by local group smoothing. To relight the image, a dense scale field is produced by in-painting the sparse scales. Finally, a gradual colour correction is applied to remove artefacts due to image post-processing. Using state-of-the-art evaluation data, we quantitatively and qualitatively demonstrate our method to outperform current leading shadow removal methods.	['Han Gong; Darren Cosker']	2018-04-18	null	null	null	image-and-vision-computing-2018-4	['shadow-removal']	['computer-vision']	[ 1.20905674e+00 1.01948850e-01 4.97430772e-01 -3.51292670e-01 -5.23495197e-01 -4.02963728e-01 3.58992457e-01 -6.65176958e-02 -1.97074324e-01 8.07659805e-01 6.45146444e-02 -1.68434709e-01 1.84100002e-01 -4.86988902e-01 -2.32479170e-01 -8.49713922e-01 -5.73696978e-02 2.16714501e-01 9.72308218e-01 -1.93922877e-01 5.04004359e-01 7.99211383e-01 -1.61836493e+00 4.11740035e-01 1.27843666e+00 9.21096563e-01 4.06194717e-01 8.45666230e-01 -2.99682915e-01 5.13664901e-01 -8.80534828e-01 -1.16402380e-01 2.22624108e-01 -6.24330401e-01 -1.73310280e-01 5.12982905e-01 5.29678047e-01 -6.05281353e-01 1.33497745e-01 6.49458647e-01 5.69442928e-01 1.26558468e-01 6.57354474e-01 -9.87968087e-01 -1.57511070e-01 -4.79250848e-01 -9.16306496e-01 -1.22317314e-01 5.61325848e-01 -7.79303834e-02 3.92761707e-01 -1.18321311e+00 8.24405432e-01 1.30388355e+00 8.77865314e-01 4.17241119e-02 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9a8710e2-d54a-4993-bf21-ff16a698db69	towards-single-word-lexical-complexity	null	null	https://aclanthology.org/W18-0508	https://aclanthology.org/W18-0508.pdf	Towards Single Word Lexical Complexity Prediction	In this paper we present work-in-progress where we investigate the usefulness of previously created word lists to the task of single-word lexical complexity analysis and prediction of the complexity level for learners of Swedish as a second language. The word lists used map each word to a single CEFR level, and the task consists of predicting CEFR levels for unseen words. In contrast to previous work on word-level lexical complexity, we experiment with topics as additional features and show that linking words to topics significantly increases accuracy of classification.	['David Alfter', 'Elena Volodina']	2018-06-01	null	null	null	ws-2018-6	['lexical-complexity-prediction']	['natural-language-processing']	[-6.03051484e-03 1.08872645e-01 -2.87997305e-01 -3.02633226e-01 -9.38820958e-01 -5.28018713e-01 6.51685655e-01 8.33847702e-01 -9.63942051e-01 5.49530864e-01 5.65902233e-01 -6.83581889e-01 -1.88444197e-01 -5.67430019e-01 -1.28032684e-01 4.39096149e-03 -1.15702443e-01 5.84638596e-01 5.87275982e-01 -5.51523626e-01 7.06523120e-01 1.46043092e-01 -1.87126029e+00 4.96233642e-01 7.42002249e-01 4.13165718e-01 5.93279421e-01 8.12477887e-01 -5.39022624e-01 4.04022396e-01 -9.02291298e-01 -2.74221748e-01 -1.01422861e-01 -4.39328194e-01 -1.00418293e+00 -1.52786747e-01 6.18976891e-01 4.03956622e-01 5.91855682e-02 9.14662182e-01 2.91549087e-01 2.61110872e-01 8.22205365e-01 -5.26538193e-01 -2.50250638e-01 9.30064559e-01 1.06146827e-01 8.32614243e-01 8.94434333e-01 -4.21406120e-01 1.13645864e+00 -9.00726557e-01 7.06848741e-01 1.33631361e+00 7.79483855e-01 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3e3f2006-e2ac-487d-b1f3-93d37de293af	improved-answer-selection-with-pre-trained	1708.04326	null	http://arxiv.org/abs/1708.04326v1	http://arxiv.org/pdf/1708.04326v1.pdf	Improved Answer Selection with Pre-Trained Word Embeddings	This paper evaluates existing and newly proposed answer selection methods based on pre-trained word embeddings. Word embeddings are highly effective in various natural language processing tasks and their integration into traditional information retrieval (IR) systems allows for the capture of semantic relatedness between questions and answers. Empirical results on three publicly available data sets show significant gains over traditional term frequency based approaches in both supervised and unsupervised settings. We show that combining these word embedding features with traditional learning-to-rank techniques can achieve similar performance to state-of-the-art neural networks trained for the answer selection task.	['Santos Cicero Nogueira dos', 'Navratil Jiri', 'Chakravarti Rishav']	2017-08-14	null	null	null	null	['answer-selection']	['natural-language-processing']	[-4.39633504e-02 -2.29780942e-01 -6.71751916e-01 -3.88769478e-01 -9.96863723e-01 -4.06491369e-01 7.19195664e-01 1.04448307e+00 -1.25147200e+00 3.49524856e-01 7.24198520e-01 -3.08380067e-01 -6.45371914e-01 -9.12347674e-01 1.69598311e-01 -2.23446637e-03 -1.51805490e-01 8.48256528e-01 5.33657610e-01 -6.75182283e-01 9.51452076e-01 9.18563008e-02 -1.68567717e+00 2.56114274e-01 7.01129258e-01 1.06094646e+00 -2.12925851e-01 7.97055244e-01 -9.31029201e-01 8.09441984e-01 -4.73693550e-01 -4.91675198e-01 -1.37033209e-01 1.03877001e-01 -1.33088088e+00 -8.16756845e-01 7.18666732e-01 -2.89977044e-01 -9.22246933e-01 6.35519564e-01 6.66782141e-01 9.18768167e-01 8.69994879e-01 -6.78753674e-01 -1.66444933e+00 9.04236361e-02 -1.03627659e-01 7.65468836e-01 1.00660336e+00 -5.59312344e-01 1.90299952e+00 -1.28947294e+00 5.78996599e-01 1.38839614e+00 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62219f81-c36a-4926-820c-8ad5a31322c7	3d-reconstruction-using-structure-for-motion	2306.06360	null	https://arxiv.org/abs/2306.06360v1	https://arxiv.org/pdf/2306.06360v1.pdf	3D reconstruction using Structure for Motion	We are working towards 3D reconstruction of indoor spaces using a pair of HDR cameras in a stereo vision configuration mounted on an indoor mobile floor robot that captures various textures and spatial features as 2D images and this data is simultaneously utilized as a feed to our algorithm which will allow us to visualize the depth map.	['Raajith Gadam', 'Mudit Singal', 'Abhimanyu Saxena', 'Hritvik Choudhari', 'Kshitij Karnawat']	2023-06-10	null	null	null	null	['3d-reconstruction']	['computer-vision']	[ 2.12336823e-01 -3.00063372e-01 5.49312115e-01 -4.66589630e-01 2.22013056e-01 -4.44602549e-01 4.82180595e-01 -1.84998468e-01 -3.69711518e-01 6.27296507e-01 2.00595856e-01 -3.21805656e-01 2.81779289e-01 -1.08836484e+00 -5.32507539e-01 -2.66360253e-01 2.13401571e-01 5.06022155e-01 4.69940692e-01 -3.63437951e-01 3.55917811e-01 7.16666102e-01 -2.19991040e+00 1.54999316e-01 9.17235762e-02 8.39840651e-01 9.61289883e-01 1.19339025e+00 1.34653717e-01 7.47328162e-01 2.41279732e-02 2.29324967e-01 7.00919032e-01 2.01949269e-01 -6.58337712e-01 6.39122367e-01 4.61031586e-01 -5.50318360e-01 -5.31737804e-01 8.78789008e-01 1.61164612e-01 7.06754699e-02 4.87758875e-01 -5.42797089e-01 9.00603458e-03 -5.95947385e-01 -3.86071771e-01 1.47970989e-01 1.46210265e+00 -1.68640241e-01 3.52962732e-01 -1.07667756e+00 1.03218877e+00 1.21868265e+00 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6aa069e0-5a6d-400b-ba1a-272ec128c0e6	multilingual-relation-classification-via	2210.13838	null	https://arxiv.org/abs/2210.13838v2	https://arxiv.org/pdf/2210.13838v2.pdf	Multilingual Relation Classification via Efficient and Effective Prompting	Prompting pre-trained language models has achieved impressive performance on various NLP tasks, especially in low data regimes. Despite the success of prompting in monolingual settings, applying prompt-based methods in multilingual scenarios has been limited to a narrow set of tasks, due to the high cost of handcrafting multilingual prompts. In this paper, we present the first work on prompt-based multilingual relation classification (RC), by introducing an efficient and effective method that constructs prompts from relation triples and involves only minimal translation for the class labels. We evaluate its performance in fully supervised, few-shot and zero-shot scenarios, and analyze its effectiveness across 14 languages, prompt variants, and English-task training in cross-lingual settings. We find that in both fully supervised and few-shot scenarios, our prompt method beats competitive baselines: fine-tuning XLM-R_EM and null prompts. It also outperforms the random baseline by a large margin in zero-shot experiments. Our method requires little in-language knowledge and can be used as a strong baseline for similar multilingual classification tasks.	['Leonhard Hennig', 'David Harbecke', 'Yuxuan Chen']	2022-10-25	null	null	null	null	['relation-classification']	['natural-language-processing']	[-6.89818263e-02 2.12962776e-01 -8.15100014e-01 -3.63291889e-01 -1.63750374e+00 -7.54190624e-01 1.06581438e+00 4.43320364e-01 -7.72234082e-01 9.77913320e-01 5.66707551e-01 -7.71388829e-01 6.10686429e-02 -2.02330172e-01 -6.55869722e-01 -3.89451474e-01 1.57958820e-01 8.67284298e-01 8.95513296e-02 -5.75269222e-01 -3.23298365e-01 2.44739931e-02 -1.05618477e+00 1.37596712e-01 7.88556218e-01 4.39647794e-01 1.44336507e-01 5.32521725e-01 -1.53968707e-01 1.13097358e+00 -3.46312076e-01 -6.17267549e-01 9.26014557e-02 -2.18823567e-01 -1.17666674e+00 -3.24808061e-01 7.00542390e-01 -6.93951249e-02 -4.52193022e-01 6.49244070e-01 9.66327310e-01 3.25368047e-01 6.12111211e-01 -9.77409899e-01 -7.71043003e-01 1.13413143e+00 -2.49050960e-01 3.84399861e-01 8.21338832e-01 -9.72868875e-03 1.45478392e+00 -1.07572663e+00 9.30047572e-01 1.24994862e+00 6.90771997e-01 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177df2a7-8ad5-402c-bf8c-00967349fdd4	flowface-explicit-semantic-flow-supervised	2306.12686	null	https://arxiv.org/abs/2306.12686v2	https://arxiv.org/pdf/2306.12686v2.pdf	FlowFace++: Explicit Semantic Flow-supervised End-to-End Face Swapping	This work proposes a novel face-swapping framework FlowFace++, utilizing explicit semantic flow supervision and end-to-end architecture to facilitate shape-aware face-swapping. Specifically, our work pretrains a facial shape discriminator to supervise the face swapping network. The discriminator is shape-aware and relies on a semantic flow-guided operation to explicitly calculate the shape discrepancies between the target and source faces, thus optimizing the face swapping network to generate highly realistic results. The face swapping network is a stack of a pre-trained face-masked autoencoder (MAE), a cross-attention fusion module, and a convolutional decoder. The MAE provides a fine-grained facial image representation space, which is unified for the target and source faces and thus facilitates final realistic results. The cross-attention fusion module carries out the source-to-target face swapping in a fine-grained latent space while preserving other attributes of the target image (e.g. expression, head pose, hair, background, illumination, etc). Lastly, the convolutional decoder further synthesizes the swapping results according to the face-swapping latent embedding from the cross-attention fusion module. Extensive quantitative and qualitative experiments on in-the-wild faces demonstrate that our FlowFace++ outperforms the state-of-the-art significantly, particularly while the source face is obstructed by uneven lighting or angle offset.	['Changjie Fan', 'Tangjie Lv', 'Yu Ding', 'Zhimeng Zhang', 'Wei zhang', 'Bowen Ma', 'Hao Zeng', 'Yu Zhang']	2023-06-22	null	null	null	null	['face-swapping']	['computer-vision']	[ 9.72569808e-02 2.38232881e-01 1.56195149e-01 -8.09431553e-01 -5.13816893e-01 -2.76537955e-01 4.83435690e-01 -8.61690402e-01 1.77613702e-02 3.81353974e-01 3.89505804e-01 2.83390284e-01 3.14783901e-01 -7.06579268e-01 -9.06771421e-01 -8.69902790e-01 3.09741437e-01 1.27837583e-01 -3.08598220e-01 -2.42552876e-01 -2.24315092e-01 6.58502877e-01 -1.77541697e+00 3.31753224e-01 6.71929538e-01 1.36081326e+00 2.66592950e-02 2.51366109e-01 -2.43796855e-02 2.61392802e-01 -4.03303862e-01 -6.43459737e-01 4.97436315e-01 -5.62873244e-01 -3.97530884e-01 3.40215296e-01 1.05461562e+00 -4.04727459e-01 -3.47211540e-01 1.05812478e+00 6.30190194e-01 -3.05169951e-02 3.45127761e-01 -1.61434865e+00 -1.00542450e+00 3.96868475e-02 -6.70720160e-01 -3.62580925e-01 3.62155616e-01 5.29906690e-01 6.32521093e-01 -1.09445095e+00 4.51737016e-01 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af252c55-8af4-4a3d-a0ba-e93b602b641c	untargeted-backdoor-attack-against-object	2211.05638	null	https://arxiv.org/abs/2211.05638v2	https://arxiv.org/pdf/2211.05638v2.pdf	Untargeted Backdoor Attack against Object Detection	Recent studies revealed that deep neural networks (DNNs) are exposed to backdoor threats when training with third-party resources (such as training samples or backbones). The backdoored model has promising performance in predicting benign samples, whereas its predictions can be maliciously manipulated by adversaries based on activating its backdoors with pre-defined trigger patterns. Currently, most of the existing backdoor attacks were conducted on the image classification under the targeted manner. In this paper, we reveal that these threats could also happen in object detection, posing threatening risks to many mission-critical applications ($e.g.$, pedestrian detection and intelligent surveillance systems). Specifically, we design a simple yet effective poison-only backdoor attack in an untargeted manner, based on task characteristics. We show that, once the backdoor is embedded into the target model by our attack, it can trick the model to lose detection of any object stamped with our trigger patterns. We conduct extensive experiments on the benchmark dataset, showing its effectiveness in both digital and physical-world settings and its resistance to potential defenses.	['Shu-Tao Xia', 'Yong Jiang', 'Yiming Li', 'Chengxiao Luo']	2022-11-02	null	null	null	null	['pedestrian-detection']	['computer-vision']	[ 3.96826535e-01 8.55433047e-02 -1.56919658e-01 1.53946353e-03 -3.55936915e-01 -1.41886210e+00 7.58141220e-01 -3.21243495e-01 -5.06456852e-01 4.51960802e-01 -6.25190318e-01 -7.79252052e-01 2.61398435e-01 -9.46128726e-01 -1.48443699e+00 -8.75426888e-01 -3.11898291e-01 -1.27116546e-01 5.65582693e-01 -2.99830548e-02 1.77645877e-01 7.40836442e-01 -9.96899843e-01 3.25675488e-01 3.61459970e-01 1.13549232e+00 -1.54876962e-01 5.09676933e-01 3.69229466e-01 7.28996873e-01 -1.09637034e+00 -8.97840977e-01 8.85250688e-01 2.65733182e-01 -4.16380554e-01 -5.22173762e-01 5.44885516e-01 -7.96087444e-01 -9.04231668e-01 1.57318008e+00 2.05142081e-01 -4.90368634e-01 2.82056957e-01 -1.68759155e+00 -5.24839938e-01 6.79969192e-01 -4.63898927e-01 1.98310882e-01 2.96944426e-03 6.88665450e-01 3.71134609e-01 -6.24384880e-01 3.11290979e-01 1.32055116e+00 5.98120689e-01 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-4.91746247e-01 -5.95429659e-01 9.43825170e-02 5.06263077e-01 -8.18615139e-01 -6.64326906e-01 -3.69003266e-01]	[5.704922199249268, 7.699390888214111]
2a9c88e6-41f8-4301-bf36-28947e31099b	learning-to-explore-using-active-neural-slam	2004.05155	null	https://arxiv.org/abs/2004.05155v1	https://arxiv.org/pdf/2004.05155v1.pdf	Learning to Explore using Active Neural SLAM	This work presents a modular and hierarchical approach to learn policies for exploring 3D environments, called `Active Neural SLAM'. Our approach leverages the strengths of both classical and learning-based methods, by using analytical path planners with learned SLAM module, and global and local policies. The use of learning provides flexibility with respect to input modalities (in the SLAM module), leverages structural regularities of the world (in global policies), and provides robustness to errors in state estimation (in local policies). Such use of learning within each module retains its benefits, while at the same time, hierarchical decomposition and modular training allow us to sidestep the high sample complexities associated with training end-to-end policies. Our experiments in visually and physically realistic simulated 3D environments demonstrate the effectiveness of our approach over past learning and geometry-based approaches. The proposed model can also be easily transferred to the PointGoal task and was the winning entry of the CVPR 2019 Habitat PointGoal Navigation Challenge.	['Dhiraj Gandhi', 'Ruslan Salakhutdinov', 'Devendra Singh Chaplot', 'Abhinav Gupta', 'Saurabh Gupta']	2020-04-10	null	https://openreview.net/forum?id=HklXn1BKDH	https://openreview.net/pdf?id=HklXn1BKDH	iclr-2020-1	['pointgoal-navigation']	['robots']	[-1.46068826e-01 4.29696947e-01 -2.53951102e-01 -2.36336872e-01 -6.82505250e-01 -6.29575014e-01 6.29052758e-01 3.82152468e-01 -7.09273934e-01 9.91639912e-01 3.42939973e-01 -2.84882694e-01 -4.90482390e-01 -7.35744417e-01 -9.29879844e-01 -5.71837723e-01 -9.36607540e-01 6.78160608e-01 4.26090032e-01 -5.74089170e-01 3.35198194e-01 7.26810932e-01 -1.47123909e+00 -3.02065998e-01 9.11261559e-01 8.29923153e-01 3.85462612e-01 6.05120242e-01 -5.88213243e-02 9.81057346e-01 -2.08857236e-03 3.89788002e-01 5.79577208e-01 1.82180837e-01 -7.16766357e-01 -7.26252794e-02 2.96159148e-01 -3.45138371e-01 -3.40800822e-01 6.78718388e-01 4.32074398e-01 6.51941240e-01 3.82291317e-01 -1.12049174e+00 1.08697362e-01 2.15333909e-01 -2.08476886e-01 -1.51127517e-01 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5be21605-4ff5-4311-a0c5-dd7c8a02258f	counterfactual-debiasing-for-generating	2305.10736	null	https://arxiv.org/abs/2305.10736v1	https://arxiv.org/pdf/2305.10736v1.pdf	Counterfactual Debiasing for Generating Factually Consistent Text Summaries	Despite substantial progress in abstractive text summarization to generate fluent and informative texts, the factual inconsistency in the generated summaries remains an important yet challenging problem to be solved. In this paper, we construct causal graphs for abstractive text summarization and identify the intrinsic causes of the factual inconsistency, i.e., the language bias and irrelevancy bias, and further propose a debiasing framework, named CoFactSum, to alleviate the causal effects of these biases by counterfactual estimation. Specifically, the proposed CoFactSum provides two counterfactual estimation strategies, i.e., Explicit Counterfactual Masking with an explicit dynamic masking strategy, and Implicit Counterfactual Training with an implicit discriminative cross-attention mechanism. Meanwhile, we design a Debiasing Degree Adjustment mechanism to dynamically adapt the debiasing degree at each decoding step. Extensive experiments on two widely-used summarization datasets demonstrate the effectiveness of CoFactSum in enhancing the factual consistency of generated summaries compared with several baselines.	['Ying Shen', 'Yaliang Li', 'Yuexiang Xie', 'Chenhe Dong']	2023-05-18	null	null	null	null	['abstractive-text-summarization', 'text-summarization']	['natural-language-processing', 'natural-language-processing']	[ 4.84989047e-01 4.42020476e-01 -6.44485116e-01 -3.65990490e-01 -9.78274643e-01 -5.69124222e-01 9.04123783e-01 1.97814465e-01 -4.52450588e-02 1.36559260e+00 1.26833594e+00 -3.76342118e-01 -3.80753987e-02 -5.19867182e-01 -8.09230506e-01 -4.20074612e-01 2.66795039e-01 2.87127942e-01 -2.14809090e-01 -1.24784820e-01 7.01382041e-01 -1.66501045e-01 -1.03853047e+00 2.77526021e-01 1.75686538e+00 4.64559793e-01 5.71883582e-02 6.69286609e-01 -8.98244306e-02 1.26255560e+00 -1.18387926e+00 -6.82452381e-01 -2.02948660e-01 -7.86193848e-01 -7.21051931e-01 -8.86029527e-02 5.79117835e-01 -4.57943469e-01 -3.94591391e-01 1.00880206e+00 7.60845244e-01 1.70570046e-01 8.84391606e-01 -1.18469357e+00 -8.20437968e-01 1.22027349e+00 -8.86122286e-01 5.53070664e-01 5.40877461e-01 2.49839813e-01 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d80d381d-e95b-4f8a-a35f-1656f6034f65	false-positive-detection-and-prediction	2110.15681	null	https://arxiv.org/abs/2110.15681v1	https://arxiv.org/pdf/2110.15681v1.pdf	False Positive Detection and Prediction Quality Estimation for LiDAR Point Cloud Segmentation	We present a novel post-processing tool for semantic segmentation of LiDAR point cloud data, called LidarMetaSeg, which estimates the prediction quality segmentwise. For this purpose we compute dispersion measures based on network probability outputs as well as feature measures based on point cloud input features and aggregate them on segment level. These aggregated measures are used to train a meta classification model to predict whether a predicted segment is a false positive or not and a meta regression model to predict the segmentwise intersection over union. Both models can then be applied to semantic segmentation inferences without knowing the ground truth. In our experiments we use different LiDAR segmentation models and datasets and analyze the power of our method. We show that our results outperform other standard approaches.	['Hanno Gottschalk', 'Lutz Roese-Koerner', 'Matthias Rottmann', 'Pascal Colling']	2021-10-29	null	null	null	null	['point-cloud-segmentation']	['computer-vision']	[ 5.25367558e-01 4.94847119e-01 -3.02636713e-01 -6.86838567e-01 -8.48150015e-01 -5.80727220e-01 5.22623301e-01 7.40068972e-01 -2.78588444e-01 7.86107004e-01 -3.58096510e-01 -5.26328862e-01 -5.12761176e-01 -1.57291293e+00 -8.65387082e-01 -2.38094047e-01 -2.13714257e-01 1.12220931e+00 7.84203947e-01 3.53476375e-01 5.53313971e-01 7.07098067e-01 -1.86282647e+00 3.66247147e-01 1.15517223e+00 1.15258765e+00 -1.85323313e-01 6.62465394e-01 -4.37904686e-01 1.39919162e-01 -4.54837680e-01 -2.90089458e-01 3.12396884e-01 1.20054297e-01 -1.01017666e+00 -2.99322367e-01 6.28719151e-01 1.66408256e-01 4.59532320e-01 1.13629448e+00 4.55164537e-02 -8.53349790e-02 1.23454905e+00 -1.39601636e+00 3.15047115e-01 9.07150149e-01 -3.83777469e-01 2.35825688e-01 1.69578969e-01 2.04961877e-02 1.26421189e+00 -6.88307166e-01 5.63986540e-01 1.26461065e+00 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c54f8358-9cd5-45a5-8ce9-18b3b6ff1b26	learning-regional-attraction-for-line-segment	1912.09344	null	https://arxiv.org/abs/1912.09344v1	https://arxiv.org/pdf/1912.09344v1.pdf	Learning Regional Attraction for Line Segment Detection	This paper presents regional attraction of line segment maps, and hereby poses the problem of line segment detection (LSD) as a problem of region coloring. Given a line segment map, the proposed regional attraction first establishes the relationship between line segments and regions in the image lattice. Based on this, the line segment map is equivalently transformed to an attraction field map (AFM), which can be remapped to a set of line segments without loss of information. Accordingly, we develop an end-to-end framework to learn attraction field maps for raw input images, followed by a squeeze module to detect line segments. Apart from existing works, the proposed detector properly handles the local ambiguity and does not rely on the accurate identification of edge pixels. Comprehensive experiments on the Wireframe dataset and the YorkUrban dataset demonstrate the superiority of our method. In particular, we achieve an F-measure of 0.831 on the Wireframe dataset, advancing the state-of-the-art performance by 10.3 percent.	['Gui-Song Xia', 'Fu-Dong Wang', 'Song Bai', 'Philip H. S. Torr', 'Liangpei Zhang', 'Tianfu Wu', 'Nan Xue']	2019-12-18	null	null	null	null	['line-segment-detection']	['computer-vision']	[ 2.92712629e-01 1.28158957e-01 -3.29803318e-01 -1.90586358e-01 -7.42005765e-01 -8.11283350e-01 3.36430073e-01 1.38054460e-01 -1.87528193e-01 4.26939279e-01 -3.38134229e-01 -3.91157418e-01 8.35632607e-02 -7.20142245e-01 -9.96657491e-01 -3.71136904e-01 -1.49742767e-01 1.01850219e-01 5.08156240e-01 -1.44599125e-01 4.74694550e-01 6.36603534e-01 -1.08150482e+00 8.52003768e-02 1.04260790e+00 1.12568045e+00 -1.03121378e-01 3.80004883e-01 -5.83426394e-02 2.84341365e-01 -2.52090007e-01 -1.30607083e-01 3.98148030e-01 -1.27123132e-01 -7.01250851e-01 5.38227499e-01 6.89202249e-01 -3.59791249e-01 -2.85287708e-01 1.01385796e+00 1.54717013e-01 -4.79155555e-02 5.32217264e-01 -1.30231941e+00 -3.50331426e-01 4.13277924e-01 -1.34129572e+00 1.76097557e-01 3.98889929e-01 -2.54258811e-01 1.11824143e+00 -1.35711563e+00 6.81123734e-01 9.01191950e-01 5.10228276e-01 -9.14999023e-02 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925c6cf1-4a31-45c9-969d-fda47decddf5	cross-resolution-flow-propagation-for	2212.13525	null	https://arxiv.org/abs/2212.13525v1	https://arxiv.org/pdf/2212.13525v1.pdf	Cross-Resolution Flow Propagation for Foveated Video Super-Resolution	The demand of high-resolution video contents has grown over the years. However, the delivery of high-resolution video is constrained by either computational resources required for rendering or network bandwidth for remote transmission. To remedy this limitation, we leverage the eye trackers found alongside existing augmented and virtual reality headsets. We propose the application of video super-resolution (VSR) technique to fuse low-resolution context with regional high-resolution context for resource-constrained consumption of high-resolution content without perceivable drop in quality. Eye trackers provide us the gaze direction of a user, aiding us in the extraction of the regional high-resolution context. As only pixels that falls within the gaze region can be resolved by the human eye, a large amount of the delivered content is redundant as we can't perceive the difference in quality of the region beyond the observed region. To generate a visually pleasing frame from the fusion of high-resolution region and low-resolution region, we study the capability of a deep neural network of transferring the context of the observed region to other regions (low-resolution) of the current and future frames. We label this task a Foveated Video Super-Resolution (FVSR), as we need to super-resolve the low-resolution regions of current and future frames through the fusion of pixels from the gaze region. We propose Cross-Resolution Flow Propagation (CRFP) for FVSR. We train and evaluate CRFP on REDS dataset on the task of 8x FVSR, i.e. a combination of 8x VSR and the fusion of foveated region. Departing from the conventional evaluation of per frame quality using SSIM or PSNR, we propose the evaluation of past foveated region, measuring the capability of a model to leverage the noise present in eye trackers during FVSR. Code is made available at https://github.com/eugenelet/CRFP.	['Chen-Yi Lee', 'Evan Chen', 'Lien-Feng Hsu', 'Eugene Lee']	2022-12-27	null	null	null	null	['video-super-resolution']	['computer-vision']	[ 3.74918580e-01 -1.72822386e-01 6.36779740e-02 -1.65548027e-01 -7.87467480e-01 -4.45029497e-01 1.86082289e-01 -4.38678443e-01 -4.44723785e-01 8.75136912e-01 2.92570889e-01 -1.35166377e-01 2.23730728e-02 -7.57720888e-01 -8.94122362e-01 -5.22321820e-01 3.98504967e-03 -6.30135357e-01 5.39687574e-01 -2.69821644e-01 1.97372451e-01 5.98773479e-01 -2.00096846e+00 5.91523647e-01 6.75621748e-01 1.14231730e+00 4.69820321e-01 1.03717446e+00 2.21782133e-01 7.42656171e-01 -5.95539987e-01 5.29700220e-02 4.90963072e-01 -3.77463371e-01 -4.50477362e-01 -4.06732261e-02 9.69525576e-01 -9.95452940e-01 -2.93429315e-01 9.33986843e-01 4.39873457e-01 1.80366337e-01 8.99815187e-02 -1.09867668e+00 -4.54992205e-01 1.32083401e-01 -9.43540752e-01 8.96265090e-01 8.52986634e-01 2.81059027e-01 6.21652961e-01 -9.96590436e-01 9.45121050e-01 1.14101470e+00 1.75562680e-01 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e2b90c86-977c-4a78-b766-de5f3d2096d4	sampling-and-ranking-for-digital-ink	2306.03103	null	https://arxiv.org/abs/2306.03103v1	https://arxiv.org/pdf/2306.03103v1.pdf	Sampling and Ranking for Digital Ink Generation on a tight computational budget	Digital ink (online handwriting) generation has a number of potential applications for creating user-visible content, such as handwriting autocompletion, spelling correction, and beautification. Writing is personal and usually the processing is done on-device. Ink generative models thus need to produce high quality content quickly, in a resource constrained environment. In this work, we study ways to maximize the quality of the output of a trained digital ink generative model, while staying within an inference time budget. We use and compare the effect of multiple sampling and ranking techniques, in the first ablation study of its kind in the digital ink domain. We confirm our findings on multiple datasets - writing in English and Vietnamese, as well as mathematical formulas - using two model types and two common ink data representations. In all combinations, we report a meaningful improvement in the recognizability of the synthetic inks, in some cases more than halving the character error rate metric, and describe a way to select the optimal combination of sampling and ranking techniques for any given computational budget.	['Claudiu Musat', 'Aleksandr Timofeev', 'Andrii Maksai', 'Andrei Afonin']	2023-06-02	null	null	null	null	['handwriting-generation', 'spelling-correction']	['computer-vision', 'natural-language-processing']	[ 5.59405029e-01 -6.43050298e-02 1.16660461e-01 -2.17806194e-02 -6.18473768e-01 -1.06457806e+00 7.15863287e-01 -1.81110054e-02 -2.72557229e-01 6.79264903e-01 8.03852603e-02 -3.69329274e-01 -1.46400645e-01 -7.80847549e-01 -6.86519206e-01 -4.11610216e-01 5.54185510e-01 7.06900597e-01 8.19006339e-02 8.50603916e-03 8.10927629e-01 6.73397660e-01 -1.51250648e+00 2.20936298e-01 1.11306620e+00 4.39468741e-01 3.53732765e-01 1.34361041e+00 -5.61792850e-01 6.83602154e-01 -9.65073109e-01 -7.66939700e-01 4.25455600e-01 -6.91847444e-01 -3.74296188e-01 1.30375654e-01 7.94681311e-01 -4.08960342e-01 -8.26284140e-02 9.35264349e-01 7.03497469e-01 -1.06073387e-01 9.20491517e-01 -8.20115328e-01 -1.13798666e+00 5.80235600e-01 -3.23072106e-01 -3.09837013e-01 4.63341355e-01 1.71037257e-01 5.99770546e-01 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236f4b13-d32d-41b5-a738-97769cba66b8	analyzing-elmo-and-distilbert-on-socio	null	null	https://aclanthology.org/2020.aespen-1.4	https://aclanthology.org/2020.aespen-1.4.pdf	Analyzing ELMo and DistilBERT on Socio-political News Classification	This study evaluates the robustness of two state-of-the-art deep contextual language representations, ELMo and DistilBERT, on supervised learning of binary protest news classification (PC) and sentiment analysis (SA) of product reviews. A {''}cross-context{''} setting is enabled using test sets that are distinct from the training data. The models are fine-tuned and fed into a Feed-Forward Neural Network (FFNN) and a Bidirectional Long Short Term Memory network (BiLSTM). Multinomial Naive Bayes (MNB) and Linear Support Vector Machine (LSVM) are used as traditional baselines. The results suggest that DistilBERT can transfer generic semantic knowledge to other domains better than ELMo. DistilBERT is also 30{\%} smaller and 83{\%} faster than ELMo, which suggests superiority for smaller computational training budgets. When generalization is not the utmost preference and test domain is similar to the training domain, the traditional machine learning (ML) algorithms can still be considered as more economic alternatives to deep language representations.	['Arzucan {\\"O}zg{\\"u}r', 'Ali H{\\"u}rriyeto{\\u{g}}lu', 'Berfu B{\\"u}y{\\"u}k{\\"o}z']	2020-05-01	null	null	null	lrec-2020-5	['news-classification']	['natural-language-processing']	[ 4.33472805e-02 2.16270581e-01 -7.91649163e-01 -6.87974393e-01 -6.64184391e-01 -5.59815466e-01 9.06782806e-01 4.78002876e-01 -7.04999626e-01 7.18808353e-01 2.88274318e-01 -8.44556212e-01 1.23735823e-01 -8.21663618e-01 -8.58837545e-01 -3.48387033e-01 1.43993780e-01 5.85460722e-01 7.97669515e-02 -6.41613781e-01 2.15616703e-01 2.94153571e-01 -1.19627702e+00 9.36109364e-01 4.35692579e-01 1.29263854e+00 7.51688555e-02 5.89582264e-01 -3.11103553e-01 1.43835890e+00 -4.65591073e-01 -8.88445199e-01 -7.80493319e-02 -1.41750649e-01 -1.01375139e+00 -3.97110522e-01 2.86321104e-01 1.65548839e-03 1.28868848e-01 9.60271180e-01 2.55776674e-01 2.38903135e-01 7.45474041e-01 -8.63190532e-01 -1.23179793e+00 9.15116310e-01 -5.42117417e-01 5.51319957e-01 4.37680185e-02 -2.71448255e-01 1.29863226e+00 -6.69304430e-01 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3.90285522e-01 -4.98679250e-01 -7.66999245e-01 -1.14139986e+00 -4.77362901e-01 5.32059968e-01 2.75772542e-01 -6.20107412e-01 -4.83106136e-01 1.36909401e-02]	[10.948975563049316, 7.354745864868164]
24a00b3a-464c-4fe8-ab1b-128885aacc25	dippas-a-deep-image-prior-prnu-anonymization	2012.03581	null	https://arxiv.org/abs/2012.03581v2	https://arxiv.org/pdf/2012.03581v2.pdf	DIPPAS: A Deep Image Prior PRNU Anonymization Scheme	Source device identification is an important topic in image forensics since it allows to trace back the origin of an image. Its forensics counter-part is source device anonymization, that is, to mask any trace on the image that can be useful for identifying the source device. A typical trace exploited for source device identification is the Photo Response Non-Uniformity (PRNU), a noise pattern left by the device on the acquired images. In this paper, we devise a methodology for suppressing such a trace from natural images without significant impact on image quality. Specifically, we turn PRNU anonymization into an optimization problem in a Deep Image Prior (DIP) framework. In a nutshell, a Convolutional Neural Network (CNN) acts as generator and returns an image that is anonymized with respect to the source PRNU, still maintaining high visual quality. With respect to widely-adopted deep learning paradigms, our proposed CNN is not trained on a set of input-target pairs of images. Instead, it is optimized to reconstruct the PRNU-free image from the original image under analysis itself. This makes the approach particularly suitable in scenarios where large heterogeneous databases are analyzed and prevents any problem due to lack of generalization. Through numerical examples on publicly available datasets, we prove our methodology to be effective compared to state-of-the-art techniques.	['Stefano Tubaro', 'Vincenzo Lipari', 'Paolo Bestagini', 'Sara Mandelli', 'Francesco Picetti']	2020-12-07	null	null	null	null	['image-forensics']	['computer-vision']	[ 6.36131823e-01 -3.71242575e-02 -5.76441325e-02 7.46598840e-02 -8.87672603e-01 -9.93999004e-01 4.19575512e-01 1.95998520e-01 -3.31022084e-01 6.65192187e-01 -1.43965945e-01 -3.77127975e-01 1.39784798e-01 -7.63587356e-01 -1.20569658e+00 -7.94309974e-01 3.01669657e-01 -1.03579186e-01 -2.32936636e-01 2.76847094e-01 1.06189899e-01 8.98745596e-01 -1.19548011e+00 2.10775927e-01 6.22705638e-01 1.06365371e+00 -1.32187381e-01 3.71252775e-01 2.82246977e-01 8.55218470e-01 -9.34170008e-01 -8.86199057e-01 7.27871478e-01 -4.79952097e-01 -5.44829071e-01 2.76768655e-01 3.85947436e-01 -5.53178251e-01 -5.84654331e-01 1.41286635e+00 3.19719821e-01 -3.05642486e-01 6.07870579e-01 -1.35486972e+00 -7.62095034e-01 3.65467161e-01 -7.66784370e-01 2.51924038e-01 1.44113779e-01 3.66882205e-01 5.88611543e-01 -3.15372169e-01 8.43194783e-01 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9f74e2e7-a51d-4b5c-a80c-9483e12fe840	ml-powered-kqi-estimation-for-xr-services-a	2212.12002	null	https://arxiv.org/abs/2212.12002v1	https://arxiv.org/pdf/2212.12002v1.pdf	ML-powered KQI estimation for XR services. A case study on 360-Video	The arise of cutting-edge technologies and services such as XR promise to change the concepts of how day-to-day things are done. At the same time, the appearance of modern and decentralized architectures approaches has given birth to a new generation of mobile networks such as 5G, as well as outlining the roadmap for B5G and posterior. These networks are expected to be the enablers for bringing to life the Metaverse and other futuristic approaches. In this sense, this work presents an ML-based (Machine Learning) framework that allows the estimation of service Key Quality Indicators (KQIs). For this, only information reachable to operators is required, such as statistics and configuration parameters from these networks. This strategy prevents operators from avoiding intrusion into the user data and guaranteeing privacy. To test this proposal, 360-Video has been selected as a use case of Virtual Reality (VR), from which specific KQIs are estimated such as video resolution, frame rate, initial startup time, throughput, and latency, among others. To select the best model for each KQI, a search grid with a cross-validation strategy has been used to determine the best hyperparameter tuning. To boost the creation of each KQI model, feature engineering techniques together with cross-validation strategies have been used. The performance is assessed using MAE (Mean Average Error) and the prediction time. The outcomes point out that KNR (K-Near Neighbors) and RF (Random Forest) are the best algorithms in combination with Feature Selection techniques. Likewise, this work will help as a baseline for E2E-Quality-of-Experience-based network management working in conjunction with network slicing, virtualization, and MEC, among other enabler technologies.	['Raquel Barco', 'Sergio Fortes', 'Carlos Baena', 'O. S. Peñaherrera-Pulla']	2022-12-08	null	null	null	null	['feature-engineering']	['methodology']	[-2.87011594e-01 4.68307827e-03 -3.92491281e-01 -2.91186094e-01 -2.79214203e-01 -4.84493136e-01 4.09602016e-01 -3.28803733e-02 -8.78040642e-02 8.63732994e-01 -1.78433448e-01 -7.30650663e-01 -7.82337546e-01 -8.15018833e-01 -1.52336791e-01 -6.37183607e-01 -6.08063698e-01 3.78140539e-01 -4.84372042e-02 -2.27497399e-01 3.08633655e-01 8.34128737e-01 -1.43181467e+00 5.22203222e-02 5.32534540e-01 1.44608963e+00 2.51022242e-02 5.01138389e-01 -3.25110629e-02 4.98222709e-01 -6.49479747e-01 -4.19085115e-01 4.29932207e-01 -8.97689238e-02 -5.49126089e-01 -2.35557124e-01 -3.38326424e-01 -2.48889878e-01 -2.29391202e-01 5.14534771e-01 5.37323594e-01 -1.32960111e-01 2.73321480e-01 -1.53213120e+00 5.86958155e-02 5.75511277e-01 -8.01926330e-02 2.41372332e-01 2.55143613e-01 7.49431700e-02 8.91083956e-01 -3.16642195e-01 6.96966887e-01 8.25746715e-01 3.25900674e-01 2.62243617e-02 -1.11503291e+00 -5.54633379e-01 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f02c949b-49bb-43e8-8e60-4a9663fed1a1	generating-quantified-referring-expressions-1	null	null	https://aclanthology.org/2020.inlg-1.16	https://aclanthology.org/2020.inlg-1.16.pdf	Generating Quantified Referring Expressions through Attention-Driven Incremental Perception	We model the production of quantified referring expressions (QREs) that identity collections of visual items. A previous approach, called Perceptual Cost Pruning, modeled human QRE production using a preference-based referring expression generation algorithm, first removing facts from the input knowledge base based on a model of perceptual cost. In this paper, we present an alternative model that incrementally constructs a symbolic knowledge base through simulating human visual attention/perception from raw images. We demonstrate that this model produces the same output as Perceptual Cost Pruning. We argue that this is a more extensible approach and a step toward developing a wider range of process-level models of human visual description.	['Gordon Briggs']	null	null	null	null	inlg-acl-2020-12	['referring-expression-generation']	['computer-vision']	[ 1.58187702e-01 7.70184159e-01 -1.88616663e-01 -2.53671199e-01 -5.54083526e-01 -6.89203918e-01 5.71190417e-01 2.99495310e-01 -1.62310049e-01 7.04419017e-01 3.60421628e-01 -2.49157831e-01 -2.74935007e-01 -9.68617141e-01 -5.57195902e-01 4.93326932e-02 1.77416623e-01 3.71395797e-01 4.36416179e-01 -3.79908383e-01 4.89266425e-01 8.51033330e-01 -1.69232094e+00 4.70597178e-01 4.34499353e-01 7.41500139e-01 1.10256039e-01 8.47701073e-01 -3.61915678e-01 1.47630870e+00 -6.29901230e-01 -1.00834811e+00 -6.90129250e-02 -4.75751907e-01 -1.14158022e+00 8.88421014e-02 2.45509490e-01 -6.61031306e-02 1.62137747e-02 1.09903347e+00 1.22962981e-01 3.39347363e-01 4.76445347e-01 -1.38413453e+00 -1.41380203e+00 7.79495358e-01 1.06338143e-01 7.33412653e-02 8.78195405e-01 2.00570256e-01 9.56706464e-01 -7.55489886e-01 1.12581468e+00 1.59605420e+00 3.12916428e-01 8.24613869e-01 -1.49365425e+00 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6.52510345e-01 -4.54073623e-02 -9.23279285e-01 1.04969405e-01 5.50664723e-01]	[10.616073608398438, 1.9045510292053223]
586e84f0-182c-469a-9d54-9800523d04cd	conner-consistency-training-for-cross-lingual	2211.09394	null	https://arxiv.org/abs/2211.09394v1	https://arxiv.org/pdf/2211.09394v1.pdf	ConNER: Consistency Training for Cross-lingual Named Entity Recognition	Cross-lingual named entity recognition (NER) suffers from data scarcity in the target languages, especially under zero-shot settings. Existing translate-train or knowledge distillation methods attempt to bridge the language gap, but often introduce a high level of noise. To solve this problem, consistency training methods regularize the model to be robust towards perturbations on data or hidden states. However, such methods are likely to violate the consistency hypothesis, or mainly focus on coarse-grain consistency. We propose ConNER as a novel consistency training framework for cross-lingual NER, which comprises of: (1) translation-based consistency training on unlabeled target-language data, and (2) dropoutbased consistency training on labeled source-language data. ConNER effectively leverages unlabeled target-language data and alleviates overfitting on the source language to enhance the cross-lingual adaptability. Experimental results show our ConNER achieves consistent improvement over various baseline methods.	['Chunyan Miao', 'Luo Si', 'Erik Cambria', 'Lidong Bing', 'Xin Li', 'Ran Zhou']	2022-11-17	null	null	null	null	['cross-lingual-ner']	['natural-language-processing']	[-3.44255537e-01 -8.92476216e-02 -7.32921660e-01 -6.83169961e-01 -1.48159945e+00 -5.51372766e-01 3.88087124e-01 -2.29983062e-01 -5.64888537e-01 8.65631759e-01 3.52684379e-01 -4.31749940e-01 4.21507597e-01 -4.23786551e-01 -8.39392722e-01 -3.31339091e-01 5.23456931e-01 6.16693079e-01 -3.97367068e-02 1.28999809e-02 -3.77917558e-01 -1.99653059e-01 -5.78174233e-01 6.17078803e-02 1.28330302e+00 4.98041838e-01 1.25539005e-01 3.02848574e-02 -5.57775557e-01 8.50605428e-01 -3.29339504e-01 -7.78291941e-01 7.98930898e-02 -5.64948976e-01 -9.37856555e-01 -9.13423747e-02 2.69577146e-01 -6.38067797e-02 -1.88330621e-01 1.43322527e+00 4.81439084e-01 1.64554179e-01 1.11067034e-01 -1.23902667e+00 -1.14881587e+00 1.06203043e+00 -2.59257019e-01 -2.85336338e-02 -2.28881910e-01 1.53341800e-01 1.03637040e+00 -1.18815064e+00 7.29550838e-01 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a7dd0a72-e8cd-4b3a-9766-80aa1811fae3	deep-convolutional-neural-networks-for-smile	1508.06535	null	http://arxiv.org/abs/1508.06535v1	http://arxiv.org/pdf/1508.06535v1.pdf	Deep Convolutional Neural Networks for Smile Recognition	This thesis describes the design and implementation of a smile detector based on deep convolutional neural networks. It starts with a summary of neural networks, the difficulties of training them and new training methods, such as Restricted Boltzmann Machines or autoencoders. It then provides a literature review of convolutional neural networks and recurrent neural networks. In order to select databases for smile recognition, comprehensive statistics of databases popular in the field of facial expression recognition were generated and are summarized in this thesis. It then proposes a model for smile detection, of which the main part is implemented. The experimental results are discussed in this thesis and justified based on a comprehensive model selection performed. All experiments were run on a Tesla K40c GPU benefiting from a speedup of up to factor 10 over the computations on a CPU. A smile detection test accuracy of 99.45% is achieved for the Denver Intensity of Spontaneous Facial Action (DISFA) database, significantly outperforming existing approaches with accuracies ranging from 65.55% to 79.67%. This experiment is re-run under various variations, such as retaining less neutral images or only the low or high intensities, of which the results are extensively compared.	['Patrick O. Glauner']	2015-08-26	null	null	null	null	['smile-recognition']	['computer-vision']	[ 1.38935789e-01 -1.34660199e-03 -1.56226724e-01 -6.60016477e-01 -2.21473604e-01 1.53452143e-01 4.02081877e-01 -6.14782453e-01 -5.61662316e-01 7.43401945e-01 6.51350096e-02 -5.17719537e-02 1.62464038e-01 -4.08839852e-01 -2.65827596e-01 -1.08132350e+00 -2.06594333e-01 3.05636734e-01 -5.28199315e-01 -2.04753458e-01 2.20686629e-01 9.86348867e-01 -1.95552135e+00 5.13672769e-01 1.61024123e-01 1.32022727e+00 -5.31715274e-01 8.00289392e-01 -3.46232168e-02 1.01071703e+00 -7.38722086e-01 -4.20451492e-01 -2.66236693e-01 -4.45325851e-01 -6.06086373e-01 -8.65672901e-02 4.94928390e-01 -4.76791382e-01 -3.81899536e-01 7.03686774e-01 1.14289367e+00 1.97737530e-01 6.68244302e-01 -1.21303236e+00 1.55727868e-03 3.56606441e-03 -3.79649222e-01 2.03904808e-01 9.78447050e-02 -3.06465834e-01 3.35598290e-01 -1.05417514e+00 4.81083423e-01 1.12254941e+00 6.54794395e-01 1.03872740e+00 -7.97462642e-01 -9.53331292e-01 -6.26786411e-01 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1.52711049e-01 -8.75482023e-01 -5.11863947e-01 -4.10078675e-01]	[13.527400970458984, 1.7433944940567017]
1243101b-2d2c-4130-937f-d5b515977e33	the-reprgesture-entry-to-the-genea-challenge	2208.12133	null	https://arxiv.org/abs/2208.12133v1	https://arxiv.org/pdf/2208.12133v1.pdf	The ReprGesture entry to the GENEA Challenge 2022	This paper describes the ReprGesture entry to the Generation and Evaluation of Non-verbal Behaviour for Embodied Agents (GENEA) challenge 2022. The GENEA challenge provides the processed datasets and performs crowdsourced evaluations to compare the performance of different gesture generation systems. In this paper, we explore an automatic gesture generation system based on multimodal representation learning. We use WavLM features for audio, FastText features for text and position and rotation matrix features for gesture. Each modality is projected to two distinct subspaces: modality-invariant and modality-specific. To learn inter-modality-invariant commonalities and capture the characters of modality-specific representations, gradient reversal layer based adversarial classifier and modality reconstruction decoders are used during training. The gesture decoder generates proper gestures using all representations and features related to the rhythm in the audio. Our code, pre-trained models and demo are available at https://github.com/YoungSeng/ReprGesture.	['Weihong Bao', 'Liyang Chen', 'Jiuxin Lin', 'Mengchen Zhao', 'Minglei Li', 'Zhiyong Wu', 'Sicheng Yang']	2022-08-25	null	null	null	null	['gesture-generation']	['robots']	[ 2.85748690e-01 2.52515096e-02 1.51960820e-01 -2.66624540e-01 -1.05451274e+00 -9.19114709e-01 1.19950640e+00 -5.47392428e-01 -3.96124482e-01 6.70411110e-01 1.05956078e+00 9.96349677e-02 2.17561737e-01 -4.41388190e-01 -7.54304528e-01 -7.20003247e-01 -1.45463049e-01 4.46640015e-01 -3.68729711e-01 -5.49096644e-01 -1.04912244e-01 3.38051617e-01 -1.33004391e+00 7.82823026e-01 1.58289850e-01 5.77153504e-01 -2.98902035e-01 1.45951581e+00 4.67950366e-02 7.39392281e-01 -6.47465169e-01 -1.97766781e-01 2.96260327e-01 -7.43432343e-01 -8.83437037e-01 -2.99874783e-01 1.34532779e-01 -6.73795938e-01 -9.47467923e-01 4.01975960e-01 1.04214919e+00 5.07878959e-01 1.17829072e+00 -1.76348400e+00 -5.71406364e-01 7.39564836e-01 2.06127428e-02 -2.79353201e-01 1.20961571e+00 5.79557300e-01 6.59039915e-01 -8.83345187e-01 9.16049659e-01 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83c1508f-ad2d-476c-971c-cf5e93096439	deepfood-deep-learning-based-food-image	1606.05675	null	http://arxiv.org/abs/1606.05675v1	http://arxiv.org/pdf/1606.05675v1.pdf	DeepFood: Deep Learning-Based Food Image Recognition for Computer-Aided Dietary Assessment	Worldwide, in 2014, more than 1.9 billion adults, 18 years and older, were overweight. Of these, over 600 million were obese. Accurately documenting dietary caloric intake is crucial to manage weight loss, but also presents challenges because most of the current methods for dietary assessment must rely on memory to recall foods eaten. The ultimate goal of our research is to develop computer-aided technical solutions to enhance and improve the accuracy of current measurements of dietary intake. Our proposed system in this paper aims to improve the accuracy of dietary assessment by analyzing the food images captured by mobile devices (e.g., smartphone). The key technique innovation in this paper is the deep learning-based food image recognition algorithms. Substantial research has demonstrated that digital imaging accurately estimates dietary intake in many environments and it has many advantages over other methods. However, how to derive the food information (e.g., food type and portion size) from food image effectively and efficiently remains a challenging and open research problem. We propose a new Convolutional Neural Network (CNN)-based food image recognition algorithm to address this problem. We applied our proposed approach to two real-world food image data sets (UEC-256 and Food-101) and achieved impressive results. To the best of our knowledge, these results outperformed all other reported work using these two data sets. Our experiments have demonstrated that the proposed approach is a promising solution for addressing the food image recognition problem. Our future work includes further improving the performance of the algorithms and integrating our system into a real-world mobile and cloud computing-based system to enhance the accuracy of current measurements of dietary intake.	['Vinod Vokkarane', 'Yu Cao', 'Guanling Chen', 'Chang Liu', 'Yan Luo', 'Yunsheng Ma']	2016-06-17	null	null	null	null	['fine-grained-image-recognition']	['computer-vision']	[ 1.92849219e-01 -6.27193689e-01 -6.49912715e-01 -4.84226227e-01 -2.57113606e-01 -3.39094102e-01 -2.00462550e-01 7.71795452e-01 -4.31850374e-01 1.15632974e-01 1.96709961e-01 -2.21672699e-01 2.36889392e-01 -1.35583270e+00 -7.76749492e-01 -3.46330404e-01 -3.95610303e-01 -2.55128115e-01 -2.01703772e-01 -3.90162915e-02 5.81388809e-02 -2.08052456e-01 -1.86326456e+00 2.84434229e-01 1.02176130e+00 1.29672301e+00 4.04900879e-01 7.82003999e-01 -2.93161869e-01 6.29915893e-01 -1.11855127e-01 6.41643861e-03 4.29577053e-01 -4.29173350e-01 -1.45598710e-01 3.12172528e-02 5.67085445e-01 -9.63801205e-01 -3.78698632e-02 1.39798105e+00 5.13618290e-01 -9.18915272e-02 3.08189303e-01 -7.25778759e-01 -1.26126194e+00 7.75270045e-01 -4.82405335e-01 2.48364136e-01 3.00012559e-01 1.18477652e-02 1.43772215e-01 -4.73642558e-01 -3.47249478e-01 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74ce6283-889a-4f21-919d-d3c8bfe71f5e	gcformer-an-efficient-framework-for-accurate	2306.08325	null	https://arxiv.org/abs/2306.08325v1	https://arxiv.org/pdf/2306.08325v1.pdf	GCformer: An Efficient Framework for Accurate and Scalable Long-Term Multivariate Time Series Forecasting	Transformer-based models have emerged as promising tools for time series forecasting. However, these model cannot make accurate prediction for long input time series. On the one hand, they failed to capture global dependencies within time series data. On the other hand, the long input sequence usually leads to large model size and high time complexity. To address these limitations, we present GCformer, which combines a structured global convolutional branch for processing long input sequences with a local Transformer-based branch for capturing short, recent signals. A cohesive framework for a global convolution kernel has been introduced, utilizing three distinct parameterization methods. The selected structured convolutional kernel in the global branch has been specifically crafted with sublinear complexity, thereby allowing for the efficient and effective processing of lengthy and noisy input signals. Empirical studies on six benchmark datasets demonstrate that GCformer outperforms state-of-the-art methods, reducing MSE error in multivariate time series benchmarks by 4.38% and model parameters by 61.92%. In particular, the global convolutional branch can serve as a plug-in block to enhance the performance of other models, with an average improvement of 31.93\%, including various recently published Transformer-based models. Our code is publicly available at https://github.com/zyj-111/GCformer.	['Yi Qian', 'Mengni Ye', 'Liang Sun', 'Tian Zhou', 'Ziqing Ma', 'Yanjun Zhao']	2023-06-14	null	null	null	null	['multivariate-time-series-forecasting']	['time-series']	[-2.45309956e-02 -6.75163507e-01 1.39049888e-01 -2.35693857e-01 -5.94310403e-01 -4.83372271e-01 2.21122980e-01 -9.34018344e-02 -2.94187337e-01 4.01488215e-01 -6.12814687e-02 -5.08887708e-01 -1.94278166e-01 -6.29706740e-01 -5.18319786e-01 -9.08503354e-01 -4.75501478e-01 -2.78463036e-01 2.90885847e-02 -2.43301615e-01 1.63188457e-01 3.80766571e-01 -1.27763617e+00 3.51587176e-01 8.00228775e-01 1.59603798e+00 2.68410653e-01 5.01922727e-01 5.07458523e-02 6.68088973e-01 -5.35919428e-01 -9.59067866e-02 3.81396115e-01 -2.86838591e-01 -7.11992308e-02 -3.07191610e-01 7.73212388e-02 -3.59299868e-01 -4.91787463e-01 7.29662955e-01 5.87590575e-01 8.97108391e-02 2.35410765e-01 -1.14297199e+00 -2.73132145e-01 6.90804720e-01 -4.33367550e-01 6.39325798e-01 -2.04628631e-01 1.82919189e-01 8.99252653e-01 -9.17415738e-01 5.58319651e-02 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a6ab6d62-191d-4831-a2e0-c8d347748e31	mmact-a-large-scale-dataset-for-cross-modal	null	null	http://openaccess.thecvf.com/content_ICCV_2019/html/Kong_MMAct_A_Large-Scale_Dataset_for_Cross_Modal_Human_Action_Understanding_ICCV_2019_paper.html	http://openaccess.thecvf.com/content_ICCV_2019/papers/Kong_MMAct_A_Large-Scale_Dataset_for_Cross_Modal_Human_Action_Understanding_ICCV_2019_paper.pdf	MMAct: A Large-Scale Dataset for Cross Modal Human Action Understanding	Unlike vision modalities, body-worn sensors or passive sensing can avoid the failure of action understanding in vision related challenges, e.g. occlusion and appearance variation. However, a standard large-scale dataset does not exist, in which different types of modalities across vision and sensors are integrated. To address the disadvantage of vision-based modalities and push towards multi/cross modal action understanding, this paper introduces a new large-scale dataset recorded from 20 distinct subjects with seven different types of modalities: RGB videos, keypoints, acceleration, gyroscope, orientation, Wi-Fi and pressure signal. The dataset consists of more than 36k video clips for 37 action classes covering a wide range of daily life activities such as desktop-related and check-in-based ones in four different distinct scenarios. On the basis of our dataset, we propose a novel multi modality distillation model with attention mechanism to realize an adaptive knowledge transfer from sensor-based modalities to vision-based modalities. The proposed model significantly improves performance of action recognition compared to models trained with only RGB information. The experimental results confirm the effectiveness of our model on cross-subject, -view, -scene and -session evaluation criteria. We believe that this new large-scale multimodal dataset will contribute the community of multimodal based action understanding.	[' Tomokazu Murakami', ' Bin Tong', ' Martin Klinkigt', ' Ziwei Deng', ' Ziming Wu', 'Quan Kong']	2019-10-01	null	null	null	iccv-2019-10	['action-understanding', 'multimodal-activity-recognition']	['computer-vision', 'computer-vision']	[ 4.08312857e-01 -2.47825176e-01 -1.92788601e-01 -5.28806567e-01 -7.28262246e-01 -2.59850323e-01 3.04770201e-01 -3.40693325e-01 -3.38935554e-01 5.98394215e-01 5.45597851e-01 3.04706573e-01 -2.23370418e-01 -2.65789568e-01 -5.51215053e-01 -5.49638748e-01 1.71024442e-01 -3.79324496e-01 2.57917374e-01 -7.19669759e-02 1.98802367e-01 2.00003564e-01 -1.78815687e+00 4.71558720e-01 6.49528384e-01 1.15095568e+00 4.01747487e-02 7.29844213e-01 3.77166152e-01 5.41735470e-01 -4.34068590e-01 1.29833808e-02 2.72250801e-01 -3.75830561e-01 -5.49539149e-01 3.30546588e-01 7.20572352e-01 -7.10999012e-01 -5.06565154e-01 6.75191998e-01 1.14912128e+00 1.04424402e-01 1.97756723e-01 -1.40734589e+00 -5.70987225e-01 -7.79722929e-02 -6.15352690e-01 2.52864629e-01 1.07597518e+00 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4373c0e8-4435-4790-974c-d5ad877e40ac	autorecon-automated-3d-object-discovery-and	2305.08810	null	https://arxiv.org/abs/2305.08810v1	https://arxiv.org/pdf/2305.08810v1.pdf	AutoRecon: Automated 3D Object Discovery and Reconstruction	A fully automated object reconstruction pipeline is crucial for digital content creation. While the area of 3D reconstruction has witnessed profound developments, the removal of background to obtain a clean object model still relies on different forms of manual labor, such as bounding box labeling, mask annotations, and mesh manipulations. In this paper, we propose a novel framework named AutoRecon for the automated discovery and reconstruction of an object from multi-view images. We demonstrate that foreground objects can be robustly located and segmented from SfM point clouds by leveraging self-supervised 2D vision transformer features. Then, we reconstruct decomposed neural scene representations with dense supervision provided by the decomposed point clouds, resulting in accurate object reconstruction and segmentation. Experiments on the DTU, BlendedMVS and CO3D-V2 datasets demonstrate the effectiveness and robustness of AutoRecon.	['Xiaowei Zhou', 'Hujun Bao', 'Haotong Lin', 'Sida Peng', 'Xingyi He', 'Yuang Wang']	2023-05-15	null	http://openaccess.thecvf.com//content/CVPR2023/html/Wang_AutoRecon_Automated_3D_Object_Discovery_and_Reconstruction_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Wang_AutoRecon_Automated_3D_Object_Discovery_and_Reconstruction_CVPR_2023_paper.pdf	cvpr-2023-1	['object-discovery', '3d-reconstruction', 'object-reconstruction']	['computer-vision', 'computer-vision', 'computer-vision']	[ 2.80555785e-01 -1.02839112e-01 1.50701791e-01 -3.39823067e-01 -6.27926886e-01 -7.66430557e-01 5.69373548e-01 -1.68621659e-01 1.10255346e-01 2.03170821e-01 -2.24749461e-01 5.04250042e-02 1.40810847e-01 -6.11616611e-01 -1.05425572e+00 -4.81495231e-01 4.89425242e-01 6.49716258e-01 6.15523458e-01 2.98975348e-01 -2.06786499e-04 9.78260398e-01 -1.75264728e+00 1.55460134e-01 6.73091114e-01 1.12623537e+00 6.15370512e-01 4.09207910e-01 -4.87619787e-01 4.26644087e-01 -5.94281293e-02 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aef62b4f-4a08-4cce-9bce-234cc5e9ff18	thor-wielding-hammers-to-integrate-language	2205.10893	null	https://arxiv.org/abs/2205.10893v1	https://arxiv.org/pdf/2205.10893v1.pdf	Thor: Wielding Hammers to Integrate Language Models and Automated Theorem Provers	In theorem proving, the task of selecting useful premises from a large library to unlock the proof of a given conjecture is crucially important. This presents a challenge for all theorem provers, especially the ones based on language models, due to their relative inability to reason over huge volumes of premises in text form. This paper introduces Thor, a framework integrating language models and automated theorem provers to overcome this difficulty. In Thor, a class of methods called hammers that leverage the power of automated theorem provers are used for premise selection, while all other tasks are designated to language models. Thor increases a language model's success rate on the PISA dataset from $39\%$ to $57\%$, while solving $8.2\%$ of problems neither language models nor automated theorem provers are able to solve on their own. Furthermore, with a significantly smaller computational budget, Thor can achieve a success rate on the MiniF2F dataset that is on par with the best existing methods. Thor can be instantiated for the majority of popular interactive theorem provers via a straightforward protocol we provide.	['Mateja Jamnik', 'Yuhuai Wu', 'Piotr Miłoś', 'Tomasz Odrzygóźdź', 'Konrad Czechowski', 'Szymon Tworkowski', 'Wenda Li', 'Albert Q. Jiang']	2022-05-22	null	null	null	null	['automated-theorem-proving', 'automated-theorem-proving']	['miscellaneous', 'reasoning']	[ 6.62125200e-02 4.50011790e-01 -2.18422100e-01 3.78382951e-02 -1.20041728e+00 -1.11296260e+00 5.25556266e-01 3.84165943e-01 -2.20282838e-01 1.16285920e+00 -6.55811489e-01 -1.44123554e+00 -3.73410374e-01 -1.13899136e+00 -9.97434616e-01 -1.08265668e-01 -2.06214949e-01 6.67951167e-01 3.67987096e-01 -2.07632393e-01 1.81502432e-01 7.08246306e-02 -1.57902622e+00 3.74019176e-01 7.70486891e-01 6.99529529e-01 -1.53463349e-01 7.89611399e-01 -1.50557995e-01 1.15726542e+00 -2.11717814e-01 -5.40688515e-01 2.78123617e-01 -3.03979039e-01 -1.37739098e+00 -7.80835330e-01 5.86558521e-01 -4.91750956e-01 -2.18640924e-01 1.11382759e+00 5.75054996e-02 -1.93588480e-01 2.56575018e-01 -1.79307866e+00 2.85050780e-01 1.06648552e+00 -2.09819257e-01 1.61888562e-02 7.63097703e-01 7.65364394e-02 1.55256033e+00 -4.77434695e-01 8.60754073e-01 1.26555920e+00 4.35238391e-01 5.25456131e-01 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81430fda-e93a-4f01-aa49-e5622bad2377	yolov6-v3-0-a-full-scale-reloading	2301.05586	null	https://arxiv.org/abs/2301.05586v1	https://arxiv.org/pdf/2301.05586v1.pdf	YOLOv6 v3.0: A Full-Scale Reloading	The YOLO community has been in high spirits since our first two releases! By the advent of Chinese New Year 2023, which sees the Year of the Rabbit, we refurnish YOLOv6 with numerous novel enhancements on the network architecture and the training scheme. This release is identified as YOLOv6 v3.0. For a glimpse of performance, our YOLOv6-N hits 37.5% AP on the COCO dataset at a throughput of 1187 FPS tested with an NVIDIA Tesla T4 GPU. YOLOv6-S strikes 45.0% AP at 484 FPS, outperforming other mainstream detectors at the same scale (YOLOv5-S, YOLOv8-S, YOLOX-S and PPYOLOE-S). Whereas, YOLOv6-M/L also achieve better accuracy performance (50.0%/52.8% respectively) than other detectors at a similar inference speed. Additionally, with an extended backbone and neck design, our YOLOv6-L6 achieves the state-of-the-art accuracy in real-time. Extensive experiments are carefully conducted to validate the effectiveness of each improving component. Our code is made available at https://github.com/meituan/YOLOv6.	['Xiangxiang Chu', 'Xiaoming Xu', 'Zaidan Ke', 'Bo Zhang', 'Meng Cheng', 'Hongliang Jiang', 'Yifei Geng', 'Lulu Li', 'Chuyi Li']	2023-01-13	null	null	null	null	['real-time-object-detection']	['computer-vision']	[-6.55349970e-01 -2.58804619e-01 -3.88044566e-01 -1.15802623e-01 -5.59153914e-01 -6.21558905e-01 2.79041201e-01 -1.04819618e-01 -6.96283996e-01 4.37703490e-01 -3.90025556e-01 -3.23378772e-01 4.76003319e-01 -6.90174222e-01 -8.19164932e-01 -3.98299634e-01 -3.73450145e-02 1.29004151e-01 6.70446098e-01 -1.59636855e-01 -3.14525925e-02 2.80404061e-01 -1.29580581e+00 2.71481723e-01 5.52350104e-01 9.34300423e-01 7.50087723e-02 1.22655046e+00 5.60406506e-01 8.05557430e-01 -4.72481430e-01 -6.57463133e-01 3.48864555e-01 -6.55415580e-02 -5.41081727e-01 -7.67295182e-01 9.31352913e-01 -5.98489404e-01 -6.31153107e-01 1.02380252e+00 6.49918556e-01 -2.09367201e-02 1.19619407e-01 -1.48367298e+00 -5.91613472e-01 5.93223512e-01 -6.53414249e-01 6.97886705e-01 -1.89530402e-02 4.36724126e-01 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855a4d90-4012-49bb-ba44-bc59ec63fec5	gridmix-strong-regularization-through-local	null	null	https://www.sciencedirect.com/science/article/abs/pii/S0031320320303976	https://doi.org/10.1016/j.patcog.2020.107594	GridMix: Strong regularization through local context mapping	Recently developed regularization techniques improve the networks generalization by only considering the global context. Therefore, the network tends to focus on a few most discriminative subregions of an image for prediction accuracy, leading the network being sensitive to unseen or noisy data. To address this disadvantage, we introduce the concept of local context mapping by predicting patch-level labels and combine it with a method of local data augmentation by grid-based mixing, called GridMix. Through our analysis of intermediate representations, we show that our GridMix can effectively regularize the network model. Finally, our evaluation results indicate that GridMix outperforms state-of-the-art techniques in classification and adversarial robustness, and it achieves a comparable performance in weakly supervised object localization.	['Hyunjung Shim', 'Duhyeon Bang', 'Kyungjune Baek']	2021-01-01	null	null	null	pattern-recognition-2021-1	['weakly-supervised-object-localization']	['computer-vision']	[ 1.17995284e-01 4.30104248e-02 -4.63769197e-01 -4.06122118e-01 -9.61454213e-01 -5.60461998e-01 5.47796130e-01 -2.94868127e-02 -3.14783305e-01 6.34064794e-01 2.43684158e-01 8.65387246e-02 3.01729292e-02 -5.77774465e-01 -8.18290472e-01 -9.30441082e-01 9.37688202e-02 1.61139537e-02 2.12370321e-01 1.68946356e-01 -8.34523141e-02 7.18473673e-01 -1.00817358e+00 5.48944712e-01 6.56790435e-01 1.09159994e+00 1.58607662e-01 1.10395990e-01 1.99471518e-01 8.77719998e-01 -5.58921576e-01 -1.45521224e-01 2.00223878e-01 -3.38087231e-01 -6.44094646e-01 -1.43785626e-01 1.15384448e+00 -5.91310374e-02 -5.36516428e-01 1.24612343e+00 5.95847309e-01 2.35591263e-01 6.58404589e-01 -9.38680232e-01 -8.91355217e-01 4.33065504e-01 -4.63623315e-01 2.94170350e-01 -8.10492877e-03 -1.15761451e-01 9.17513788e-01 -1.06936789e+00 6.47506118e-01 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434eec3a-a303-49e2-ae42-4c63cff118a8	cit-emotionnet-cnn-interactive-transformer	2305.05548	null	https://arxiv.org/abs/2305.05548v1	https://arxiv.org/pdf/2305.05548v1.pdf	CIT-EmotionNet: CNN Interactive Transformer Network for EEG Emotion Recognition	Emotion recognition using Electroencephalogram (EEG) signals has emerged as a significant research challenge in affective computing and intelligent interaction. However, effectively combining global and local features of EEG signals to improve performance in emotion recognition is still a difficult task. In this study, we propose a novel CNN Interactive Transformer Network for EEG Emotion Recognition, known as CIT-EmotionNet, which efficiently integrates global and local features of EEG signals. Initially, we convert raw EEG signals into spatial-frequency representations, which serve as inputs. Then, we integrate Convolutional Neural Network (CNN) and Transformer within a single framework in a parallel manner. Finally, we design a CNN interactive Transformer module, which facilitates the interaction and fusion of local and global features, thereby enhancing the model's ability to extract both types of features from EEG spatial-frequency representations. The proposed CIT-EmotionNet outperforms state-of-the-art methods, achieving an average recognition accuracy of 98.57\% and 92.09\% on two publicly available datasets, SEED and SEED-IV, respectively.	['Tien-Ping Tan', 'Hua Ma', 'Wei Lu']	2023-05-07	null	null	null	null	['eeg', 'eeg-emotion-recognition', 'eeg']	['methodology', 'miscellaneous', 'time-series']	[-1.22684553e-01 -5.76514781e-01 6.12386167e-01 -6.49554193e-01 -3.70641053e-01 -2.31723770e-01 1.14727430e-01 6.00147061e-02 -6.04862332e-01 7.39686370e-01 -3.06755281e-03 2.03059614e-01 -1.49901047e-01 -6.36367738e-01 -3.10012251e-01 -6.82061732e-01 -2.49561146e-01 -3.26019853e-01 -5.00780642e-01 -1.57734081e-01 1.57303646e-01 3.94865215e-01 -1.56181765e+00 4.43031847e-01 9.68773842e-01 1.83794570e+00 -1.05674565e-01 2.45197490e-01 3.76454554e-02 5.54030061e-01 -7.72980332e-01 -2.55330026e-01 -3.07502061e-01 -2.87599862e-01 -5.03278911e-01 -4.48554307e-01 -3.70685309e-01 1.10272594e-01 -2.42629573e-01 1.07830799e+00 8.19481432e-01 1.49226293e-01 5.94526410e-01 -1.32723212e+00 -5.19616067e-01 2.30465099e-01 -3.42982620e-01 4.71984863e-01 4.39002693e-01 -9.01217312e-02 5.64198732e-01 -1.00335598e+00 -1.42023349e-02 5.81715465e-01 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21fc0757-c0e7-4284-951d-e51db60f7795	controlled-sparsity-via-constrained	2208.04425	null	https://arxiv.org/abs/2208.04425v2	https://arxiv.org/pdf/2208.04425v2.pdf	Controlled Sparsity via Constrained Optimization or: How I Learned to Stop Tuning Penalties and Love Constraints	The performance of trained neural networks is robust to harsh levels of pruning. Coupled with the ever-growing size of deep learning models, this observation has motivated extensive research on learning sparse models. In this work, we focus on the task of controlling the level of sparsity when performing sparse learning. Existing methods based on sparsity-inducing penalties involve expensive trial-and-error tuning of the penalty factor, thus lacking direct control of the resulting model sparsity. In response, we adopt a constrained formulation: using the gate mechanism proposed by Louizos et al. (2018), we formulate a constrained optimization problem where sparsification is guided by the training objective and the desired sparsity target in an end-to-end fashion. Experiments on CIFAR-{10, 100}, TinyImageNet, and ImageNet using WideResNet and ResNet{18, 50} models validate the effectiveness of our proposal and demonstrate that we can reliably achieve pre-determined sparsity targets without compromising on predictive performance.	['Simon Lacoste-Julien', 'Yoshua Bengio', 'Akram Erraqabi', 'Juan Ramirez', 'Jose Gallego-Posada']	2022-08-08	null	null	null	null	['sparse-learning']	['methodology']	[ 3.71319205e-01 2.30253845e-01 -1.88840672e-01 -5.66704631e-01 -4.19857442e-01 -2.06476778e-01 5.92279792e-01 -1.84152741e-02 -5.94925165e-01 5.31085432e-01 1.99400812e-01 -1.77501589e-01 -1.07918933e-01 -4.31148142e-01 -8.92013550e-01 -4.13212657e-01 -1.38220102e-01 2.77074039e-01 -9.80091840e-02 1.02734357e-01 9.87533033e-02 1.98491096e-01 -1.23245907e+00 2.13391736e-01 6.73500538e-01 1.18623114e+00 1.98807240e-01 3.82755876e-01 4.49413747e-01 1.02269411e+00 -3.23070079e-01 -1.81639925e-01 5.33049524e-01 -3.23976904e-01 -4.52989042e-01 2.01563910e-01 8.28240037e-01 -2.00066522e-01 -5.10594130e-01 1.09792364e+00 3.45578760e-01 2.43990600e-01 2.50690967e-01 -8.73348415e-01 -5.23140371e-01 9.05480206e-01 -4.26104426e-01 5.81301868e-01 -3.28429610e-01 1.93504140e-01 1.19788384e+00 -1.06563461e+00 3.91127557e-01 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81e0a534-e2e3-45bf-b721-73391d36180f	region-based-quality-estimation-network-for	1711.08766	null	http://arxiv.org/abs/1711.08766v2	http://arxiv.org/pdf/1711.08766v2.pdf	Region-based Quality Estimation Network for Large-scale Person Re-identification	One of the major restrictions on the performance of video-based person re-id is partial noise caused by occlusion, blur and illumination. Since different spatial regions of a single frame have various quality, and the quality of the same region also varies across frames in a tracklet, a good way to address the problem is to effectively aggregate complementary information from all frames in a sequence, using better regions from other frames to compensate the influence of an image region with poor quality. To achieve this, we propose a novel Region-based Quality Estimation Network (RQEN), in which an ingenious training mechanism enables the effective learning to extract the complementary region-based information between different frames. Compared with other feature extraction methods, we achieved comparable results of 92.4%, 76.1% and 77.83% on the PRID 2011, iLIDS-VID and MARS, respectively. In addition, to alleviate the lack of clean large-scale person re-id datasets for the community, this paper also contributes a new high-quality dataset, named "Labeled Pedestrian in the Wild (LPW)" which contains 7,694 tracklets with over 590,000 images. Despite its relatively large scale, the annotations also possess high cleanliness. Moreover, it's more challenging in the following aspects: the age of characters varies from childhood to elderhood; the postures of people are diverse, including running and cycling in addition to the normal walking state.	['Biao Leng', 'Yu Liu', 'Congrui Hetang', 'Shaofan Cai', 'Guanglu Song']	2017-11-23	null	null	null	null	['large-scale-person-re-identification']	['computer-vision']	[-3.68857861e-01 -6.91096246e-01 -5.86171001e-02 -2.80162632e-01 -3.71710032e-01 -8.47910717e-02 3.41728032e-01 -2.37465337e-01 -6.14525139e-01 1.08212519e+00 6.13627851e-01 6.61788046e-01 9.24999267e-02 -7.99299657e-01 -5.25722980e-01 -6.93255067e-01 8.90235156e-02 -9.59294103e-03 4.02808100e-01 -3.93311769e-01 -1.81464568e-01 3.10915411e-01 -1.87016368e+00 3.26757252e-01 1.01075327e+00 8.85163784e-01 8.26142356e-02 3.87693912e-01 1.74196616e-01 7.67426550e-01 -9.17713821e-01 -6.66931212e-01 1.58824831e-01 -3.19411933e-01 -4.38095570e-01 1.19412921e-01 8.87839913e-01 -6.03991091e-01 -7.52775967e-01 1.10772407e+00 8.31872582e-01 3.53211790e-01 3.70705664e-01 -1.34307265e+00 -6.83079660e-01 1.00242667e-01 -5.85227907e-01 3.73951107e-01 6.35840952e-01 2.05960676e-01 6.04270399e-01 -6.70434117e-01 6.83207631e-01 1.34438324e+00 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-1.99809447e-02 -8.43315661e-01 -6.52064145e-01 9.64874685e-01 2.18912497e-01 -1.00396442e+00 -6.42914772e-02 2.66138941e-01]	[14.657613754272461, 0.9452428817749023]
eba7afd4-5a95-483f-912c-02a55a024957	tw-star-at-semeval-2018-task-1-preprocessing	null	null	https://aclanthology.org/S18-1024	https://aclanthology.org/S18-1024.pdf	Tw-StAR at SemEval-2018 Task 1: Preprocessing Impact on Multi-label Emotion Classification	In this paper, we describe our contribution in SemEval-2018 contest. We tackled task 1 {``}Affect in Tweets{''}, subtask E-c {``}Detecting Emotions (multi-label classification){''}. A multilabel classification system Tw-StAR was developed to recognize the emotions embedded in Arabic, English and Spanish tweets. To handle the multi-label classification problem via traditional classifiers, we employed the binary relevance transformation strategy while a TF-IDF scheme was used to generate the tweets{'} features. We investigated using single and combinations of several preprocessing tasks to further improve the performance. The results showed that specific combinations of preprocessing tasks could significantly improve the evaluation measures. This has been later emphasized by the official results as our system ranked 3rd for both Arabic and Spanish datasets and 14th for the English dataset.	['Ismail Babao{\\u{g}}lu', 'Chedi Bechikh Ali', 'Hala Mulki', 'Hatem Haddad']	2018-06-01	null	null	null	semeval-2018-6	['twitter-sentiment-analysis']	['natural-language-processing']	[ 1.72297865e-01 7.46960193e-02 1.29747629e-01 -6.61833048e-01 -9.52823997e-01 -7.54656017e-01 8.92706394e-01 6.88042164e-01 -9.60630417e-01 8.52729082e-01 -2.21472867e-02 -1.97355542e-03 -1.11979023e-01 -4.67230409e-01 -2.26835608e-01 -6.47846878e-01 -3.07974871e-02 3.61986220e-01 -2.43308723e-01 -7.11020112e-01 3.31107140e-01 4.61919665e-01 -1.94601822e+00 7.82645345e-01 7.03293920e-01 1.32831383e+00 -3.79024893e-01 5.93597353e-01 -2.68061757e-01 1.08729446e+00 -6.42996788e-01 -8.52280498e-01 -9.05813351e-02 -1.64385423e-01 -1.05757666e+00 -1.29807353e-01 1.94797348e-02 3.48283261e-01 5.11580825e-01 8.92340422e-01 5.74557185e-01 3.48074526e-01 1.03142869e+00 -1.35269523e+00 -2.20905989e-01 7.27676213e-01 -6.38073266e-01 -5.70426621e-02 5.81813514e-01 -5.11202037e-01 1.10845852e+00 -1.28905034e+00 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b03705b5-49d5-4965-84c6-a18c20c2a314	learning-semantics-for-visual-place	2201.09701	null	https://arxiv.org/abs/2201.09701v2	https://arxiv.org/pdf/2201.09701v2.pdf	Learning Semantics for Visual Place Recognition through Multi-Scale Attention	In this paper we address the task of visual place recognition (VPR), where the goal is to retrieve the correct GPS coordinates of a given query image against a huge geotagged gallery. While recent works have shown that building descriptors incorporating semantic and appearance information is beneficial, current state-of-the-art methods opt for a top down definition of the significant semantic content. Here we present the first VPR algorithm that learns robust global embeddings from both visual appearance and semantic content of the data, with the segmentation process being dynamically guided by the recognition of places through a multi-scale attention module. Experiments on various scenarios validate this new approach and demonstrate its performance against state-of-the-art methods. Finally, we propose the first synthetic-world dataset suited for both place recognition and segmentation tasks.	['Barbara Caputo', 'Carlo Masone', 'Gabriele Berton', 'Antonio Tavera', 'Valerio Paolicelli']	2022-01-24	null	null	null	null	['visual-place-recognition']	['computer-vision']	[ 3.26392837e-02 2.58925445e-02 4.83728014e-02 -4.72746104e-01 -8.84721041e-01 -7.26544142e-01 9.27526176e-01 3.54870468e-01 -7.18467474e-01 2.67505050e-01 -4.43837978e-02 -6.88475892e-02 1.11928649e-01 -7.82146037e-01 -9.40321267e-01 -5.77319682e-01 -2.02440202e-01 5.56426346e-01 6.18295789e-01 -2.92070389e-01 5.09649575e-01 7.63898849e-01 -1.66994739e+00 -2.13265076e-01 5.47596216e-01 1.03772819e+00 3.05598587e-01 6.04869843e-01 1.23531546e-03 3.64879102e-01 -4.19301003e-01 -2.97844023e-01 3.44522029e-01 -3.90610658e-03 -9.65987682e-01 2.68839091e-01 6.40276194e-01 -1.24593014e-02 -3.02324295e-01 1.04885054e+00 4.10823345e-01 4.75850821e-01 6.46762669e-01 -1.39487290e+00 -1.04713249e+00 -2.04951838e-01 -5.54399371e-01 2.16850698e-01 5.90032101e-01 -8.81865919e-02 1.14783216e+00 -8.37115407e-01 9.29236650e-01 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94268dc2-ad4b-43cf-92b6-b94e4e241c68	zero-shot-information-extraction-as-a-unified	2109.11171	null	https://arxiv.org/abs/2109.11171v1	https://arxiv.org/pdf/2109.11171v1.pdf	Zero-Shot Information Extraction as a Unified Text-to-Triple Translation	We cast a suite of information extraction tasks into a text-to-triple translation framework. Instead of solving each task relying on task-specific datasets and models, we formalize the task as a translation between task-specific input text and output triples. By taking the task-specific input, we enable a task-agnostic translation by leveraging the latent knowledge that a pre-trained language model has about the task. We further demonstrate that a simple pre-training task of predicting which relational information corresponds to which input text is an effective way to produce task-specific outputs. This enables the zero-shot transfer of our framework to downstream tasks. We study the zero-shot performance of this framework on open information extraction (OIE2016, NYT, WEB, PENN), relation classification (FewRel and TACRED), and factual probe (Google-RE and T-REx). The model transfers non-trivially to most tasks and is often competitive with a fully supervised method without the need for any task-specific training. For instance, we significantly outperform the F1 score of the supervised open information extraction without needing to use its training set.	['Dawn Song', 'Jie Tang', 'Haoyun Hong', 'Zui Chen', 'Xiao Liu', 'Chenguang Wang']	2021-09-23	zero-shot-information-extraction-as-a-unified-1	https://aclanthology.org/2021.emnlp-main.94	https://aclanthology.org/2021.emnlp-main.94.pdf	emnlp-2021-11	['open-information-extraction']	['natural-language-processing']	[ 3.42995912e-01 6.71406984e-01 -5.59383512e-01 -3.79005581e-01 -1.36890578e+00 -7.35247612e-01 1.04278338e+00 -4.35401406e-03 -3.82050902e-01 1.00400293e+00 1.85570210e-01 -6.64379001e-01 5.56247905e-02 -8.48870277e-01 -1.08658266e+00 2.34760251e-02 3.87818545e-01 9.63979065e-01 8.39443058e-02 -4.21956629e-01 -1.87232837e-01 -2.45741576e-01 -1.12393248e+00 7.86248565e-01 8.94888818e-01 1.00692677e+00 -6.90643340e-02 4.54987556e-01 -3.05899262e-01 7.20670164e-01 -2.31362551e-01 -1.03093660e+00 2.86775172e-01 -2.89029747e-01 -1.40778077e+00 -1.59011275e-01 2.65783757e-01 1.77205071e-01 -1.01401478e-01 7.36981094e-01 1.44622773e-01 -2.19963163e-01 9.95406806e-01 -1.21692610e+00 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1711d74f-bc78-42f2-9b97-3bf35a45691a	confining-brownian-motion-of-single	1701.03422	null	http://arxiv.org/abs/1701.03422v1	http://arxiv.org/pdf/1701.03422v1.pdf	Confining Brownian motion of single nanoparticles in an ABELtrap	Trapping nanoscopic objects to observe their dynamic behaviour for extended periods of time is an ongoing quest. Particularly, sub-100nm transparent objects are hard to catch and most techniques rely on immobilisation or transient diffusion through a confocal laser focus. We present an Anti-Brownian ELectrokinetic trap (pioneered by A. E. Cohen and W. E. Moerner) to hold nanoparticles and individual FoF1-ATP synthase proteins in solution. We are interested in the conformational dynamics of this membrane-bound rotary motor protein that we monitor using single-molecule FRET. The ABELtrap is an active feedback system cancelling the nano-object's Brownian motion by applying an electric field. We show how the induced electrokinetic forces confine the motion of nanoparticles and proteoliposomes to the centre of the trap.	[]	2017-01-12	null	null	null	null	['transparent-objects']	['computer-vision']	[ 3.41064930e-01 -1.72592670e-01 -2.23806977e-01 3.44161570e-01 -2.71278143e-01 -1.15837348e+00 4.71982509e-01 -3.39894235e-01 -9.11601484e-01 1.28937495e+00 -2.36361146e-01 1.27450852e-02 1.82285234e-01 -4.40366983e-01 -7.72496879e-01 -1.43588734e+00 3.33822906e-01 6.06287062e-01 7.72689939e-01 2.19187848e-02 7.09749103e-01 5.70299625e-01 -1.25700653e+00 6.87154382e-02 6.57847047e-01 6.47681206e-02 7.88191497e-01 1.03955972e+00 -5.07356897e-02 4.10238862e-01 -4.58852589e-01 1.48486570e-01 -3.28142196e-01 -2.12598592e-01 -4.06690270e-01 -6.58779025e-01 -5.21641783e-02 -3.17256525e-02 2.08014756e-01 7.01547146e-01 6.39699757e-01 -2.13342413e-01 5.96180916e-01 -5.22920787e-01 -3.57955575e-01 1.92379206e-01 -5.39830625e-01 3.99012566e-01 2.27553368e-01 1.15441233e-01 1.34414285e-01 -9.15538907e-01 1.27105808e+00 1.10663128e+00 5.39608657e-01 1.02973986e+00 -1.36358356e+00 -3.24183792e-01 -7.83301950e-01 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18867b75-502f-4cd8-9219-6acdaecbf283	detecting-broken-absorber-tubes-in-csp-plants	2211.14077	null	https://arxiv.org/abs/2211.14077v1	https://arxiv.org/pdf/2211.14077v1.pdf	Detecting broken Absorber Tubes in CSP plants using intelligent sampling and dual loss	Concentrated solar power (CSP) is one of the growing technologies that is leading the process of changing from fossil fuels to renewable energies. The sophistication and size of the systems require an increase in maintenance tasks to ensure reliability, availability, maintainability and safety. Currently, automatic fault detection in CSP plants using Parabolic Trough Collector systems evidences two main drawbacks: 1) the devices in use needs to be manually placed near the receiver tube, 2) the Machine Learning-based solutions are not tested in real plants. We address both gaps by combining the data extracted with the use of an Unmaned Aerial Vehicle, and the data provided by sensors placed within 7 real plants. The resulting dataset is the first one of this type and can help to standardize research activities for the problem of fault detection in this type of plants. Our work proposes supervised machine-learning algorithms for detecting broken envelopes of the absorber tubes in CSP plants. The proposed solution takes the class imbalance problem into account, boosting the accuracy of the algorithms for the minority class without harming the overall performance of the models. For a Deep Residual Network, we solve an imbalance and a balance problem at the same time, which increases by 5% the Recall of the minority class with no harm to the F1-score. Additionally, the Random Under Sampling technique boost the performance of traditional Machine Learning models, being the Histogram Gradient Boost Classifier the algorithm with the highest increase (3%) in the F1-Score. To the best of our knowledge, this paper is the first providing an automated solution to this problem using data from operating plants.	['José Miguel Díaz-Báñez', 'Juan Sebastián Valverde', 'Miguel Angel Pérez-Cutiño']	2022-11-25	null	null	null	null	['fault-detection']	['miscellaneous']	[ 2.30251327e-01 1.53111115e-01 -1.67287961e-02 1.95413291e-01 -1.55480221e-01 -7.22131252e-01 2.02132970e-01 5.48416495e-01 7.84926042e-02 6.98624730e-01 -6.25494778e-01 -3.52329671e-01 -5.48533142e-01 -1.10169041e+00 -5.60777128e-01 -9.42125559e-01 2.13310152e-01 5.04494905e-01 3.84260237e-01 -7.59021267e-02 2.73118287e-01 8.82476330e-01 -1.72037566e+00 2.53258854e-01 1.20160711e+00 1.37191856e+00 4.07820612e-01 3.58058840e-01 1.23717025e-01 4.91521686e-01 -7.35943913e-01 3.67447883e-01 3.43700051e-01 -4.22684520e-01 -4.58070993e-01 2.01718472e-02 3.74513399e-03 -1.71900228e-01 1.68592572e-01 8.33135605e-01 4.70489264e-01 -2.39194065e-01 7.98468590e-01 -1.45899868e+00 -1.61177948e-01 2.02064753e-01 -5.49781024e-01 -9.42929089e-02 5.02949171e-02 1.81955859e-01 5.57110012e-01 -5.64320803e-01 2.08805263e-01 3.77232581e-01 7.63837397e-01 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9099e48d-29b0-4245-9702-45968b9baeeb	distilling-knowledge-for-search-based	1805.11224	null	http://arxiv.org/abs/1805.11224v1	http://arxiv.org/pdf/1805.11224v1.pdf	Distilling Knowledge for Search-based Structured Prediction	Many natural language processing tasks can be modeled into structured prediction and solved as a search problem. In this paper, we distill an ensemble of multiple models trained with different initialization into a single model. In addition to learning to match the ensemble's probability output on the reference states, we also use the ensemble to explore the search space and learn from the encountered states in the exploration. Experimental results on two typical search-based structured prediction tasks -- transition-based dependency parsing and neural machine translation show that distillation can effectively improve the single model's performance and the final model achieves improvements of 1.32 in LAS and 2.65 in BLEU score on these two tasks respectively over strong baselines and it outperforms the greedy structured prediction models in previous literatures.	['Ting Liu', 'Yijia Liu', 'Wanxiang Che', 'Huaipeng Zhao', 'Bing Qin']	2018-05-29	distilling-knowledge-for-search-based-1	https://aclanthology.org/P18-1129	https://aclanthology.org/P18-1129.pdf	acl-2018-7	['transition-based-dependency-parsing']	['natural-language-processing']	[ 4.48160470e-01 7.41807759e-01 -6.08838320e-01 -6.59774899e-01 -1.41518855e+00 -4.93570566e-01 6.49264336e-01 -1.01178430e-03 -4.15892631e-01 1.11547291e+00 4.85435188e-01 -6.04342043e-01 3.90599877e-01 -3.08332443e-01 -8.64526212e-01 -6.21672511e-01 1.18568078e-01 9.76568460e-01 2.75621772e-01 8.29824619e-03 2.64255047e-01 -4.42839563e-02 -6.90843165e-01 4.39390481e-01 1.31279647e+00 6.30887687e-01 6.49213850e-01 5.61547279e-01 -4.09158707e-01 7.69066036e-01 -2.90636688e-01 -3.41479570e-01 3.90854813e-02 -5.19115925e-01 -1.09208989e+00 -5.49686909e-01 -1.18371276e-02 1.09626673e-01 -9.78263095e-02 1.12148404e+00 2.22635850e-01 2.36791223e-01 4.33276922e-01 -5.98477721e-01 -3.64294380e-01 1.13105297e+00 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ce92b3ef-74f5-49b7-9661-abf50fa5374a	accurate-and-robust-eye-contact-detection	1907.11115	null	https://arxiv.org/abs/1907.11115v1	https://arxiv.org/pdf/1907.11115v1.pdf	Accurate and Robust Eye Contact Detection During Everyday Mobile Device Interactions	Quantification of human attention is key to several tasks in mobile human-computer interaction (HCI), such as predicting user interruptibility, estimating noticeability of user interface content, or measuring user engagement. Previous works to study mobile attentive behaviour required special-purpose eye tracking equipment or constrained users' mobility. We propose a novel method to sense and analyse visual attention on mobile devices during everyday interactions. We demonstrate the capabilities of our method on the sample task of eye contact detection that has recently attracted increasing research interest in mobile HCI. Our method builds on a state-of-the-art method for unsupervised eye contact detection and extends it to address challenges specific to mobile interactive scenarios. Through evaluation on two current datasets, we demonstrate significant performance improvements for eye contact detection across mobile devices, users, or environmental conditions. Moreover, we discuss how our method enables the calculation of additional attention metrics that, for the first time, enable researchers from different domains to study and quantify attention allocation during mobile interactions in the wild.	['Mihai Bâce', 'Sander Staal', 'Andreas Bulling']	2019-07-25	null	null	null	null	['contact-detection']	['robots']	[ 5.75088024e-01 2.18399279e-02 -3.30489874e-01 1.20642647e-01 -3.23097199e-01 -4.86312568e-01 3.42088282e-01 1.56364232e-01 -5.45436203e-01 3.08361620e-01 1.97338194e-01 -6.51152551e-01 -4.21441495e-02 2.29371525e-02 -1.49359748e-01 -1.13521710e-01 1.17653869e-01 -1.08963504e-01 4.16513801e-01 8.23395734e-04 5.71275651e-01 2.74637431e-01 -2.05184364e+00 9.01729688e-02 6.92938864e-01 9.64036226e-01 3.61144304e-01 1.24296045e+00 3.72690231e-01 6.06165469e-01 -5.88368535e-01 -2.49275919e-02 -3.77308697e-01 -3.66804779e-01 -8.61391008e-01 1.48516625e-01 4.94579494e-01 -9.02098119e-02 -8.01562294e-02 5.65786362e-01 8.60576868e-01 3.59885424e-01 3.74727577e-01 -1.35478115e+00 -5.06321788e-01 -3.84391040e-01 -4.27129209e-01 9.36022401e-01 1.11132658e+00 1.62874952e-01 8.08114052e-01 -7.77220070e-01 2.44686320e-01 1.00680315e+00 7.13622391e-01 5.51075220e-01 -1.08643198e+00 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4.83263463e-01 8.06654811e-01 -8.31156135e-01 -3.84700716e-01 1.59763694e-01]	[14.095568656921387, 0.14299939572811127]
927bf19d-3ce3-4b0d-983a-403bc552ea75	aga-gan-attribute-guided-attention-generative	2111.10591	null	https://arxiv.org/abs/2111.10591v1	https://arxiv.org/pdf/2111.10591v1.pdf	AGA-GAN: Attribute Guided Attention Generative Adversarial Network with U-Net for Face Hallucination	The performance of facial super-resolution methods relies on their ability to recover facial structures and salient features effectively. Even though the convolutional neural network and generative adversarial network-based methods deliver impressive performances on face hallucination tasks, the ability to use attributes associated with the low-resolution images to improve performance is unsatisfactory. In this paper, we propose an Attribute Guided Attention Generative Adversarial Network which employs novel attribute guided attention (AGA) modules to identify and focus the generation process on various facial features in the image. Stacking multiple AGA modules enables the recovery of both high and low-level facial structures. We design the discriminator to learn discriminative features exploiting the relationship between the high-resolution image and their corresponding facial attribute annotations. We then explore the use of U-Net based architecture to refine existing predictions and synthesize further facial details. Extensive experiments across several metrics show that our AGA-GAN and AGA-GAN+U-Net framework outperforms several other cutting-edge face hallucination state-of-the-art methods. We also demonstrate the viability of our method when every attribute descriptor is not known and thus, establishing its application in real-world scenarios.	['Umapada Pal', 'Sukalpa Chanda', 'Abhishek Srivastava']	2021-11-20	null	null	null	null	['face-hallucination']	['computer-vision']	[ 3.47133726e-01 5.08436203e-01 9.21661407e-02 -5.44628620e-01 -9.43489194e-01 -1.83736011e-01 7.52620220e-01 -8.40797842e-01 2.28352189e-01 7.60412812e-01 5.56608438e-01 4.58985090e-01 1.09701365e-01 -1.01680112e+00 -8.45415711e-01 -6.77558362e-01 3.90299223e-02 4.13778573e-01 -3.40936422e-01 -5.22762001e-01 -2.17689782e-01 7.79397905e-01 -1.83219695e+00 4.87454504e-01 7.07855761e-01 1.11903691e+00 -7.51452148e-02 3.13664526e-01 8.88370350e-02 8.93693089e-01 -3.64599615e-01 -6.70112967e-01 3.94985616e-01 -6.38098896e-01 -6.41602039e-01 2.44072422e-01 8.31796467e-01 -6.41954780e-01 -4.41029608e-01 9.75497305e-01 7.01149523e-01 3.78351510e-02 7.21162617e-01 -1.27486527e+00 -1.31401515e+00 1.65183023e-01 -5.99265277e-01 -1.44060254e-02 5.59136331e-01 3.44809182e-02 8.33655417e-01 -1.30743790e+00 8.33226562e-01 1.58657587e+00 6.64381623e-01 1.06668329e+00 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f3c18c1a-f36a-4819-bb09-2fcaba413480	a-memory-model-for-question-answering-from	2305.07565	null	https://arxiv.org/abs/2305.07565v1	https://arxiv.org/pdf/2305.07565v1.pdf	A Memory Model for Question Answering from Streaming Data Supported by Rehearsal and Anticipation of Coreference Information	Existing question answering methods often assume that the input content (e.g., documents or videos) is always accessible to solve the task. Alternatively, memory networks were introduced to mimic the human process of incremental comprehension and compression of the information in a fixed-capacity memory. However, these models only learn how to maintain memory by backpropagating errors in the answers through the entire network. Instead, it has been suggested that humans have effective mechanisms to boost their memorization capacities, such as rehearsal and anticipation. Drawing inspiration from these, we propose a memory model that performs rehearsal and anticipation while processing inputs to memorize important information for solving question answering tasks from streaming data. The proposed mechanisms are applied self-supervised during training through masked modeling tasks focused on coreference information. We validate our model on a short-sequence (bAbI) dataset as well as large-sequence textual (NarrativeQA) and video (ActivityNet-QA) question answering datasets, where it achieves substantial improvements over previous memory network approaches. Furthermore, our ablation study confirms the proposed mechanisms' importance for memory models.	['Marie-Francine Moens', 'Alvaro Soto', 'Vladimir Araujo']	2023-05-12	null	null	null	null	['memorization']	['natural-language-processing']	[ 6.50963247e-01 6.04743481e-01 -3.53974439e-02 -3.82262707e-01 -6.45584822e-01 -3.94961953e-01 6.55423939e-01 4.72938895e-01 -7.96595275e-01 6.29533947e-01 6.27135813e-01 -3.73931527e-01 1.69560721e-03 -8.33483994e-01 -1.01441133e+00 -9.01426449e-02 -6.36210293e-02 4.18447554e-01 3.33663046e-01 -2.25388840e-01 4.84445661e-01 -1.89988762e-01 -1.66781271e+00 9.26033080e-01 8.39646220e-01 7.56237328e-01 6.19989157e-01 9.99305248e-01 -3.40650648e-01 1.80263829e+00 -7.27834642e-01 -5.09965301e-01 -2.17109278e-01 -8.54904294e-01 -1.64409029e+00 -3.43793154e-01 5.79644024e-01 -7.72701502e-01 -8.98252606e-01 6.04385674e-01 2.96507210e-01 5.25479317e-01 2.87801921e-01 -8.11186910e-01 -8.42691243e-01 9.15291846e-01 -1.58544921e-03 7.10547209e-01 7.47380018e-01 3.79518598e-01 1.05275512e+00 -9.10942376e-01 8.32263827e-01 1.01850820e+00 3.59702349e-01 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f4592ecf-74eb-4efe-b669-7d088357ef91	mast-a-memory-augmented-self-supervised	2002.07793	null	https://arxiv.org/abs/2002.07793v2	https://arxiv.org/pdf/2002.07793v2.pdf	MAST: A Memory-Augmented Self-supervised Tracker	Recent interest in self-supervised dense tracking has yielded rapid progress, but performance still remains far from supervised methods. We propose a dense tracking model trained on videos without any annotations that surpasses previous self-supervised methods on existing benchmarks by a significant margin (+15%), and achieves performance comparable to supervised methods. In this paper, we first reassess the traditional choices used for self-supervised training and reconstruction loss by conducting thorough experiments that finally elucidate the optimal choices. Second, we further improve on existing methods by augmenting our architecture with a crucial memory component. Third, we benchmark on large-scale semi-supervised video object segmentation(aka. dense tracking), and propose a new metric: generalizability. Our first two contributions yield a self-supervised network that for the first time is competitive with supervised methods on standard evaluation metrics of dense tracking. When measuring generalizability, we show self-supervised approaches are actually superior to the majority of supervised methods. We believe this new generalizability metric can better capture the real-world use-cases for dense tracking, and will spur new interest in this research direction.	['Weidi Xie', 'Erika Lu', 'Zihang Lai']	2020-02-18	mast-a-memory-augmented-self-supervised-1	http://openaccess.thecvf.com/content_CVPR_2020/html/Lai_MAST_A_Memory-Augmented_Self-Supervised_Tracker_CVPR_2020_paper.html	http://openaccess.thecvf.com/content_CVPR_2020/papers/Lai_MAST_A_Memory-Augmented_Self-Supervised_Tracker_CVPR_2020_paper.pdf	cvpr-2020-6	['unsupervised-video-object-segmentation']	['computer-vision']	[-2.32543349e-02 -1.20664518e-02 -7.34486103e-01 -4.40370500e-01 -7.83170819e-01 -6.33481026e-01 5.50337374e-01 -1.51695684e-01 -4.76432741e-01 7.04525590e-01 4.17387158e-01 3.29788029e-02 1.02109477e-01 -3.15989166e-01 -1.02405298e+00 -3.67389083e-01 -3.44974995e-01 6.57487452e-01 6.56825662e-01 3.07282627e-01 -1.41177773e-01 3.89857352e-01 -1.53422618e+00 -6.63779452e-02 5.54056525e-01 9.30266857e-01 -1.05777152e-01 6.22230828e-01 1.88925952e-01 1.03901637e+00 -3.78804207e-01 -5.12918591e-01 3.83875370e-01 -3.99432540e-01 -9.79712784e-01 3.51814240e-01 1.43339312e+00 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9da5ddfa-7a7b-459b-8748-6c248dd2a3ee	strategies-to-improve-few-shot-learning-for	null	null	https://aclanthology.org/2022.suki-1.3	https://aclanthology.org/2022.suki-1.3.pdf	Strategies to Improve Few-shot Learning for Intent Classification and Slot-Filling	Intent classification (IC) and slot filling (SF) are two fundamental tasks in modern Natural Language Understanding (NLU) systems. Collecting and annotating large amounts of data to train deep learning models for such systems are not scalable. This problem can be addressed by learning from few examples using fast supervised meta-learning techniques such as prototypical networks. In this work, we systematically investigate how contrastive learning and data augmentation methods can benefit these existing meta-learning pipelines for jointly modelled IC/SF tasks. Through extensive experiments across standard IC/SF benchmarks (SNIPS and ATIS), we show that our proposed approaches outperform standard meta-learning methods: contrastive losses as a regularizer in conjunction with prototypical networks consistently outperform the existing state-of-the-art for both IC and SF tasks, while data augmentation strategies primarily improve few-shot IC by a significant margin	['Benjamin Han', 'Vijay Ramani', 'Ehi Nosakhare', 'Hazem El-Hammamy', 'Michael Amoake', 'Vishal Rohra', 'Alex Fischer', 'Karine Ip Kiun Chong', 'Amr Sharaf', 'Samyadeep Basu']	null	null	null	null	naacl-suki-2022-7	['intent-classification', 'slot-filling']	['natural-language-processing', 'natural-language-processing']	[ 4.99482304e-01 2.55905986e-01 -8.96577120e-01 -5.71675897e-01 -7.81227171e-01 -1.40664652e-01 9.71265137e-01 4.54011083e-01 -8.38841081e-01 8.40756834e-01 2.78296441e-01 -3.52002114e-01 8.89103711e-02 -6.09850824e-01 -7.46697485e-01 -6.85229525e-02 1.87407345e-01 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ed093647-383c-4f37-a805-2b1a72b183bb	investigating-fairness-disparities-in-peer	2211.06398	null	https://arxiv.org/abs/2211.06398v1	https://arxiv.org/pdf/2211.06398v1.pdf	Investigating Fairness Disparities in Peer Review: A Language Model Enhanced Approach	Double-blind peer review mechanism has become the skeleton of academic research across multiple disciplines including computer science, yet several studies have questioned the quality of peer reviews and raised concerns on potential biases in the process. In this paper, we conduct a thorough and rigorous study on fairness disparities in peer review with the help of large language models (LMs). We collect, assemble, and maintain a comprehensive relational database for the International Conference on Learning Representations (ICLR) conference from 2017 to date by aggregating data from OpenReview, Google Scholar, arXiv, and CSRanking, and extracting high-level features using language models. We postulate and study fairness disparities on multiple protective attributes of interest, including author gender, geography, author, and institutional prestige. We observe that the level of disparity differs and textual features are essential in reducing biases in the predictive modeling. We distill several insights from our analysis on study the peer review process with the help of large LMs. Our database also provides avenues for studying new natural language processing (NLP) methods that facilitate the understanding of the peer review mechanism. We study a concrete example towards automatic machine review systems and provide baseline models for the review generation and scoring tasks such that the database can be used as a benchmark.	['Dan Roth', 'Zhun Deng', 'Hongming Zhang', 'Jiayao Zhang']	2022-11-07	null	null	null	null	['review-generation']	['natural-language-processing']	[-2.98631936e-01 3.84838194e-01 -1.03448319e+00 -5.22716105e-01 -8.36015046e-01 -3.03012669e-01 1.09980428e+00 8.09463501e-01 -6.12631083e-01 7.80468225e-01 5.76241851e-01 -7.62371719e-01 -3.04904670e-01 -5.90032279e-01 -5.97547114e-01 6.08142205e-02 3.23956817e-01 4.17422689e-02 -5.05034924e-01 -1.82161972e-01 9.10463870e-01 9.26729366e-02 -1.37143958e+00 1.10653214e-01 1.16218102e+00 5.88410974e-01 -5.83335221e-01 3.62062812e-01 -3.11177909e-01 9.99861836e-01 -6.87358975e-01 -1.27559769e+00 4.42946583e-01 -6.50374666e-02 -5.08438826e-01 -6.03774071e-01 8.48860621e-01 1.33029856e-02 -3.94854605e-01 8.32871437e-01 4.11993414e-01 -1.04983196e-01 1.11635685e+00 -1.31222773e+00 -1.54324615e+00 1.07866585e+00 -9.09468293e-01 2.94503361e-01 3.55274260e-01 -1.03825457e-01 1.46311557e+00 -9.40603971e-01 6.32395327e-01 1.40486336e+00 4.28587645e-01 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4fb60838-2d54-4f14-9604-3d9b60ad98a2	contextualized-attention-based-knowledge	2010.11066	null	https://arxiv.org/abs/2010.11066v4	https://arxiv.org/pdf/2010.11066v4.pdf	Contextualized Attention-based Knowledge Transfer for Spoken Conversational Question Answering	Spoken conversational question answering (SCQA) requires machines to model complex dialogue flow given the speech utterances and text corpora. Different from traditional text question answering (QA) tasks, SCQA involves audio signal processing, passage comprehension, and contextual understanding. However, ASR systems introduce unexpected noisy signals to the transcriptions, which result in performance degradation on SCQA. To overcome the problem, we propose CADNet, a novel contextualized attention-based distillation approach, which applies both cross-attention and self-attention to obtain ASR-robust contextualized embedding representations of the passage and dialogue history for performance improvements. We also introduce the spoken conventional knowledge distillation framework to distill the ASR-robust knowledge from the estimated probabilities of the teacher model to the student. We conduct extensive experiments on the Spoken-CoQA dataset and demonstrate that our approach achieves remarkable performance in this task.	['Yuexian Zou', 'Nuo Chen', 'Chenyu You']	2020-10-21	null	null	null	null	['audio-signal-processing']	['audio']	[ 2.13285387e-01 3.75920415e-01 5.03896236e-01 -7.23798692e-01 -1.51230073e+00 -5.83998621e-01 5.83186626e-01 1.34295121e-01 -4.83445317e-01 6.16553366e-01 8.69567215e-01 -4.40398127e-01 1.37737975e-01 -4.56217110e-01 -4.07615036e-01 -4.41877931e-01 2.07768187e-01 5.48733950e-01 1.35945380e-01 -4.83530492e-01 -1.81526411e-02 -3.51135246e-02 -1.08859849e+00 4.34821457e-01 1.02735209e+00 8.57150614e-01 2.15739086e-01 1.40404367e+00 -4.97849762e-01 1.32646930e+00 -1.13515294e+00 -3.54025394e-01 -4.69878525e-01 -9.56990600e-01 -1.43212879e+00 -1.14424892e-01 3.68835121e-01 -4.64185596e-01 -7.18467593e-01 7.95051873e-01 6.23755157e-01 6.06562376e-01 5.19512594e-01 -8.68476629e-01 -8.78985345e-01 8.99852097e-01 2.74314694e-02 4.20231193e-01 9.24195170e-01 2.54368156e-01 1.44414949e+00 -1.01116061e+00 -2.83962805e-02 1.63418531e+00 2.03145131e-01 9.03795302e-01 -9.92133200e-01 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1.88886106e-01 8.40311348e-01 -1.77143887e-03 -4.96222019e-01 -4.31227013e-02]	[11.784612655639648, 7.965671062469482]
5f9af486-a9dc-4987-a898-802bb2000627	color-overmodification-emerges-from-data	2205.09172	null	https://arxiv.org/abs/2205.09172v1	https://arxiv.org/pdf/2205.09172v1.pdf	Color Overmodification Emerges from Data-Driven Learning and Pragmatic Reasoning	Speakers' referential expressions often depart from communicative ideals in ways that help illuminate the nature of pragmatic language use. Patterns of overmodification, in which a speaker uses a modifier that is redundant given their communicative goal, have proven especially informative in this regard. It seems likely that these patterns are shaped by the environment a speaker is exposed to in complex ways. Unfortunately, systematically manipulating these factors during human language acquisition is impossible. In this paper, we propose to address this limitation by adopting neural networks (NN) as learning agents. By systematically varying the environments in which these agents are trained, while keeping the NN architecture constant, we show that overmodification is more likely with environmental features that are infrequent or salient. We show that these findings emerge naturally in the context of a probabilistic model of pragmatic communication.	['Elisa Kreiss', 'Christopher Potts', 'Noah D. Goodman', 'Kunal Sinha', 'Fei Fang']	2022-05-18	null	null	null	null	['language-acquisition']	['natural-language-processing']	[ 3.92483324e-01 4.58249360e-01 -6.84944838e-02 -5.30327737e-01 -2.05147922e-01 -4.14626241e-01 8.95997941e-01 2.06455722e-01 -7.15090215e-01 4.43049580e-01 9.54082131e-01 -4.97073561e-01 -3.62283051e-01 -7.81903803e-01 -5.48388004e-01 -6.93827033e-01 2.46177614e-02 2.65097827e-01 -6.77424893e-02 -4.21571404e-01 3.06361586e-01 2.85682410e-01 -1.47094488e+00 1.60604253e-01 4.08035100e-01 -6.46255165e-02 5.78682721e-01 2.44782135e-01 -2.30180293e-01 8.62679958e-01 -6.86075687e-01 -1.14826411e-01 2.48063542e-02 -4.83968049e-01 -7.77553737e-01 3.72167751e-02 1.53267995e-01 -2.69457936e-01 -7.15058371e-02 9.48814988e-01 3.77800047e-01 2.55398810e-01 6.55303121e-01 -5.78057289e-01 -7.15847254e-01 1.25437546e+00 5.14423996e-02 4.13374364e-01 4.04823661e-01 3.22295755e-01 1.21663499e+00 -2.77032614e-01 7.95752525e-01 1.56298804e+00 4.52732623e-01 9.01297629e-01 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-3.92907888e-01 3.18006247e-01 5.14815152e-01 -7.53727555e-01 -6.75985277e-01 -3.03290069e-01]	[10.429924011230469, 8.640168190002441]
f5dc68ef-cd4c-45d2-a901-a2a051959781	on-coresets-for-support-vector-machines	2002.06469	null	https://arxiv.org/abs/2002.06469v1	https://arxiv.org/pdf/2002.06469v1.pdf	On Coresets for Support Vector Machines	We present an efficient coreset construction algorithm for large-scale Support Vector Machine (SVM) training in Big Data and streaming applications. A coreset is a small, representative subset of the original data points such that a models trained on the coreset are provably competitive with those trained on the original data set. Since the size of the coreset is generally much smaller than the original set, our preprocess-then-train scheme has potential to lead to significant speedups when training SVM models. We prove lower and upper bounds on the size of the coreset required to obtain small data summaries for the SVM problem. As a corollary, we show that our algorithm can be used to extend the applicability of any off-the-shelf SVM solver to streaming, distributed, and dynamic data settings. We evaluate the performance of our algorithm on real-world and synthetic data sets. Our experimental results reaffirm the favorable theoretical properties of our algorithm and demonstrate its practical effectiveness in accelerating SVM training.	['Murad Tukan', 'Daniela Rus', 'Dan Feldman', 'Cenk Baykal']	2020-02-15	null	null	null	null	['small-data']	['computer-vision']	[ 2.01247975e-01 -2.06173941e-01 -5.74014068e-01 -5.76455891e-01 -5.40004849e-01 -5.20949125e-01 1.07671410e-01 2.87903965e-01 -2.81040519e-01 7.41508007e-01 -1.41019404e-01 -4.55712676e-01 4.36224975e-02 -6.82005107e-01 -1.04242849e+00 -6.82937920e-01 -1.86324641e-01 6.84825599e-01 4.60573912e-01 -1.07932188e-01 2.25616038e-01 3.07091266e-01 -1.96319926e+00 5.93296528e-01 9.05212224e-01 1.16365016e+00 1.25762299e-01 8.16331387e-01 1.07224882e-01 6.38670146e-01 -8.12620521e-01 9.26756859e-02 3.92757952e-01 -4.39136513e-02 -7.26720095e-01 2.10299984e-01 5.34067333e-01 -3.76920134e-01 -1.37236893e-01 8.27696621e-01 1.50614634e-01 -9.98377800e-03 1.74296677e-01 -1.80759084e+00 1.55218169e-01 5.10384202e-01 -6.49177372e-01 5.62521219e-01 1.61362454e-01 3.03988028e-02 1.05376530e+00 -5.43847978e-01 3.90235186e-01 5.69399297e-01 7.06481457e-01 2.75536925e-01 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8abb1a6e-6e0e-44e4-8105-faffc56654eb	decentralized-online-federated-g-network	2306.13029	null	https://arxiv.org/abs/2306.13029v1	https://arxiv.org/pdf/2306.13029v1.pdf	Decentralized Online Federated G-Network Learning for Lightweight Intrusion Detection	Cyberattacks are increasingly threatening networked systems, often with the emergence of new types of unknown (zero-day) attacks and the rise of vulnerable devices. While Machine Learning (ML)-based Intrusion Detection Systems (IDSs) have been shown to be extremely promising in detecting these attacks, the need to learn large amounts of labelled data often limits the applicability of ML-based IDSs to cybersystems that only have access to private local data. To address this issue, this paper proposes a novel Decentralized and Online Federated Learning Intrusion Detection (DOF-ID) architecture. DOF-ID is a collaborative learning system that allows each IDS used for a cybersystem to learn from experience gained in other cybersystems in addition to its own local data without violating the data privacy of other systems. As the performance evaluation results using public Kitsune and Bot-IoT datasets show, DOF-ID significantly improves the intrusion detection performance in all collaborating nodes simultaneously with acceptable computation time for online learning.	['Erol Gelenbe', 'Baran Can Gül', 'Mert Nakıp']	2023-06-22	null	null	null	null	['intrusion-detection']	['miscellaneous']	[-4.23964620e-01 6.42424077e-02 -1.72775462e-01 -9.13752913e-02 -1.27657607e-01 -1.06836462e+00 7.17971802e-01 3.39189380e-01 -3.82016808e-01 5.73061228e-01 -5.99026203e-01 -7.60719061e-01 -1.84470713e-01 -8.59440029e-01 -3.45258385e-01 -5.35416782e-01 -3.80405694e-01 4.17243958e-01 5.98257303e-01 5.03563657e-02 -1.14323474e-01 1.02893615e+00 -1.18601143e+00 1.23247184e-01 2.64021546e-01 1.03210568e+00 -7.72153437e-01 6.92720652e-01 3.05216815e-02 9.27485466e-01 -7.50923753e-01 -3.95951331e-01 8.70026171e-01 6.01157211e-02 -6.95692420e-01 -4.63024557e-01 -1.09607242e-01 -3.36119324e-01 -4.26046580e-01 9.79132056e-01 3.07702273e-01 2.50041559e-02 6.74232244e-02 -2.07977891e+00 -1.61108986e-01 4.93519574e-01 -1.14081226e-01 7.93051645e-02 1.20415486e-01 3.38711590e-01 5.23641229e-01 -1.09120399e-01 6.46810949e-01 1.06683278e+00 4.60499227e-01 6.50387108e-01 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-6.23194158e-01 9.26401913e-02 2.40803838e-01 -2.67831504e-01 -6.94137931e-01 -3.96012962e-01]	[5.34013032913208, 7.16211462020874]
d4669ec8-759f-4428-a8d0-c5717e17af74	light-weight-residual-dense-attention-net-for	2004.06930	null	https://arxiv.org/abs/2004.06930v2	https://arxiv.org/pdf/2004.06930v2.pdf	Light Weight Residual Dense Attention Net for Spectral Reconstruction from RGB Images	Hyperspectral Imaging is the acquisition of spectral and spatial information of a particular scene. Capturing such information from a specialized hyperspectral camera remains costly. Reconstructing such information from the RGB image achieves a better solution in both classification and object recognition tasks. This work proposes a novel light weight network with very less number of parameters about 233,059 parameters based on Residual dense model with attention mechanism to obtain this solution. This network uses Coordination Convolutional Block to get the spatial information. The weights from this block are shared by two independent feature extraction mechanisms, one by dense feature extraction and the other by the multiscale hierarchical feature extraction. Finally, the features from both the feature extraction mechanisms are globally fused to produce the 31 spectral bands. The network is trained with NTIRE 2020 challenge dataset and thus achieved 0.0457 MRAE metric value with less computational complexity.	['S. M. Md. Mansoor Roomi', 'K. Uma', 'D Synthiya Vinothini', 'D. Sabari Nathan', 'B. Sathya Bama']	2020-04-15	null	null	null	null	['spectral-reconstruction']	['computer-vision']	[ 7.33181238e-01 -3.68241161e-01 3.66683215e-01 -4.44745421e-01 -8.71279597e-01 -3.05565625e-01 1.40510574e-01 -3.96339118e-01 -5.37667155e-01 7.58225262e-01 3.51562276e-02 1.03483588e-01 -5.39226532e-01 -9.18730259e-01 -4.94755358e-01 -1.25496030e+00 -3.92391458e-02 -2.89260387e-01 -2.19370753e-01 -3.87310125e-02 3.36668342e-02 7.98488438e-01 -1.63668454e+00 3.19461316e-01 9.38684762e-01 1.74355030e+00 6.10588849e-01 6.75673366e-01 1.42563045e-01 7.41579473e-01 -9.28839594e-02 3.87710601e-01 7.34026372e-01 -4.96994816e-02 -6.02566004e-01 3.96806687e-01 4.10823792e-01 -5.52881718e-01 -3.35523695e-01 1.26258934e+00 6.94902718e-01 3.13100368e-01 4.09661978e-01 -8.10381949e-01 -6.87410295e-01 3.35024029e-01 -9.37163532e-01 7.40969777e-02 -2.49354318e-01 1.61470354e-01 8.46967041e-01 -1.03475130e+00 2.98933834e-01 7.58290172e-01 6.21404707e-01 -8.70678574e-02 -9.09210920e-01 -5.81086695e-01 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5.77323139e-01 -3.26297998e-01 -4.98984665e-01 4.54241857e-02]	[10.14787483215332, -2.0005011558532715]
6734158d-f1d2-4d14-940f-360c76d9aa22	self-supervised-pre-training-reduces-label	2010.15366	null	https://arxiv.org/abs/2010.15366v3	https://arxiv.org/pdf/2010.15366v3.pdf	Stabilizing Label Assignment for Speech Separation by Self-supervised Pre-training	Speech separation has been well developed, with the very successful permutation invariant training (PIT) approach, although the frequent label assignment switching happening during PIT training remains to be a problem when better convergence speed and achievable performance are desired. In this paper, we propose to perform self-supervised pre-training to stabilize the label assignment in training the speech separation model. Experiments over several types of self-supervised approaches, several typical speech separation models and two different datasets showed that very good improvements are achievable if a proper self-supervised approach is chosen.	['Hung-Yi Lee', 'Gene-Ping Yang', 'Yi-Chen Chen', 'Da-Rong Liu', 'Shun-Po Chuang', 'Sung-Feng Huang']	2020-10-29	null	null	null	null	['speaker-separation']	['speech']	[ 6.38239980e-01 3.89011041e-03 -3.95580560e-01 -6.24776483e-01 -7.62309194e-01 -4.14986610e-01 6.43116415e-01 -8.05892274e-02 -3.89187485e-01 9.86869693e-01 8.53988603e-02 -4.52709466e-01 -6.09754324e-01 -1.82269454e-01 -3.26738298e-01 -9.70681667e-01 -2.93548524e-01 9.47209537e-01 2.97381580e-01 2.31170431e-02 1.51388302e-01 4.38024372e-01 -1.74500215e+00 3.77360620e-02 9.98404741e-01 3.52078170e-01 1.49507046e-01 9.61930573e-01 -3.08975935e-01 4.03273463e-01 -8.46101701e-01 -1.06534973e-01 2.95134902e-01 -7.40698338e-01 -9.43825781e-01 4.36252832e-01 2.95962811e-01 6.45321429e-01 3.04776505e-02 1.34507143e+00 7.27488935e-01 4.33502048e-01 6.11220539e-01 -1.12219954e+00 1.88965928e-02 1.07031155e+00 -3.67048442e-01 4.82639730e-01 5.96490204e-02 -2.92217612e-01 9.80567932e-01 -5.47918975e-01 3.39747667e-01 1.03154504e+00 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79056387-88c3-44bc-8a39-d4c94d9fc736	distributed-one-class-learning	1802.03583	null	http://arxiv.org/abs/1802.03583v1	http://arxiv.org/pdf/1802.03583v1.pdf	Distributed One-class Learning	We propose a cloud-based filter trained to block third parties from uploading privacy-sensitive images of others to online social media. The proposed filter uses Distributed One-Class Learning, which decomposes the cloud-based filter into multiple one-class classifiers. Each one-class classifier captures the properties of a class of privacy-sensitive images with an autoencoder. The multi-class filter is then reconstructed by combining the parameters of the one-class autoencoders. The training takes place on edge devices (e.g. smartphones) and therefore users do not need to upload their private and/or sensitive images to the cloud. A major advantage of the proposed filter over existing distributed learning approaches is that users cannot access, even indirectly, the parameters of other users. Moreover, the filter can cope with the imbalanced and complex distribution of the image content and the independent probability of addition of new users. We evaluate the performance of the proposed distributed filter using the exemplar task of blocking a user from sharing privacy-sensitive images of other users. In particular, we validate the behavior of the proposed multi-class filter with non-privacy-sensitive images, the accuracy when the number of classes increases, and the robustness to attacks when an adversary user has access to privacy-sensitive images of other users.	['Andrea Cavallaro', 'Hamed Haddadi', 'Ali Shahin Shamsabadi']	2018-02-10	null	null	null	null	['one-class-classifier']	['methodology']	[-7.38215446e-02 -1.97657004e-01 -9.81979445e-02 -2.84824252e-01 -6.35667384e-01 -8.28941107e-01 2.26985827e-01 1.62486643e-01 -6.57281756e-01 5.33036888e-01 -1.59149975e-01 -1.80758893e-01 -1.33295044e-01 -1.03184974e+00 -9.70882058e-01 -9.96058404e-01 -1.52623346e-02 3.66497725e-01 2.41847381e-01 1.93576112e-01 -2.96796083e-01 6.01284623e-01 -1.70900452e+00 6.20601058e-01 6.48245037e-01 1.42456961e+00 -2.42717247e-02 6.61397398e-01 3.48108947e-01 5.41407049e-01 -5.97021699e-01 -8.06864858e-01 7.60060310e-01 9.10375342e-02 -2.31979251e-01 1.37208804e-01 3.72405767e-01 -6.78459644e-01 -5.01142263e-01 1.38709068e+00 2.86187381e-01 -1.15218438e-01 3.08892459e-01 -1.57472801e+00 -3.80485982e-01 2.05746859e-01 -3.19436163e-01 5.17106391e-02 -1.08123962e-02 -2.64442470e-02 5.52372575e-01 -2.98416227e-01 5.02200723e-01 8.58693421e-01 5.02702117e-01 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3865f9d0-65c6-42cc-a3a1-f36aa376d556	spda-cnn-unifying-semantic-part-detection-and	null	null	http://openaccess.thecvf.com/content_cvpr_2016/html/Zhang_SPDA-CNN_Unifying_Semantic_CVPR_2016_paper.html	http://openaccess.thecvf.com/content_cvpr_2016/papers/Zhang_SPDA-CNN_Unifying_Semantic_CVPR_2016_paper.pdf	SPDA-CNN: Unifying Semantic Part Detection and Abstraction for Fine-Grained Recognition	Most convolutional neural networks (CNNs) lack midlevel layers that model semantic parts of objects. This limits CNN-based methods from reaching their full potential in detecting and utilizing small semantic parts in recognition. Introducing such mid-level layers can facilitate the extraction of part-specific features which can be utilized for better recognition performance. This is particularly important in the domain of fine-grained recognition. In this paper, we propose a new CNN architecture that integrates semantic part detection and abstraction (SPDA-CNN) for fine-grained classification. The proposed network has two sub-networks: one for detection and one for recognition. The detection sub-network has a novel top-down proposal method to generate small semantic part candidates for detection. The classification sub-network introduces novel part layers that extract features from parts detected by the detection sub-network, and combine them for recognition. As a result, the proposed architecture provides an end-to-end network that performs detection, localization of multiple semantic parts, and whole object recognition within one framework that shares the computation of convolutional filters. Our method outperforms state-of-the-art methods with a large margin for small parts detection (e.g. our precision of 93.40% vs the best previous precision of 74.00% for detecting the head on CUB-2011). It also compares favorably to the existing state-of-the-art on fine-grained classification, e.g. it achieves 85.14% accuracy on CUB-2011.	['Xiaolei Huang', 'Ahmed Elgammal', 'Han Zhang', 'Dimitris Metaxas', 'Tao Xu', 'Shaoting Zhang', 'Mohamed Elhoseiny']	2016-06-01	null	null	null	cvpr-2016-6	['semantic-part-detection']	['computer-vision']	[ 4.87075560e-02 1.45616263e-01 -2.50426650e-01 -3.49795520e-01 -8.63668978e-01 -3.49149615e-01 6.74039543e-01 -6.36030510e-02 -3.67097497e-01 3.62011671e-01 6.64296793e-03 2.15568393e-01 1.78291127e-01 -1.10449457e+00 -1.04436207e+00 -5.83488941e-01 2.37105280e-01 3.81580025e-01 7.77446151e-01 4.17080112e-02 2.11016804e-01 9.85783517e-01 -2.08859634e+00 1.00219285e+00 4.20846492e-01 1.82786655e+00 2.74239272e-01 3.67389917e-01 -4.50962633e-01 5.56893647e-01 -8.13171566e-01 -2.20751747e-01 3.44587028e-01 1.25787154e-01 -7.11561739e-01 2.50430644e-01 9.31734383e-01 -2.50049114e-01 -1.59562111e-01 9.06008244e-01 4.01869237e-01 -2.02097386e-01 6.13098621e-01 -1.06901860e+00 -4.81622845e-01 5.21028519e-01 -4.79026139e-01 9.43147391e-03 1.17309159e-02 -6.98626041e-02 7.99720824e-01 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e92b73e8-bad1-4bbc-afdc-c315411efbad	zero-shot-object-detection-by-hybrid-region	1805.06157	null	http://arxiv.org/abs/1805.06157v2	http://arxiv.org/pdf/1805.06157v2.pdf	Zero-Shot Object Detection by Hybrid Region Embedding	Object detection is considered as one of the most challenging problems in computer vision, since it requires correct prediction of both classes and locations of objects in images. In this study, we define a more difficult scenario, namely zero-shot object detection (ZSD) where no visual training data is available for some of the target object classes. We present a novel approach to tackle this ZSD problem, where a convex combination of embeddings are used in conjunction with a detection framework. For evaluation of ZSD methods, we propose a simple dataset constructed from Fashion-MNIST images and also a custom zero-shot split for the Pascal VOC detection challenge. The experimental results suggest that our method yields promising results for ZSD.	['Nazli Ikizler-Cinbis', 'Ramazan Gokberk Cinbis', 'Berkan Demirel']	2018-05-16	null	null	null	null	['zero-shot-object-detection']	['computer-vision']	[ 2.35399321e-01 -3.66517216e-01 6.54692501e-02 -2.91728735e-01 -5.27915418e-01 -4.10438210e-01 8.42082739e-01 3.96837234e-01 -7.12894559e-01 2.34829322e-01 -2.57590204e-01 4.90135252e-02 7.35853165e-02 -4.65963840e-01 -6.93014026e-01 -5.46782732e-01 7.48579353e-02 3.70332271e-01 9.69606400e-01 -1.58393919e-01 9.14720297e-02 3.70425880e-01 -1.70724940e+00 2.65847385e-01 3.28413129e-01 1.09199011e+00 5.76841593e-01 8.54687333e-01 1.74561620e-01 5.48063040e-01 -6.06589854e-01 -6.12382591e-01 6.70136690e-01 6.17530607e-02 -5.45866549e-01 3.85310143e-01 7.62677729e-01 -3.20886344e-01 -5.86967729e-02 1.15174294e+00 6.24107659e-01 3.68239760e-01 9.18111265e-01 -1.39234626e+00 -3.84843200e-01 2.05045626e-01 -7.91725278e-01 5.50930202e-01 1.78189233e-01 3.59027654e-01 9.80051279e-01 -1.12520170e+00 6.30779147e-01 1.19357121e+00 5.60926974e-01 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cdfb7aaf-c475-44d7-bf84-baf18149e145	msgnn-a-spectral-graph-neural-network-based	2209.00546	null	https://arxiv.org/abs/2209.00546v4	https://arxiv.org/pdf/2209.00546v4.pdf	MSGNN: A Spectral Graph Neural Network Based on a Novel Magnetic Signed Laplacian	Signed and directed networks are ubiquitous in real-world applications. However, there has been relatively little work proposing spectral graph neural networks (GNNs) for such networks. Here we introduce a signed directed Laplacian matrix, which we call the magnetic signed Laplacian, as a natural generalization of both the signed Laplacian on signed graphs and the magnetic Laplacian on directed graphs. We then use this matrix to construct a novel efficient spectral GNN architecture and conduct extensive experiments on both node clustering and link prediction tasks. In these experiments, we consider tasks related to signed information, tasks related to directional information, and tasks related to both signed and directional information. We demonstrate that our proposed spectral GNN is effective for incorporating both signed and directional information, and attains leading performance on a wide range of data sets. Additionally, we provide a novel synthetic network model, which we refer to as the Signed Directed Stochastic Block Model, and a number of novel real-world data sets based on lead-lag relationships in financial time series.	['Mihai Cucuringu', 'Gesine Reinert', 'Michael Permultter', 'Yixuan He']	2022-09-01	null	null	null	null	['stochastic-block-model']	['graphs']	[ 1.48422092e-01 6.80083176e-03 -4.26522717e-02 -4.69891608e-01 -5.96437939e-02 -5.77933490e-01 4.85560566e-01 -1.37166083e-01 4.76513542e-02 6.93477035e-01 2.48374213e-02 -6.89440548e-01 -8.42667997e-01 -7.09448516e-01 -6.46752715e-01 -5.61332643e-01 -7.40794897e-01 3.75949740e-01 2.72732466e-01 -5.69866955e-01 4.77582216e-02 2.63452381e-01 -6.23529971e-01 -2.70181090e-01 1.09434998e+00 7.74686992e-01 -1.74421161e-01 5.08265078e-01 1.47401512e-01 9.83643651e-01 -2.53871560e-01 -3.37293148e-01 3.04888308e-01 -5.03002703e-01 -5.66264451e-01 -1.12343783e-04 3.14822304e-03 3.93032610e-01 -6.97602808e-01 1.22842646e+00 4.91818398e-01 -9.36344266e-03 8.26558828e-01 -1.72699547e+00 -1.11539567e+00 1.27635276e+00 -8.62494349e-01 1.75967976e-01 1.85663059e-01 -1.16439879e-01 1.23980117e+00 -7.70596325e-01 4.91067231e-01 1.23081577e+00 1.00689900e+00 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b19f4815-be13-44e8-ab04-0685edacb3ee	a-neural-network-based-energy-management	2206.06716	null	https://arxiv.org/abs/2206.06716v1	https://arxiv.org/pdf/2206.06716v1.pdf	A Neural Network-Based Energy Management System for PV-Battery Based Microgrids	A neural network-based energy management system (NN-EMS) has been proposed in this paper for islanded ac microgrids fed by multiple PV-battery based distributed generators (DG). The stochastic and unequal irradiation results in unequal PV output, which causes an unequal state-of-charge (SoC) among the batteries of the DGs. This effect may cause the difference in the SoCs to increase considerably over time, leading to some batteries reaching their SoC limits. These batteries would no longer be able to control the dc-link of the hybrid grid forming DG. The proposed NN-EMS ensures SoC balancing by learning an optimal state-action mapping using the outputs of an optimal power flow (OPF). The training dataset has been generated by executing a mixed-integer linear programming based OPF for droop-based island microgrids considering a practical generation-load profile. The resultant NN-EMS controller inherits the information of optimal states and the network behaviour. Compared to traditional time-ahead centralized methods, the proposed strategy does not require accurate generation-load forecasting. Further, it can also respond to the variations in the PV power in near-real-time without resorting to solving an OPF. The proposed NN-EMS controller has been validated by case studies on a CIGRE LV microgrid containing PV-battery hybrid DGs. The proposed concept can also be extended to synthesize decentralized controllers that can cooperate among themselves to achieve a global objective without communication.	['Mohammad Amin', 'Yusuf Gupta']	2022-06-14	null	null	null	null	['load-forecasting']	['miscellaneous']	[-3.20186466e-01 1.00214668e-01 5.80060557e-02 7.39451125e-02 -1.37157083e-01 -8.75361085e-01 2.76913702e-01 1.81414157e-01 1.46658987e-01 1.42602134e+00 -4.69537824e-01 3.16233432e-04 -3.71244341e-01 -9.18847024e-01 -5.46725988e-01 -1.38937783e+00 -2.01247454e-01 4.25512016e-01 -1.36336148e-01 -2.92897284e-01 -1.45501837e-01 4.55265999e-01 -1.49910057e+00 -3.40290755e-01 1.24456680e+00 1.06670177e+00 4.40514475e-01 4.11793351e-01 6.98240340e-01 1.98662519e-01 -1.02849078e+00 5.05839229e-01 2.95061737e-01 -4.70374435e-01 3.38204056e-01 -2.09776089e-01 -4.42364931e-01 -3.67156208e-01 -1.15905814e-01 1.24060261e+00 8.75301480e-01 6.33842945e-01 6.85625196e-01 -1.77066326e+00 -2.36829981e-01 3.80886614e-01 -4.34199162e-02 2.14236863e-02 -3.27255321e-03 2.82929271e-01 4.95780796e-01 -3.28648716e-01 1.93600833e-01 9.67403352e-01 2.03572720e-01 2.43835554e-01 -1.30353928e+00 -3.27508450e-01 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1ec587c7-15ef-416e-baad-c84068201610	exploiting-the-large-scale-german-broadcast	null	null	https://aclanthology.org/L14-1664	https://aclanthology.org/L14-1664.pdf	Exploiting the large-scale German Broadcast Corpus to boost the Fraunhofer IAIS Speech Recognition System	In this paper we describe the large-scale German broadcast corpus (GER-TV1000h) containing more than 1,000 hours of transcribed speech data. This corpus is unique in the German language corpora domain and enables significant progress in tuning the acoustic modelling of German large vocabulary continuous speech recognition (LVCSR) systems. The exploitation of this huge broadcast corpus is demonstrated by optimizing and improving the Fraunhofer IAIS speech recognition system. Due to the availability of huge amount of acoustic training data new training strategies are investigated. The performance of the automatic speech recognition (ASR) system is evaluated on several datasets and compared to previously published results. It can be shown that the word error rate (WER) using a larger corpus can be reduced by up to 9.1 {\textbackslash}{\%} relative. By using both larger corpus and recent training paradigms the WER was reduced by up to 35.8 {\textbackslash}{\%} relative and below 40 {\textbackslash}{\%} absolute even for spontaneous dialectal speech in noisy conditions, making the ASR output a useful resource for subsequent tasks like named entity recognition also in difficult acoustic situations.	['Michael Stadtschnitzer', 'Joachim Koehler', 'Jochen Schwenninger', 'Daniel Stein']	2014-05-01	null	null	null	lrec-2014-5	['acoustic-modelling']	['speech']	[ 2.68957406e-01 3.06760162e-01 3.90128493e-01 -5.13555527e-01 -1.37928414e+00 -3.79765242e-01 6.48648679e-01 1.86337188e-01 -9.80780721e-01 7.38573790e-01 1.63600519e-01 -4.44097817e-01 2.01544240e-02 -3.95359844e-01 -3.35756212e-01 -7.75158823e-01 -1.18723229e-01 6.06365860e-01 3.08682859e-01 -5.74851215e-01 -2.58326858e-01 5.08441806e-01 -1.76079690e+00 2.22454444e-01 2.43324950e-01 8.22995543e-01 7.55673587e-01 9.80543554e-01 -2.69176271e-02 3.43180716e-01 -1.27873874e+00 -1.32977009e-01 -8.61007422e-02 -2.18205348e-01 -7.41661668e-01 -7.33790323e-02 9.64109600e-02 -1.89547404e-03 -2.50990182e-01 9.60049510e-01 9.22252715e-01 5.49460113e-01 1.35796577e-01 -2.93954432e-01 1.32482439e-01 7.92861879e-01 2.29161769e-01 5.86499929e-01 2.98602968e-01 -1.27822638e-01 1.00875878e+00 -8.28314900e-01 4.88292217e-01 9.83394265e-01 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9e8c78d9-b096-44a4-99db-d0fbf927f1aa	fine-grained-visual-prompting	2306.04356	null	https://arxiv.org/abs/2306.04356v1	https://arxiv.org/pdf/2306.04356v1.pdf	Fine-Grained Visual Prompting	Vision-Language Models (VLMs), such as CLIP, have demonstrated impressive zero-shot transfer capabilities in image-level visual perception. However, these models have shown limited performance in instance-level tasks that demand precise localization and recognition. Previous works have suggested that incorporating visual prompts, such as colorful boxes or circles, can improve the ability of models to recognize objects of interest. Nonetheless, compared to language prompting, visual prompting designs are rarely explored. Existing approaches, which employ coarse visual cues such as colorful boxes or circles, often result in sub-optimal performance due to the inclusion of irrelevant and noisy pixels. In this paper, we carefully study the visual prompting designs by exploring more fine-grained markings, such as segmentation masks and their variations. In addition, we introduce a new zero-shot framework that leverages pixel-level annotations acquired from a generalist segmentation model for fine-grained visual prompting. Consequently, our investigation reveals that a straightforward application of blur outside the target mask, referred to as the Blur Reverse Mask, exhibits exceptional effectiveness. This proposed prompting strategy leverages the precise mask annotations to reduce focus on weakly related regions while retaining spatial coherence between the target and the surrounding background. Our Fine-Grained Visual Prompting (FGVP) demonstrates superior performance in zero-shot comprehension of referring expressions on the RefCOCO, RefCOCO+, and RefCOCOg benchmarks. It outperforms prior methods by an average margin of 3.0% to 4.6%, with a maximum improvement of 12.5% on the RefCOCO+ testA subset. The part detection experiments conducted on the PACO dataset further validate the preponderance of FGVP over existing visual prompting techniques. Code and models will be made available.	['Jian Yang', 'Xinlong Wang', 'Xiang Li', 'Yueze Wang', 'Lingfeng Yang']	2023-06-07	null	null	null	null	['visual-prompting']	['computer-vision']	[ 4.35227692e-01 -5.04647456e-02 -1.00210987e-01 -3.05184752e-01 -8.04233909e-01 -6.75250947e-01 6.86975241e-01 7.65385181e-02 -5.08267105e-01 4.70106184e-01 1.64763331e-01 -3.91310543e-01 2.17251197e-01 -2.72477001e-01 -8.75004470e-01 -6.23114347e-01 3.97545248e-01 -1.39039174e-01 4.96020645e-01 -1.79523349e-01 4.65793073e-01 3.82995993e-01 -1.65772927e+00 5.51277518e-01 9.56664383e-01 8.73287141e-01 4.74641740e-01 5.50816119e-01 -3.33477229e-01 7.75392234e-01 -7.33431637e-01 -1.38488427e-01 2.96342522e-01 -1.26655847e-01 -5.23050845e-01 1.97086841e-01 1.10588884e+00 -4.72617686e-01 -1.27465472e-01 1.05689287e+00 4.50262308e-01 2.09418595e-01 5.83414674e-01 -1.28993773e+00 -9.77319717e-01 2.97364980e-01 -8.72972906e-01 5.36143005e-01 5.44203937e-01 6.63910389e-01 1.02111828e+00 -1.15058732e+00 4.83390898e-01 1.31813908e+00 3.57259572e-01 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6cf589d1-a8f2-4793-80f5-06c1a6b54f31	consistent-text-categorization-using-data	2305.05402	null	https://arxiv.org/abs/2305.05402v2	https://arxiv.org/pdf/2305.05402v2.pdf	Consistent Text Categorization using Data Augmentation in e-Commerce	The categorization of massive e-Commerce data is a crucial, well-studied task, which is prevalent in industrial settings. In this work, we aim to improve an existing product categorization model that is already in use by a major web company, serving multiple applications. At its core, the product categorization model is a text classification model that takes a product title as an input and outputs the most suitable category out of thousands of available candidates. Upon a closer inspection, we found inconsistencies in the labeling of similar items. For example, minor modifications of the product title pertaining to colors or measurements majorly impacted the model's output. This phenomenon can negatively affect downstream recommendation or search applications, leading to a sub-optimal user experience. To address this issue, we propose a new framework for consistent text categorization. Our goal is to improve the model's consistency while maintaining its production-level performance. We use a semi-supervised approach for data augmentation and presents two different methods for utilizing unlabeled samples. One method relies directly on existing catalogs, while the other uses a generative model. We compare the pros and cons of each approach and present our experimental results.	['Ariel Raviv', 'Noa Avigdor-Elgrabli', 'Stav Yanovsky Daye', 'Guy Horowitz']	2023-05-09	null	null	null	null	['product-categorization', 'text-categorization']	['miscellaneous', 'natural-language-processing']	[ 2.68669099e-01 -1.18868396e-01 -2.58292466e-01 -5.63135028e-01 -3.98940176e-01 -6.49112761e-01 4.74489868e-01 5.08896410e-01 -1.27327979e-01 2.82933563e-01 -3.86856981e-02 -5.21914840e-01 -1.16741687e-01 -8.12321723e-01 -4.52780843e-01 -4.46362317e-01 4.36180532e-01 4.40842897e-01 1.07856520e-01 -3.08776855e-01 5.23332477e-01 9.96049792e-02 -1.96214819e+00 4.82276976e-01 9.83753502e-01 1.22021830e+00 1.86513990e-01 1.73452467e-01 -5.28646350e-01 4.28265184e-01 -5.01432478e-01 -7.66934931e-01 4.28912789e-01 -4.10004705e-01 -6.87313437e-01 5.08503258e-01 4.73281592e-01 -4.43782061e-02 1.47257119e-01 1.34550905e+00 3.55606042e-02 2.21677765e-01 7.00214088e-01 -1.42547464e+00 -9.00182605e-01 8.20083499e-01 -4.03378397e-01 -1.33258760e-01 9.14328545e-03 -2.06594989e-01 1.30909336e+00 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cbca4678-fb14-4f6a-91a9-07d9f8cf59d0	multi-view-contrastive-graph-clustering	2110.11842	null	https://arxiv.org/abs/2110.11842v1	https://arxiv.org/pdf/2110.11842v1.pdf	Multi-view Contrastive Graph Clustering	With the explosive growth of information technology, multi-view graph data have become increasingly prevalent and valuable. Most existing multi-view clustering techniques either focus on the scenario of multiple graphs or multi-view attributes. In this paper, we propose a generic framework to cluster multi-view attributed graph data. Specifically, inspired by the success of contrastive learning, we propose multi-view contrastive graph clustering (MCGC) method to learn a consensus graph since the original graph could be noisy or incomplete and is not directly applicable. Our method composes of two key steps: we first filter out the undesirable high-frequency noise while preserving the graph geometric features via graph filtering and obtain a smooth representation of nodes; we then learn a consensus graph regularized by graph contrastive loss. Results on several benchmark datasets show the superiority of our method with respect to state-of-the-art approaches. In particular, our simple approach outperforms existing deep learning-based methods.	['Zhao Kang', 'Erlin Pan']	2021-10-22	null	http://proceedings.neurips.cc/paper/2021/hash/10c66082c124f8afe3df4886f5e516e0-Abstract.html	http://proceedings.neurips.cc/paper/2021/file/10c66082c124f8afe3df4886f5e516e0-Paper.pdf	neurips-2021-12	['graph-clustering']	['graphs']	[-1.81355491e-01 -1.01862483e-01 3.57063971e-02 -1.69903666e-01 -6.35498583e-01 -5.82478523e-01 5.69500625e-01 3.18119884e-01 6.55307248e-02 2.47824013e-01 1.36081070e-01 1.66197270e-01 -3.26842427e-01 -9.64060545e-01 -5.57028174e-01 -9.31023896e-01 -6.79972321e-02 5.19957304e-01 9.48660150e-02 -5.50454631e-02 -3.58361602e-02 1.81734771e-01 -1.30335009e+00 2.15235949e-01 8.33709002e-01 5.59024513e-01 1.01345457e-01 3.38146478e-01 1.43230081e-01 8.48645568e-01 -2.14498505e-01 -4.08019871e-01 1.01186194e-01 -3.54618579e-01 -6.54560983e-01 6.06046557e-01 3.87863398e-01 1.65017515e-01 -5.13076067e-01 1.38148415e+00 6.01320744e-01 1.38567194e-01 4.95863765e-01 -1.39654231e+00 -9.21838164e-01 5.45736969e-01 -1.12265372e+00 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8ce831ca-24fa-41b5-b5d6-20a58a95bda7	zoom-rnn-a-novel-method-for-person	1809.09189	null	http://arxiv.org/abs/1809.09189v2	http://arxiv.org/pdf/1809.09189v2.pdf	Zoom-RNN: A Novel Method for Person Recognition Using Recurrent Neural Networks	The overwhelming popularity of social media has resulted in bulk amounts of personal photos being uploaded to the internet every day. Since these photos are taken in unconstrained settings, recognizing the identities of people among the photos remains a challenge. Studies have indicated that utilizing evidence other than face appearance improves the performance of person recognition systems. In this work, we aim to take advantage of additional cues obtained from different body regions in a zooming in fashion for person recognition. Hence, we present Zoom-RNN, a novel method based on recurrent neural networks for combining evidence extracted from the whole body, upper body, and head regions. Our model is evaluated on a challenging dataset, namely People In Photo Albums (PIPA), and we demonstrate that employing our system improves the performance of conventional fusion methods by a noticeable margin.	['Mina Ghadimi Atigh', 'Sina Mokhtarzadeh Azar', 'Mohammad Javadi', 'Ahmad Nickabadi', 'Sajjad Azami']	2018-09-24	null	null	null	null	['person-recognition']	['computer-vision']	[ 1.47981167e-01 -2.03099325e-01 1.18767563e-03 -4.60608959e-01 -5.33544421e-01 -1.67105019e-01 7.25061476e-01 -3.58937234e-01 -2.92711645e-01 5.03660679e-01 5.70115328e-01 4.15798038e-01 3.12883019e-01 -2.96453416e-01 -3.25877249e-01 -5.34877360e-01 1.77424282e-01 1.65071979e-01 -1.05326213e-01 -1.21064931e-01 -2.17037246e-01 3.29940975e-01 -1.73048735e+00 2.24264011e-01 3.99958909e-01 1.02228975e+00 -3.72852951e-01 5.37187696e-01 2.78889775e-01 5.64365685e-01 -7.03189671e-01 -8.50945234e-01 4.78548110e-01 -3.29037189e-01 -2.67649055e-01 4.22444701e-01 1.21445835e+00 -7.29055524e-01 -6.67877495e-01 9.17301059e-01 9.54362273e-01 2.63053298e-01 2.97557384e-01 -1.28732884e+00 -5.29688060e-01 5.33107162e-01 -8.00800323e-01 1.42683893e-01 8.24858248e-01 -7.85815269e-02 5.79088748e-01 -8.80988896e-01 4.33750719e-01 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9cc29b1a-6234-4dfe-9270-dcdc900071be	banet-bidirectional-aggregation-network-with	2003.14031	null	https://arxiv.org/abs/2003.14031v1	https://arxiv.org/pdf/2003.14031v1.pdf	BANet: Bidirectional Aggregation Network with Occlusion Handling for Panoptic Segmentation	Panoptic segmentation aims to perform instance segmentation for foreground instances and semantic segmentation for background stuff simultaneously. The typical top-down pipeline concentrates on two key issues: 1) how to effectively model the intrinsic interaction between semantic segmentation and instance segmentation, and 2) how to properly handle occlusion for panoptic segmentation. Intuitively, the complementarity between semantic segmentation and instance segmentation can be leveraged to improve the performance. Besides, we notice that using detection/mask scores is insufficient for resolving the occlusion problem. Motivated by these observations, we propose a novel deep panoptic segmentation scheme based on a bidirectional learning pipeline. Moreover, we introduce a plug-and-play occlusion handling algorithm to deal with the occlusion between different object instances. The experimental results on COCO panoptic benchmark validate the effectiveness of our proposed method. Codes will be released soon at https://github.com/Mooonside/BANet.	['Xi Li', 'Mingliang Xu', 'Guangchen Lin', 'Fangfang Wang', 'Bourahla Omar', 'Songyuan Li', 'Yiming Wu', 'Yifeng Chen', 'Junyi Feng']	2020-03-31	banet-bidirectional-aggregation-network-with-1	http://openaccess.thecvf.com/content_CVPR_2020/html/Chen_BANet_Bidirectional_Aggregation_Network_With_Occlusion_Handling_for_Panoptic_Segmentation_CVPR_2020_paper.html	http://openaccess.thecvf.com/content_CVPR_2020/papers/Chen_BANet_Bidirectional_Aggregation_Network_With_Occlusion_Handling_for_Panoptic_Segmentation_CVPR_2020_paper.pdf	cvpr-2020-6	['occlusion-handling']	['computer-vision']	[ 1.29566714e-01 -8.78178999e-02 -3.00194830e-01 -3.35192353e-01 -6.83028340e-01 -6.90189958e-01 6.07923329e-01 -5.03939018e-02 2.19096746e-02 2.47845232e-01 -2.16127515e-01 -2.27332607e-01 1.02930665e-02 -1.06296027e+00 -5.77351689e-01 -9.68945324e-01 1.74678534e-01 4.74014699e-01 4.95831162e-01 2.74751425e-01 2.08453387e-01 4.77930516e-01 -1.33298576e+00 2.48564780e-01 1.01605785e+00 1.04222310e+00 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75171e11-fde9-4dd1-8170-3dbf0fd37372	on-human-predictions-with-explanations-and	1811.07901	null	http://arxiv.org/abs/1811.07901v4	http://arxiv.org/pdf/1811.07901v4.pdf	On Human Predictions with Explanations and Predictions of Machine Learning Models: A Case Study on Deception Detection	Humans are the final decision makers in critical tasks that involve ethical and legal concerns, ranging from recidivism prediction, to medical diagnosis, to fighting against fake news. Although machine learning models can sometimes achieve impressive performance in these tasks, these tasks are not amenable to full automation. To realize the potential of machine learning for improving human decisions, it is important to understand how assistance from machine learning models affects human performance and human agency. In this paper, we use deception detection as a testbed and investigate how we can harness explanations and predictions of machine learning models to improve human performance while retaining human agency. We propose a spectrum between full human agency and full automation, and develop varying levels of machine assistance along the spectrum that gradually increase the influence of machine predictions. We find that without showing predicted labels, explanations alone slightly improve human performance in the end task. In comparison, human performance is greatly improved by showing predicted labels (>20% relative improvement) and can be further improved by explicitly suggesting strong machine performance. Interestingly, when predicted labels are shown, explanations of machine predictions induce a similar level of accuracy as an explicit statement of strong machine performance. Our results demonstrate a tradeoff between human performance and human agency and show that explanations of machine predictions can moderate this tradeoff.	['Vivian Lai', 'Chenhao Tan']	2018-11-19	null	null	null	null	['deception-detection']	['miscellaneous']	[ 2.32481748e-01 7.52045274e-01 -4.50628191e-01 -5.32296419e-01 -5.28278530e-01 -5.46895981e-01 9.16197062e-01 1.86869904e-01 -3.81607473e-01 7.56685793e-01 1.99397579e-01 -8.15797567e-01 2.42531160e-03 -5.02834260e-01 -3.31225753e-01 -1.27346009e-01 3.91590297e-01 4.15719986e-01 -5.50001077e-02 -2.03324854e-01 5.35901248e-01 5.49913585e-01 -1.06361270e+00 5.71704209e-01 1.02666759e+00 4.50959980e-01 -8.33077967e-01 8.26273978e-01 3.25957805e-01 1.36296225e+00 -7.02357948e-01 -7.74897099e-01 2.90167421e-01 -3.46261144e-01 -8.83228838e-01 1.78601459e-01 2.70023733e-01 -6.58158362e-01 -8.60834345e-02 7.59888768e-01 1.58187479e-01 -1.54544011e-01 7.99281716e-01 -1.64515591e+00 -7.26584613e-01 5.15250802e-01 -5.24617255e-01 2.15106327e-02 6.47236288e-01 6.98734522e-01 1.04210913e+00 -4.58857626e-01 4.68297362e-01 1.21966553e+00 6.86205089e-01 9.06272709e-01 -1.29360771e+00 -7.21810639e-01 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12954b9f-dd9d-40e7-99b5-017c366caa9c	high-resolution-face-completion-with-multiple	1801.07632	null	http://arxiv.org/abs/1801.07632v1	http://arxiv.org/pdf/1801.07632v1.pdf	High Resolution Face Completion with Multiple Controllable Attributes via Fully End-to-End Progressive Generative Adversarial Networks	We present a deep learning approach for high resolution face completion with multiple controllable attributes (e.g., male and smiling) under arbitrary masks. Face completion entails understanding both structural meaningfulness and appearance consistency locally and globally to fill in "holes" whose content do not appear elsewhere in an input image. It is a challenging task with the difficulty level increasing significantly with respect to high resolution, the complexity of "holes" and the controllable attributes of filled-in fragments. Our system addresses the challenges by learning a fully end-to-end framework that trains generative adversarial networks (GANs) progressively from low resolution to high resolution with conditional vectors encoding controllable attributes. We design novel network architectures to exploit information across multiple scales effectively and efficiently. We introduce new loss functions encouraging sharp completion. We show that our system can complete faces with large structural and appearance variations using a single feed-forward pass of computation with mean inference time of 0.007 seconds for images at 1024 x 1024 resolution. We also perform a pilot human study that shows our approach outperforms state-of-the-art face completion methods in terms of rank analysis. The code will be released upon publication.	['Shaoliang Nie', 'Zeyuan Chen', 'Tianfu Wu', 'Christopher G. Healey']	2018-01-23	null	null	null	null	['facial-inpainting']	['computer-vision']	[ 4.34045643e-01 3.60033751e-01 4.35998827e-01 -6.00135386e-01 -1.11552322e+00 -5.10768414e-01 4.65846241e-01 -5.36392391e-01 -1.32003531e-01 6.48817539e-01 2.05655739e-01 3.08694869e-01 8.54830351e-03 -6.98075652e-01 -9.87587690e-01 -3.06718022e-01 -2.18518227e-01 5.65873086e-01 -3.63815635e-01 -1.48415416e-01 -1.71915963e-01 7.29500890e-01 -1.53045547e+00 5.70300221e-01 6.09018862e-01 8.95895362e-01 2.34564021e-02 8.21104646e-01 3.20197046e-01 7.12474942e-01 -5.27671933e-01 -8.58150005e-01 7.19121814e-01 -1.36340871e-01 -5.96767724e-01 3.32054973e-01 1.25892770e+00 -8.06960881e-01 -2.81270564e-01 9.81497526e-01 6.43879831e-01 9.12783593e-02 3.99631828e-01 -1.02302241e+00 -9.91908133e-01 8.04744661e-02 -8.65686834e-01 -3.18248123e-01 5.73325038e-01 2.23318338e-01 7.39971995e-01 -1.35605705e+00 8.29499125e-01 1.67192018e+00 9.37169850e-01 8.83280456e-01 -1.60000265e+00 -8.09427142e-01 -2.67976839e-02 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2b1692e9-935b-4b4f-b9f7-f133f1e0fbc9	adaptive-local-component-aware-graph	2209.10073	null	https://arxiv.org/abs/2209.10073v1	https://arxiv.org/pdf/2209.10073v1.pdf	Adaptive Local-Component-aware Graph Convolutional Network for One-shot Skeleton-based Action Recognition	Skeleton-based action recognition receives increasing attention because the skeleton representations reduce the amount of training data by eliminating visual information irrelevant to actions. To further improve the sample efficiency, meta-learning-based one-shot learning solutions were developed for skeleton-based action recognition. These methods find the nearest neighbor according to the similarity between instance-level global average embedding. However, such measurement holds unstable representativity due to inadequate generalized learning on local invariant and noisy features, while intuitively, more fine-grained recognition usually relies on determining key local body movements. To address this limitation, we present the Adaptive Local-Component-aware Graph Convolutional Network, which replaces the comparison metric with a focused sum of similarity measurements on aligned local embedding of action-critical spatial/temporal segments. Comprehensive one-shot experiments on the public benchmark of NTU-RGB+D 120 indicate that our method provides a stronger representation than the global embedding and helps our model reach state-of-the-art.	['James Bailey', 'Mingming Gong', 'Qiuhong Ke', 'Anqi Zhu']	2022-09-21	null	null	null	null	['one-shot-learning']	['methodology']	[ 4.79921043e-01 -5.38027436e-02 -7.86251128e-01 -2.30577394e-01 -7.47482300e-01 -1.50572620e-02 6.15946352e-01 -5.65251261e-02 -4.76358414e-01 3.23847115e-01 7.89579749e-01 4.50421363e-01 -2.99428493e-01 -7.20014513e-01 -4.52881485e-01 -8.17546189e-01 -2.11962655e-01 1.27492771e-01 5.20110011e-01 -1.67627320e-01 3.03867877e-01 4.95960027e-01 -1.70195532e+00 6.30138397e-01 4.29459870e-01 1.22144544e+00 -2.18113646e-01 3.62722337e-01 -1.22749858e-01 9.21470582e-01 -3.64241898e-01 -1.75925288e-02 3.02180260e-01 -7.85811603e-01 -7.80434012e-01 1.39679343e-01 7.32741833e-01 -4.46889162e-01 -6.88945651e-01 8.69901240e-01 7.07195520e-01 4.27900314e-01 6.39110386e-01 -1.39764261e+00 -6.12708390e-01 3.66597265e-01 -5.90755820e-01 3.43643576e-01 5.86581707e-01 5.16639531e-01 1.07543504e+00 -6.53034270e-01 6.74580038e-01 1.17569053e+00 6.04107678e-01 6.57475233e-01 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-6.48917794e-01 6.91057265e-01 -4.42869077e-03 -1.08345759e+00 -3.38279068e-01 -1.42577544e-01]	[7.900422096252441, 0.4244266450405121]
6a3da21b-bbf4-47c3-a1c7-28bcce0d3574	locality-relationship-constrained-multi-view	2107.05073	null	https://arxiv.org/abs/2107.05073v1	https://arxiv.org/pdf/2107.05073v1.pdf	Locality Relationship Constrained Multi-view Clustering Framework	In most practical applications, it's common to utilize multiple features from different views to represent one object. Among these works, multi-view subspace-based clustering has gained extensive attention from many researchers, which aims to provide clustering solutions to multi-view data. However, most existing methods fail to take full use of the locality geometric structure and similarity relationship among samples under the multi-view scenario. To solve these issues, we propose a novel multi-view learning method with locality relationship constraint to explore the problem of multi-view clustering, called Locality Relationship Constrained Multi-view Clustering Framework (LRC-MCF). LRC-MCF aims to explore the diversity, geometric, consensus and complementary information among different views, by capturing the locality relationship information and the common similarity relationships among multiple views. Moreover, LRC-MCF takes sufficient consideration to weights of different views in finding the common-view locality structure and straightforwardly produce the final clusters. To effectually reduce the redundancy of the learned representations, the low-rank constraint on the common similarity matrix is considered additionally. To solve the minimization problem of LRC-MCF, an Alternating Direction Minimization (ADM) method is provided to iteratively calculate all variables LRC-MCF. Extensive experimental results on seven benchmark multi-view datasets validate the effectiveness of the LRC-MCF method.	['Wenzhe Liu', 'Wei Wei', 'Xiangzhu Meng']	2021-07-11	null	null	null	null	['multi-view-learning']	['computer-vision']	[-3.10133636e-01 -7.02155292e-01 -2.37987697e-01 -1.81932062e-01 -5.40588558e-01 -4.34290975e-01 3.29907477e-01 -2.59259164e-01 1.68912888e-01 6.50792718e-02 4.03129578e-01 2.70950139e-01 -5.91875970e-01 -4.37195092e-01 -1.67056441e-01 -1.02353394e+00 2.58169383e-01 7.95254484e-02 1.58533193e-02 6.22447096e-02 3.26226443e-01 2.40613848e-01 -1.35987604e+00 5.35912633e-01 9.08548295e-01 5.57594538e-01 5.33825338e-01 6.21227883e-02 1.14535324e-01 5.15738904e-01 -2.84918379e-02 4.48022820e-02 2.33666033e-01 -5.47485590e-01 -4.73217815e-01 6.87492669e-01 1.53533891e-01 -4.31538895e-02 -4.25092094e-02 1.17090273e+00 4.72752601e-01 2.71132827e-01 5.09663582e-01 -1.36277747e+00 -6.02397203e-01 1.82121262e-01 -1.19286144e+00 8.82929713e-02 3.80965829e-01 -2.58910835e-01 1.05600035e+00 -1.42171931e+00 5.82540691e-01 1.31489885e+00 2.77544469e-01 6.12430498e-02 -1.14691532e+00 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bc8f6fb6-1c43-4250-82c3-e0d0c5967758	text-mining-drug-chemical-protein	2111.15617	null	https://arxiv.org/abs/2111.15617v1	https://arxiv.org/pdf/2111.15617v1.pdf	Text Mining Drug/Chemical-Protein Interactions using an Ensemble of BERT and T5 Based Models	In Track-1 of the BioCreative VII Challenge participants are asked to identify interactions between drugs/chemicals and proteins. In-context named entity annotations for each drug/chemical and protein are provided and one of fourteen different interactions must be automatically predicted. For this relation extraction task, we attempt both a BERT-based sentence classification approach, and a more novel text-to-text approach using a T5 model. We find that larger BERT-based models perform better in general, with our BioMegatron-based model achieving the highest scores across all metrics, achieving 0.74 F1 score. Though our novel T5 text-to-text method did not perform as well as most of our BERT-based models, it outperformed those trained on similar data, showing promising results, achieving 0.65 F1 score. We believe a text-to-text approach to relation extraction has some competitive advantages and there is a lot of room for research advancement.	['Anas Abidin', 'Bo Liu', 'Carol Anderson', 'Hoo-chang Shin', 'Virginia Adams']	2021-11-30	null	null	null	null	['sentence-classification']	['natural-language-processing']	[ 2.93347448e-01 3.76962930e-01 -4.64844346e-01 -4.73163992e-01 -1.06539667e+00 -6.84031725e-01 5.69080770e-01 7.93156981e-01 -2.84942597e-01 1.20301747e+00 4.09230530e-01 -6.43486261e-01 -2.98651189e-01 -4.15768474e-01 -6.17007732e-01 -5.15908241e-01 -6.65734559e-02 7.29889214e-01 4.92047966e-02 -8.20159540e-02 -4.96544950e-02 3.32875222e-01 -6.50397182e-01 5.88658154e-01 7.95040846e-01 5.50831318e-01 -2.45349586e-01 5.68412781e-01 -1.30562880e-03 1.01909375e+00 -6.14211142e-01 -6.91764534e-01 -1.21613465e-01 -4.69417930e-01 -1.19950199e+00 -5.04545271e-01 2.15572044e-01 2.81546623e-01 -5.72280437e-02 7.31463432e-01 3.92868847e-01 -3.13955307e-01 8.55852187e-01 -9.18377936e-01 -3.90941650e-01 8.29815984e-01 -3.00206393e-01 3.07695232e-02 8.75419915e-01 1.04785152e-01 1.30777693e+00 -7.65126884e-01 1.00805855e+00 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87433569-9a07-4f63-b5f8-4f149e2ca328	hierarchical-attention-prototypical-networks	null	null	https://aclanthology.org/D19-1045	https://aclanthology.org/D19-1045.pdf	Hierarchical Attention Prototypical Networks for Few-Shot Text Classification	Most of the current effective methods for text classification tasks are based on large-scale labeled data and a great number of parameters, but when the supervised training data are few and difficult to be collected, these models are not available. In this work, we propose a hierarchical attention prototypical networks (HAPN) for few-shot text classification. We design the feature level, word level, and instance level multi cross attention for our model to enhance the expressive ability of semantic space, so it can highlight or weaken the importance of the features, words, and instances separately. We verify the effectiveness of our model on two standard benchmark few-shot text classification datasets{---}FewRel and CSID, and achieve the state-of-the-art performance. The visualization of hierarchical attention layers illustrates that our model can capture more important features, words, and instances. In addition, our attention mechanism increases support set augmentability and accelerates convergence speed in the training stage.	['Shengli Sun', 'Qingfeng Sun', 'Kevin Zhou', 'Tengchao Lv']	2019-11-01	null	null	null	ijcnlp-2019-11	['few-shot-text-classification']	['natural-language-processing']	[-2.34124400e-02 -3.04184765e-01 -3.37255597e-01 -4.29204136e-01 -2.00178519e-01 4.27922085e-02 5.10940015e-01 5.39201081e-01 -5.70700824e-01 5.47972858e-01 4.96245712e-01 5.29079325e-02 -1.59529895e-01 -9.13889229e-01 -4.49414439e-02 -4.81374681e-01 3.86134267e-01 3.69266927e-01 4.89829391e-01 -5.47839165e-01 2.96906292e-01 -9.93470028e-02 -1.70384705e+00 3.87637734e-01 7.42083192e-01 9.95778382e-01 2.89243817e-01 4.19825375e-01 -6.39427125e-01 8.06554973e-01 -5.77066898e-01 -9.32270139e-02 -8.84297118e-02 -2.67123818e-01 -7.72652805e-01 -9.38019007e-02 -3.95839103e-02 -3.46462280e-01 -4.95529592e-01 1.00630176e+00 7.05240548e-01 5.65857530e-01 5.62751293e-01 -1.33870208e+00 -1.04131031e+00 1.06381607e+00 -7.28479981e-01 3.81717175e-01 -2.45174598e-02 9.17156879e-03 1.29257774e+00 -1.00715554e+00 4.45738941e-01 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d8061fa3-f050-49dc-918f-ae9168751994	lossless-simd-compression-of-lidar-range-and	2209.08196	null	https://arxiv.org/abs/2209.08196v2	https://arxiv.org/pdf/2209.08196v2.pdf	Lossless SIMD Compression of LiDAR Range and Attribute Scan Sequences	As LiDAR sensors have become ubiquitous, the need for an efficient LiDAR data compression algorithm has increased. Modern LiDARs produce gigabytes of scan data per hour and are often used in applications with limited compute, bandwidth, and storage resources. We present a fast, lossless compression algorithm for LiDAR range and attribute scan sequences including multiple-return range, signal, reflectivity, and ambient infrared. Our algorithm -- dubbed "Jiffy" -- achieves substantial compression by exploiting spatiotemporal redundancy and sparsity. Speed is accomplished by maximizing use of single-instruction-multiple-data (SIMD) instructions. In autonomous driving, infrastructure monitoring, drone inspection, and handheld mapping benchmarks, the Jiffy algorithm consistently outcompresses competing lossless codecs while operating at speeds in excess of 65M points/sec on a single core. In a typical autonomous vehicle use case, single-threaded Jiffy achieves 6x compression of centimeter-precision range scans at 500+ scans per second. To ensure reproducibility and enable adoption, the software is freely available as an open source library.	['Jordan Ford', 'Jeff Ford']	2022-09-16	null	null	null	null	['data-compression']	['time-series']	[ 3.84577453e-01 -4.59409893e-01 -3.08070898e-01 -8.41014981e-01 -7.40199625e-01 -1.13837793e-01 4.03810233e-01 3.26982141e-01 -7.39352643e-01 5.49913645e-01 1.08850347e-02 -5.23624837e-01 -1.22911528e-01 -1.25415373e+00 -7.59404957e-01 -2.09357068e-01 -2.33574048e-01 6.10514998e-01 5.42930484e-01 -4.06650797e-04 3.95111769e-01 7.64985740e-01 -2.20313692e+00 -1.19464152e-01 7.17282653e-01 1.20303261e+00 1.07033774e-01 7.53575325e-01 -1.21537633e-01 3.16914052e-01 -1.98750019e-01 -2.30628997e-01 5.58239877e-01 2.23721519e-01 -3.84610981e-01 -5.59337556e-01 1.02919936e+00 -1.01049256e+00 -7.14068532e-01 6.19512081e-01 6.40900195e-01 -3.38838398e-02 2.41276205e-01 -1.24224305e+00 1.58722788e-01 6.19442426e-02 -6.90583110e-01 1.95565358e-01 5.56589179e-02 4.34131593e-01 7.25901365e-01 -8.86079371e-01 5.09301424e-01 1.15832794e+00 8.26109588e-01 -6.84377970e-03 -1.33159745e+00 -1.08299851e+00 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7985ec47-a1e4-4e8f-acdb-265cf7b77a92	biological-hotspot-mapping-in-coral-reefs	2305.02330	null	https://arxiv.org/abs/2305.02330v2	https://arxiv.org/pdf/2305.02330v2.pdf	Robot Goes Fishing: Rapid, High-Resolution Biological Hotspot Mapping in Coral Reefs with Vision-Guided Autonomous Underwater Vehicles	Coral reefs are fast-changing and complex ecosystems that are crucial to monitor and study. Biological hotspot detection can help coral reef managers prioritize limited resources for monitoring and intervention tasks. Here, we explore the use of autonomous underwater vehicles (AUVs) with cameras, coupled with visual detectors and photogrammetry, to map and identify these hotspots. This approach can provide high spatial resolution information in fast feedback cycles. To the best of our knowledge, we present one of the first attempts at using an AUV to gather visually-observed, fine-grain biological hotspot maps in concert with topography of a coral reefs. Our hotspot maps correlate with rugosity, an established proxy metric for coral reef biodiversity and abundance, as well as with our visual inspections of the 3D reconstruction. We also investigate issues of scaling this approach when applied to new reefs by using these visual detectors pre-trained on large public datasets.	['Yogesh Girdhar', 'Stewart Jamieson', 'Levi Cai', 'Daniel Yang']	2023-05-03	null	null	null	null	['3d-reconstruction']	['computer-vision']	[ 1.69564098e-01 2.40623783e-02 5.60597241e-01 1.96829718e-02 -4.62586135e-01 -8.68456900e-01 6.60477996e-01 4.31512386e-01 -9.23167884e-01 4.02802974e-01 6.02425933e-01 -4.62474346e-01 -1.15192816e-01 -1.02887297e+00 -5.53805828e-01 -5.93828619e-01 -6.53454900e-01 3.06028545e-01 7.39367783e-01 -2.20871866e-01 5.47843516e-01 6.55263901e-01 -1.67764473e+00 -1.02602072e-01 1.02038108e-01 6.11850321e-01 6.05760753e-01 1.02290487e+00 1.67744175e-01 9.08123404e-02 -2.30624601e-01 -8.53579268e-02 4.79795754e-01 7.70429447e-02 -4.59078401e-01 -3.53310198e-01 5.61239779e-01 -7.16204464e-01 1.13712341e-01 7.93720961e-01 5.93162477e-01 -2.02001423e-01 7.16875672e-01 -2.59567529e-01 -9.08384770e-02 1.38997018e-01 -7.04811871e-01 5.31476796e-01 1.29035652e-01 7.11892009e-01 9.88442659e-01 -8.31927776e-01 6.34465396e-01 1.32497334e+00 9.51873541e-01 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021964e5-aeb5-4fa6-8d20-be7d2ca48004	skrgan-sketching-rendering-unconditional	1908.04346	null	https://arxiv.org/abs/1908.04346v1	https://arxiv.org/pdf/1908.04346v1.pdf	SkrGAN: Sketching-rendering Unconditional Generative Adversarial Networks for Medical Image Synthesis	Generative Adversarial Networks (GANs) have the capability of synthesizing images, which have been successfully applied to medical image synthesis tasks. However, most of existing methods merely consider the global contextual information and ignore the fine foreground structures, e.g., vessel, skeleton, which may contain diagnostic indicators for medical image analysis. Inspired by human painting procedure, which is composed of stroking and color rendering steps, we propose a Sketching-rendering Unconditional Generative Adversarial Network (SkrGAN) to introduce a sketch prior constraint to guide the medical image generation. In our SkrGAN, a sketch guidance module is utilized to generate a high quality structural sketch from random noise, then a color render mapping is used to embed the sketch-based representations and resemble the background appearances. Experimental results show that the proposed SkrGAN achieves the state-of-the-art results in synthesizing images for various image modalities, including retinal color fundus, X-Ray, Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). In addition, we also show that the performances of medical image segmentation method have been improved by using our synthesized images as data augmentation.	['Yuting Xiao', 'Tianyang Zhang', 'Mengjie Guo', 'Jiang Liu', 'Huazhu Fu', 'Zaiwang Gu', 'Yitian Zhao', 'Shenghua Gao', 'Jun Cheng', 'Bing Yang']	2019-08-06	null	null	null	null	['medical-image-generation']	['medical']	[ 5.93589664e-01 2.62593329e-01 2.96739757e-01 -1.30650997e-01 -5.25615156e-01 -3.01338792e-01 5.52473545e-01 -6.48154795e-01 -9.61337462e-02 7.13221312e-01 4.21178862e-02 -3.99973989e-01 2.01047361e-01 -1.01887584e+00 -6.17706060e-01 -1.00326014e+00 4.05558199e-01 1.90308481e-01 1.89090535e-01 -1.83908805e-01 -3.62600833e-02 4.86274332e-01 -9.75336432e-01 3.09662849e-01 1.08603418e+00 8.66024852e-01 -5.59145445e-03 6.36278391e-01 -4.12592888e-01 7.90719986e-01 -7.44927347e-01 -5.29656589e-01 2.38191888e-01 -9.45867538e-01 -4.77213174e-01 4.43938583e-01 1.60596341e-01 -5.54502189e-01 -5.70061862e-01 1.26261711e+00 5.43704987e-01 -6.98457733e-02 8.34430575e-01 -1.16013038e+00 -1.06149685e+00 2.15662733e-01 -7.78798699e-01 -6.08413555e-02 7.11449906e-02 4.03059781e-01 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ec6c90d5-ec36-4147-bf66-5979e8102119	trust-your-model-light-field-depth-estimation	null	null	http://openaccess.thecvf.com/content_cvpr_2018/html/Schilling_Trust_Your_Model_CVPR_2018_paper.html	http://openaccess.thecvf.com/content_cvpr_2018/papers/Schilling_Trust_Your_Model_CVPR_2018_paper.pdf	Trust Your Model: Light Field Depth Estimation With Inline Occlusion Handling	We address the problem of depth estimation from light-field images. Our main contribution is a new way to handle occlusions which improves general accuracy and quality of object borders. In contrast to all prior work we work with a model which directly incorporates both depth and occlusion, using a local optimization scheme based on the PatchMatch algorithm. The key benefit of this joint approach is that we utilize all available data, and not erroneously discard valuable information in pre-processing steps. We see the benefit of our approach not only at improved object boundaries, but also at smooth surface reconstruction, where we outperform even methods which focus on good surface regularization. We have evaluated our method on a public light-field dataset, where we achieve state-of-the-art results in nine out of twelve error metrics, with a close tie for the remaining three.	['Bernd JÃ¤hne', 'Carsten Rother', 'Maximilian Diebold', 'Hendrik Schilling']	2018-06-01	null	null	null	cvpr-2018-6	['occlusion-handling']	['computer-vision']	[ 3.74349594e-01 4.30982038e-02 2.48892784e-01 -3.55380356e-01 -1.02441835e+00 -2.98219025e-01 4.96023685e-01 -6.61886483e-02 -5.02096593e-01 7.16796398e-01 6.99637905e-02 2.40831047e-01 5.58661409e-02 -6.67520285e-01 -5.62266052e-01 -6.63870454e-01 3.31087768e-01 4.26116616e-01 8.84388566e-01 2.74046808e-02 5.96256614e-01 7.02890337e-01 -1.71508288e+00 1.62736163e-01 8.82881105e-01 1.12568390e+00 7.84583315e-02 5.77697039e-01 1.35146938e-02 5.00889421e-01 2.95773474e-03 -4.49661434e-01 4.99678582e-01 2.00025016e-03 -8.15755010e-01 3.31511348e-01 1.26488221e+00 -4.84973252e-01 -9.63437557e-02 1.01510715e+00 5.66695869e-01 4.87810038e-02 5.38414299e-01 -6.85458124e-01 6.21933006e-02 -3.97250056e-01 -8.25366318e-01 8.04199558e-03 3.59381944e-01 2.21768349e-01 9.30400133e-01 -9.61810410e-01 1.01194155e+00 1.00488353e+00 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-1.23422337e+00 -1.52211592e-01 -6.96552753e-01 -5.90008438e-01 4.22179312e-01 7.12122023e-01 -1.02660894e+00 -5.61778665e-01 7.48385787e-02]	[8.887411117553711, -2.609441041946411]
ead10d03-c6df-4e76-8433-2313a3a01f16	learning-thin-plate-spline-motion-and	2302.08207	null	https://arxiv.org/abs/2302.08207v1	https://arxiv.org/pdf/2302.08207v1.pdf	Learning Thin-Plate Spline Motion and Seamless Composition for Parallax-Tolerant Unsupervised Deep Image Stitching	Traditional image stitching approaches tend to leverage increasingly complex geometric features (point, line, edge, etc.) for better performance. However, these hand-crafted features are only suitable for specific natural scenes with adequate geometric structures. In contrast, deep stitching schemes overcome the adverse conditions by adaptively learning robust semantic features, but they cannot handle large-parallax cases due to homography-based registration. To solve these issues, we propose UDIS++, a parallax-tolerant unsupervised deep image stitching technique. First, we propose a robust and flexible warp to model the image registration from global homography to local thin-plate spline motion. It provides accurate alignment for overlapping regions and shape preservation for non-overlapping regions by joint optimization concerning alignment and distortion. Subsequently, to improve the generalization capability, we design a simple but effective iterative strategy to enhance the warp adaption in cross-dataset and cross-resolution applications. Finally, to further eliminate the parallax artifacts, we propose to composite the stitched image seamlessly by unsupervised learning for seam-driven composition masks. Compared with existing methods, our solution is parallax-tolerant and free from laborious designs of complicated geometric features for specific scenes. Extensive experiments show our superiority over the SoTA methods, both quantitatively and qualitatively. The code will be available at https://github.com/nie-lang/UDIS2.	['Yao Zhao', 'Shuaicheng Liu', 'Kang Liao', 'Chunyu Lin', 'Lang Nie']	2023-02-16	null	null	null	null	['image-stitching']	['computer-vision']	[ 2.89863974e-01 -3.93419027e-01 6.85951859e-02 -2.25970879e-01 -5.08490622e-01 -5.42509198e-01 5.58790505e-01 -2.81380028e-01 -1.92653105e-01 3.87248248e-01 7.57457539e-02 2.13443130e-01 -3.00641686e-01 -6.12775266e-01 -6.45495296e-01 -8.86229336e-01 3.02150816e-01 2.64184415e-01 3.70066226e-01 -3.34193021e-01 4.38429892e-01 4.49478239e-01 -1.30890775e+00 -1.32914528e-01 1.31483424e+00 8.32677007e-01 2.37716630e-01 7.74359554e-02 3.96245196e-02 -6.05284087e-02 -2.20713884e-01 -2.40353346e-01 6.73462451e-01 -2.37642109e-01 -3.35769385e-01 5.07119715e-01 7.66759694e-01 -3.81741941e-01 -3.66650343e-01 1.19319165e+00 4.33770895e-01 2.86611021e-01 3.77744198e-01 -1.22556114e+00 -5.13291776e-01 7.02998564e-02 -1.19422197e+00 -2.04333395e-01 2.78790563e-01 2.75479019e-01 6.73809528e-01 -1.01160657e+00 5.13431072e-01 1.04468906e+00 9.39947009e-01 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7dfe37ec-4ab8-4f51-9635-4945d128cf22	few-shot-temporal-action-localization-with	2110.10552	null	https://arxiv.org/abs/2110.10552v1	https://arxiv.org/pdf/2110.10552v1.pdf	Few-Shot Temporal Action Localization with Query Adaptive Transformer	Existing temporal action localization (TAL) works rely on a large number of training videos with exhaustive segment-level annotation, preventing them from scaling to new classes. As a solution to this problem, few-shot TAL (FS-TAL) aims to adapt a model to a new class represented by as few as a single video. Exiting FS-TAL methods assume trimmed training videos for new classes. However, this setting is not only unnatural actions are typically captured in untrimmed videos, but also ignores background video segments containing vital contextual cues for foreground action segmentation. In this work, we first propose a new FS-TAL setting by proposing to use untrimmed training videos. Further, a novel FS-TAL model is proposed which maximizes the knowledge transfer from training classes whilst enabling the model to be dynamically adapted to both the new class and each video of that class simultaneously. This is achieved by introducing a query adaptive Transformer in the model. Extensive experiments on two action localization benchmarks demonstrate that our method can outperform all the state of the art alternatives significantly in both single-domain and cross-domain scenarios. The source code can be found in https://github.com/sauradip/fewshotQAT	['Tao Xiang', 'Xiatian Zhu', 'Sauradip Nag']	2021-10-20	null	null	null	null	['few-shot-temporal-action-localization', 'fine-grained-action-detection']	['computer-vision', 'computer-vision']	[ 5.15245140e-01 -9.39610973e-02 -5.23005366e-01 -1.73497155e-01 -9.53251600e-01 -5.51423609e-01 4.91752267e-01 -2.37470821e-01 -4.04167563e-01 6.38731599e-01 -5.27770035e-02 2.54043996e-01 1.80115864e-01 -3.26559722e-01 -8.54929328e-01 -6.59725666e-01 2.65992861e-02 2.84207374e-01 1.01695967e+00 6.12731241e-02 8.77910703e-02 2.45618716e-01 -1.24740756e+00 4.92059261e-01 8.59640718e-01 9.09820795e-01 4.31741416e-01 5.49126089e-01 2.25050338e-02 9.65903401e-01 -4.39981997e-01 -1.94431260e-01 4.77736145e-01 -6.50419891e-01 -9.43915844e-01 5.63372850e-01 6.31373763e-01 -4.27211791e-01 -4.54712391e-01 9.62143481e-01 1.82579324e-01 6.45472527e-01 2.18218431e-01 -1.28976035e+00 -3.95445317e-01 4.84675795e-01 -5.36039948e-01 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2.53098346e-02 9.82220292e-01 3.48997235e-01 6.55918539e-01 4.30737704e-01 -1.00116551e+00 -2.71138608e-01 -8.26349974e-01 -3.52365553e-01 3.16252559e-01 4.34792280e-01 -8.83704662e-01 -2.78005511e-01 6.32516742e-01]	[8.497187614440918, 0.6109372973442078]
fd940783-aa47-47d4-a493-5026e697c78c	benchmarking-node-outlier-detection-on-graphs	2206.10071	null	https://arxiv.org/abs/2206.10071v2	https://arxiv.org/pdf/2206.10071v2.pdf	BOND: Benchmarking Unsupervised Outlier Node Detection on Static Attributed Graphs	Detecting which nodes in graphs are outliers is a relatively new machine learning task with numerous applications. Despite the proliferation of algorithms developed in recent years for this task, there has been no standard comprehensive setting for performance evaluation. Consequently, it has been difficult to understand which methods work well and when under a broad range of settings. To bridge this gap, we present--to the best of our knowledge--the first comprehensive benchmark for unsupervised outlier node detection on static attributed graphs called BOND, with the following highlights. (1) We benchmark the outlier detection performance of 14 methods ranging from classical matrix factorization to the latest graph neural networks. (2) Using nine real datasets, our benchmark assesses how the different detection methods respond to two major types of synthetic outliers and separately to "organic" (real non-synthetic) outliers. (3) Using an existing random graph generation technique, we produce a family of synthetically generated datasets of different graph sizes that enable us to compare the running time and memory usage of the different outlier detection algorithms. Based on our experimental results, we discuss the pros and cons of existing graph outlier detection algorithms, and we highlight opportunities for future research. Importantly, our code is freely available and meant to be easily extendable: https://github.com/pygod-team/pygod/tree/main/benchmark	['Philip S. Yu', 'Zhihao Jia', 'George H. Chen', 'Jundong Li', 'Lichao Sun', 'Kai Shu', 'Hao Peng', 'Canyu Chen', 'Kaize Ding', 'Ruitong Zhang', 'Xiyang Hu', 'Xueying Ding', 'Yue Zhao', 'Yingtong Dou', 'Kay Liu']	2022-06-21	null	null	null	null	['graph-outlier-detection']	['graphs']	[-6.68790052e-03 9.73331835e-03 -4.76756413e-03 7.62663409e-02 -3.90168220e-01 -4.34857458e-01 3.18402648e-01 7.82913446e-01 -5.58674447e-02 5.42692900e-01 9.34638157e-02 -4.36036348e-01 -2.61108637e-01 -8.93088996e-01 -6.66034162e-01 -3.95865291e-01 -7.96936393e-01 5.03866613e-01 2.71952093e-01 -6.43275082e-02 1.86788201e-01 5.75058520e-01 -1.34564495e+00 1.55792793e-03 9.06409085e-01 6.02745652e-01 -4.96460289e-01 6.84580207e-01 3.16481143e-01 5.70212722e-01 -7.66791880e-01 -4.92452294e-01 4.38732445e-01 -3.36457372e-01 -6.75567269e-01 3.46960984e-02 5.15431464e-01 -1.07190587e-01 -7.28757858e-01 1.14537811e+00 6.06240690e-01 1.95610188e-02 5.24274051e-01 -1.79918206e+00 -6.36511683e-01 7.17983842e-01 -5.77008724e-01 5.29958367e-01 6.17786884e-01 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6e0f4b1e-d09c-4176-8481-6ae35632d0e6	hevas-headline-evaluation-and-analysis-system	null	null	https://aclanthology.org/W19-8910	https://aclanthology.org/W19-8910.pdf	HEvAS: Headline Evaluation and Analysis System	Automatic headline generation is a subtask of one-line summarization with many reported applications. Evaluation of systems generating headlines is a very challenging and undeveloped area. We introduce the Headline Evaluation and Analysis System (HEvAS) that performs automatic evaluation of systems in terms of a quality of the generated headlines. HEvAS provides two types of metrics{--} one which measures the informativeness of a headline, and another that measures its readability. The results of evaluation can be compared to the results of baseline methods which are implemented in HEvAS. The system also performs the statistical analysis of the evaluation results and provides different visualization charts. This paper describes all evaluation metrics, baselines, analysis, and architecture, utilized by our system.	['Marina Litvak', 'Itzhak Eretz Kdosha', 'Natalia Vanetik']	2019-09-01	null	null	null	ranlp-2019-9	['headline-generation']	['natural-language-processing']	[ 2.13123057e-02 2.60184139e-01 -8.18170458e-02 -2.51538903e-01 -1.00404334e+00 -7.14227915e-01 9.79144394e-01 7.65991569e-01 -1.06172711e-01 1.14693785e+00 1.03890657e+00 -1.26811117e-01 -3.04997005e-02 -5.83456337e-01 -3.53787243e-01 -2.08986402e-01 -4.25444208e-02 3.91345710e-01 4.28983897e-01 -5.09730279e-01 9.67018604e-01 3.44014436e-01 -1.49455535e+00 4.79293913e-01 9.07333791e-01 6.26865864e-01 -5.06120585e-02 1.44326735e+00 -2.83927113e-01 8.58632147e-01 -1.52879155e+00 -1.48739651e-01 -3.46836090e-01 -7.82641351e-01 -9.97737050e-01 -2.65421625e-02 6.88767076e-01 -1.11650221e-01 -7.51867071e-02 7.13474691e-01 7.54177392e-01 4.08832952e-02 9.18969870e-01 -1.32030666e+00 -4.02200073e-01 7.62542307e-01 -3.30342174e-01 5.60268283e-01 1.08809006e+00 -2.71315668e-02 9.97477531e-01 -5.59134662e-01 9.16217923e-01 1.09886384e+00 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7e0f32e0-aae2-4ec5-81f1-811d822ebd42	non-parametric-spatially-constrained-local	2006.12874	null	https://arxiv.org/abs/2006.12874v1	https://arxiv.org/pdf/2006.12874v1.pdf	Non-parametric spatially constrained local prior for scene parsing on real-world data	Scene parsing aims to recognize the object category of every pixel in scene images, and it plays a central role in image content understanding and computer vision applications. However, accurate scene parsing from unconstrained real-world data is still a challenging task. In this paper, we present the non-parametric Spatially Constrained Local Prior (SCLP) for scene parsing on realistic data. For a given query image, the non-parametric SCLP is learnt by first retrieving a subset of most similar training images to the query image and then collecting prior information about object co-occurrence statistics between spatial image blocks and between adjacent superpixels from the retrieved subset. The SCLP is powerful in capturing both long- and short-range context about inter-object correlations in the query image and can be effectively integrated with traditional visual features to refine the classification results. Our experiments on the SIFT Flow and PASCAL-Context benchmark datasets show that the non-parametric SCLP used in conjunction with superpixel-level visual features achieves one of the top performance compared with state-of-the-art approaches.	['Ligang Zhang']	2020-06-23	null	null	null	null	['scene-parsing']	['computer-vision']	[ 5.20393848e-01 -3.76614034e-01 -3.75997096e-01 -7.41538525e-01 -7.43735135e-01 -4.76074278e-01 4.71266419e-01 2.15636924e-01 -3.84791732e-01 3.84072751e-01 7.89870247e-02 -8.77485201e-02 -5.78349270e-02 -7.42999494e-01 -9.56171751e-01 -8.05506229e-01 -6.62620366e-02 1.24384515e-01 9.35642540e-01 2.19629779e-01 4.48447555e-01 5.34370482e-01 -1.72950494e+00 6.19321764e-01 7.43251264e-01 1.21842575e+00 9.75940585e-01 5.68066478e-01 -3.95423651e-01 8.48244131e-01 -2.64519095e-01 -1.24833770e-02 1.12119205e-01 -2.15962410e-01 -9.35182512e-01 3.89271617e-01 9.96612906e-01 -3.28116596e-01 -3.77422571e-02 1.32984138e+00 -1.95993762e-02 1.00079969e-01 4.32832986e-01 -1.00021994e+00 -3.54107261e-01 1.73464984e-01 -7.91529119e-01 5.17819345e-01 1.57943130e-01 -6.98798569e-03 1.09678626e+00 -7.80117691e-01 6.42288625e-01 1.40368962e+00 2.18622908e-01 9.14901961e-03 -1.11664474e+00 -5.46700835e-01 5.43679059e-01 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2f870845-42a0-464b-a052-8b5ecb5c7703	flexround-learnable-rounding-based-on-element	2306.00317	null	https://arxiv.org/abs/2306.00317v1	https://arxiv.org/pdf/2306.00317v1.pdf	FlexRound: Learnable Rounding based on Element-wise Division for Post-Training Quantization	Post-training quantization (PTQ) has been gaining popularity for the deployment of deep neural networks on resource-limited devices since unlike quantization-aware training, neither a full training dataset nor end-to-end training is required at all. As PTQ schemes based on reconstructing each layer or block output turn out to be effective to enhance quantized model performance, recent works have developed algorithms to devise and learn a new weight-rounding scheme so as to better reconstruct each layer or block output. In this work, we propose a simple yet effective new weight-rounding mechanism for PTQ, coined FlexRound, based on element-wise division instead of typical element-wise addition such that FlexRound enables jointly learning a common quantization grid size as well as a different scale for each pre-trained weight. Thanks to the reciprocal rule of derivatives induced by element-wise division, FlexRound is inherently able to exploit pre-trained weights when updating their corresponding scales, and thus, flexibly quantize pre-trained weights depending on their magnitudes. We empirically validate the efficacy of FlexRound on a wide range of models and tasks. To the best of our knowledge, our work is the first to carry out comprehensive experiments on not only image classification and natural language understanding but also natural language generation, assuming a per-tensor uniform PTQ setting. Moreover, we demonstrate, for the first time, that large language models can be efficiently quantized, with only a negligible impact on performance compared to half-precision baselines, achieved by reconstructing the output in a block-by-block manner.	['Dongsoo Lee', 'Se Jung Kwon', 'Jeonghoon Kim', 'Jung Hyun Lee']	2023-06-01	null	null	null	null	['quantization']	['methodology']	[ 2.80327380e-01 -8.02833494e-03 -2.42946625e-01 -4.34228033e-01 -9.56606090e-01 -5.70509493e-01 7.44994521e-01 2.98072904e-01 -6.94307566e-01 5.18887579e-01 -8.49437527e-03 -4.42528099e-01 1.72596648e-01 -8.98779154e-01 -1.10844064e+00 -6.66080594e-01 -1.26888037e-01 2.11374253e-01 7.49200657e-02 -1.78283229e-01 1.86247945e-01 3.55932146e-01 -1.57494450e+00 3.98309708e-01 6.44622445e-01 1.32561445e+00 3.76516491e-01 7.67692626e-01 -1.62845161e-02 6.28561497e-01 -5.08183241e-01 -6.82385683e-01 4.82306272e-01 -1.61986113e-01 -5.77343643e-01 6.66169822e-02 7.62366891e-01 -7.25760043e-01 -2.22610563e-01 1.04859257e+00 5.74531436e-01 -9.35159326e-02 5.76641560e-01 -8.66001368e-01 -6.77601457e-01 8.58523071e-01 -3.99042249e-01 6.82507157e-02 -1.25337631e-01 3.30165148e-01 1.32362247e+00 -8.48102689e-01 4.16903794e-01 1.09242904e+00 6.46510422e-01 5.06953120e-01 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4.65411395e-01 4.41685647e-01 -5.16923130e-01 -1.52337417e-01 1.40141159e-01]	[8.637809753417969, 3.1590521335601807]
513cfd7f-5115-4a24-b713-718d6d85d382	semantic-palette-guiding-scene-generation	2106.01629	null	https://arxiv.org/abs/2106.01629v1	https://arxiv.org/pdf/2106.01629v1.pdf	Semantic Palette: Guiding Scene Generation with Class Proportions	Despite the recent progress of generative adversarial networks (GANs) at synthesizing photo-realistic images, producing complex urban scenes remains a challenging problem. Previous works break down scene generation into two consecutive phases: unconditional semantic layout synthesis and image synthesis conditioned on layouts. In this work, we propose to condition layout generation as well for higher semantic control: given a vector of class proportions, we generate layouts with matching composition. To this end, we introduce a conditional framework with novel architecture designs and learning objectives, which effectively accommodates class proportions to guide the scene generation process. The proposed architecture also allows partial layout editing with interesting applications. Thanks to the semantic control, we can produce layouts close to the real distribution, helping enhance the whole scene generation process. On different metrics and urban scene benchmarks, our models outperform existing baselines. Moreover, we demonstrate the merit of our approach for data augmentation: semantic segmenters trained on real layout-image pairs along with additional ones generated by our approach outperform models only trained on real pairs.	['Matthieu Cord', 'Patrick Pérez', 'Himalaya Jain', 'Tuan-Hung Vu', 'Guillaume Le Moing']	2021-06-03	null	http://openaccess.thecvf.com//content/CVPR2021/html/Le_Moing_Semantic_Palette_Guiding_Scene_Generation_With_Class_Proportions_CVPR_2021_paper.html	http://openaccess.thecvf.com//content/CVPR2021/papers/Le_Moing_Semantic_Palette_Guiding_Scene_Generation_With_Class_Proportions_CVPR_2021_paper.pdf	cvpr-2021-1	['scene-generation']	['computer-vision']	[ 6.81733489e-01 3.05488229e-01 1.45841420e-01 -2.81259477e-01 -6.25606954e-01 -8.27401876e-01 9.16551292e-01 -1.05996557e-01 -9.29165706e-02 6.44121766e-01 2.07311153e-01 -1.86944962e-01 2.73921520e-01 -1.21140909e+00 -1.23239255e+00 -4.58687037e-01 4.64744419e-01 4.64017123e-01 1.99075025e-02 -3.24600995e-01 1.66857779e-01 5.73533356e-01 -1.57959425e+00 3.68265301e-01 1.22466362e+00 7.23698020e-01 4.49321300e-01 7.72036612e-01 -3.27793777e-01 6.99151993e-01 -7.96405792e-01 -4.36744034e-01 5.32447100e-01 -6.80917859e-01 -6.63943887e-01 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dfadd47c-a10b-435c-814f-7b8430d69d96	bayesian-learning-with-information-gain-1	2212.02003	null	https://arxiv.org/abs/2212.02003v1	https://arxiv.org/pdf/2212.02003v1.pdf	Bayesian Learning with Information Gain Provably Bounds Risk for a Robust Adversarial Defense	We present a new algorithm to learn a deep neural network model robust against adversarial attacks. Previous algorithms demonstrate an adversarially trained Bayesian Neural Network (BNN) provides improved robustness. We recognize the adversarial learning approach for approximating the multi-modal posterior distribution of a Bayesian model can lead to mode collapse; consequently, the model's achievements in robustness and performance are sub-optimal. Instead, we first propose preventing mode collapse to better approximate the multi-modal posterior distribution. Second, based on the intuition that a robust model should ignore perturbations and only consider the informative content of the input, we conceptualize and formulate an information gain objective to measure and force the information learned from both benign and adversarial training instances to be similar. Importantly. we prove and demonstrate that minimizing the information gain objective allows the adversarial risk to approach the conventional empirical risk. We believe our efforts provide a step toward a basis for a principled method of adversarially training BNNs. Our model demonstrate significantly improved robustness--up to 20%--compared with adversarial training and Adv-BNN under PGD attacks with 0.035 distortion on both CIFAR-10 and STL-10 datasets.	['Damith C. Ranasinghe', 'Javen Qinfeng Shi', 'Ehsan Abbasnejad', 'Bao Gia Doan']	2022-12-05	bayesian-learning-with-information-gain	https://openreview.net/forum?id=5_zwnS5oJDp	https://openreview.net/pdf?id=5_zwnS5oJDp	null	['adversarial-defense']	['adversarial']	[ 1.80630311e-01 5.89812875e-01 2.89663970e-01 -3.23559105e-01 -1.28167903e+00 -1.02785957e+00 6.30804539e-01 -3.57073396e-01 -3.40295166e-01 6.61538601e-01 2.38508493e-01 -5.48410177e-01 -1.77882224e-01 -8.74426961e-01 -1.31417060e+00 -9.45824087e-01 -3.04177880e-01 1.16217010e-01 5.35404310e-02 -1.61905393e-01 -6.09104000e-02 6.74696684e-01 -6.28222823e-01 -2.10836791e-02 4.80982721e-01 9.15863037e-01 -6.02174103e-01 8.04999173e-01 6.33729935e-01 7.86441863e-01 -8.57446790e-01 -9.69595432e-01 8.32475841e-01 -2.74626285e-01 -6.28059685e-01 -6.10826075e-01 6.70796156e-01 -8.67775559e-01 -1.13700187e+00 1.56132305e+00 6.42446399e-01 1.45769402e-01 9.24001753e-01 -1.24902332e+00 -7.01308727e-01 1.11985481e+00 -1.77197292e-01 1.69834912e-01 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d1a62f5e-66ff-43ae-afd6-71d31196caab	label-matching-semi-supervised-object-1	2206.06608	null	https://arxiv.org/abs/2206.06608v1	https://arxiv.org/pdf/2206.06608v1.pdf	Label Matching Semi-Supervised Object Detection	Semi-supervised object detection has made significant progress with the development of mean teacher driven self-training. Despite the promising results, the label mismatch problem is not yet fully explored in the previous works, leading to severe confirmation bias during self-training. In this paper, we delve into this problem and propose a simple yet effective LabelMatch framework from two different yet complementary perspectives, i.e., distribution-level and instance-level. For the former one, it is reasonable to approximate the class distribution of the unlabeled data from that of the labeled data according to Monte Carlo Sampling. Guided by this weakly supervision cue, we introduce a re-distribution mean teacher, which leverages adaptive label-distribution-aware confidence thresholds to generate unbiased pseudo labels to drive student learning. For the latter one, there exists an overlooked label assignment ambiguity problem across teacher-student models. To remedy this issue, we present a novel label assignment mechanism for self-training framework, namely proposal self-assignment, which injects the proposals from student into teacher and generates accurate pseudo labels to match each proposal in the student model accordingly. Experiments on both MS-COCO and PASCAL-VOC datasets demonstrate the considerable superiority of our proposed framework to other state-of-the-arts. Code will be available at https://github.com/hikvision-research/SSOD.	['Yueting Zhuang', 'Mingli Song', 'ShiLiang Pu', 'Di Xie', 'Jie Song', 'Yunyi Xuan', 'Shicai Yang', 'WeiJie Chen', 'Binbin Chen']	2022-06-14	label-matching-semi-supervised-object	http://openaccess.thecvf.com//content/CVPR2022/html/Chen_Label_Matching_Semi-Supervised_Object_Detection_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Chen_Label_Matching_Semi-Supervised_Object_Detection_CVPR_2022_paper.pdf	cvpr-2022-1	['semi-supervised-object-detection']	['computer-vision']	[ 3.27409625e-01 2.64287561e-01 -4.69416440e-01 -7.38313377e-01 -8.81652236e-01 -4.29158062e-01 5.08802652e-01 2.09233716e-01 -4.43901598e-01 8.35457981e-01 -2.41350248e-01 -1.39170498e-01 1.02336824e-01 -5.15981972e-01 -8.05484772e-01 -8.51451635e-01 7.68079221e-01 5.38971186e-01 4.93396580e-01 1.68679953e-01 1.45199075e-01 1.57298252e-01 -1.78480089e+00 3.70878167e-02 1.36890638e+00 8.80008221e-01 1.98457733e-01 2.11542636e-01 -2.16521859e-01 6.80389762e-01 -5.31277657e-01 -4.00510550e-01 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39125a67-929c-4df9-bdc3-054e4119199d	visual-spoofing-in-content-based-spam	2004.05265	null	https://arxiv.org/abs/2004.05265v2	https://arxiv.org/pdf/2004.05265v2.pdf	Visual Spoofing in content based spam detection	Although the problem of spam classification seems to be solved, there are still vulnerabilities in the current spam filters that could be easily exploited. We present one such vulnerability, in which one could replace some characters with corresponding characters from a different alphabet. These characters are visually similar, yet have a different Unicode encoding. With this approach spammers can create messages that bypass existing spam filters. Moreover, we show that this approach can be used to avoid plagiarism detection, and in other applications that use natural language processing for automatic analysis of text documents.	['Mark Sokolov', 'Nic Herndon', 'Kehinde Olufowobi']	2020-04-11	null	null	null	null	['spam-detection']	['natural-language-processing']	[ 3.20635378e-01 6.04407750e-02 4.14081067e-01 -1.47261575e-01 -1.95823431e-01 -1.02623796e+00 8.36436808e-01 3.45374227e-01 -3.79104972e-01 7.65286446e-01 9.75613147e-02 -7.99161911e-01 1.57214299e-01 -1.25276279e+00 -2.53636420e-01 -4.75036383e-01 4.00165208e-02 4.51714426e-01 9.94546294e-01 -7.34598637e-01 6.95880592e-01 7.64850497e-01 -1.48171008e+00 7.52795815e-01 7.16300189e-01 2.97019899e-01 1.13644749e-02 6.95476770e-01 -7.82149315e-01 4.98787314e-01 -1.08287144e+00 -8.52196455e-01 3.16917896e-01 -4.29385275e-01 -1.18595445e+00 5.28225787e-02 4.03881699e-01 -1.02925934e-01 -3.01170558e-01 1.55189443e+00 -5.73846884e-02 -9.58341211e-02 3.57827336e-01 -1.15010846e+00 -3.48401606e-01 4.71252888e-01 -1.26667812e-01 -9.41251889e-02 6.64429009e-01 -1.55647799e-01 4.85981226e-01 -4.85054255e-01 8.04069817e-01 1.74024081e+00 5.00790477e-01 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82d9c324-ca1f-4421-aa2c-bb1270964b5e	towards-end-to-end-car-license-plates	1709.08828	null	http://arxiv.org/abs/1709.08828v1	http://arxiv.org/pdf/1709.08828v1.pdf	Towards End-to-End Car License Plates Detection and Recognition with Deep Neural Networks	In this work, we tackle the problem of car license plate detection and recognition in natural scene images. We propose a unified deep neural network which can localize license plates and recognize the letters simultaneously in a single forward pass. The whole network can be trained end-to-end. In contrast to existing approaches which take license plate detection and recognition as two separate tasks and settle them step by step, our method jointly solves these two tasks by a single network. It not only avoids intermediate error accumulation, but also accelerates the processing speed. For performance evaluation, three datasets including images captured from various scenes under different conditions are tested. Extensive experiments show the effectiveness and efficiency of our proposed approach.	['Chunhua Shen', 'Peng Wang', 'Hui Li']	2017-09-26	null	null	null	null	['license-plate-detection']	['computer-vision']	[-4.24199067e-02 -8.48482966e-01 -5.67878969e-02 -4.46543008e-01 -8.54881048e-01 -8.75441790e-01 4.00599003e-01 -6.92363024e-01 -6.48140609e-01 3.96520525e-01 -3.99449825e-01 -3.47530782e-01 3.65448833e-01 -6.87540531e-01 -7.92674661e-01 -5.73339701e-01 7.33479500e-01 3.70734721e-01 5.53347945e-01 2.65127003e-01 6.75859272e-01 6.72548950e-01 -1.24155045e+00 2.38506511e-01 8.75877023e-01 9.64673162e-01 3.54333997e-01 6.09719634e-01 5.26028983e-02 1.06050503e+00 -4.63015944e-01 -6.24193072e-01 6.89691603e-01 6.97889477e-02 -4.83394444e-01 7.67448485e-01 4.61251646e-01 -6.24330580e-01 -7.58882344e-01 1.34444392e+00 4.28551227e-01 2.30572477e-01 5.49623132e-01 -8.43351603e-01 -8.72296095e-01 -1.09018482e-01 -7.23093808e-01 3.51085961e-01 -1.13210320e-01 2.12527379e-01 5.06886661e-01 -1.42013741e+00 2.82769620e-01 7.25042820e-01 8.42918336e-01 4.03562695e-01 -5.91235459e-01 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2.61385124e-02 4.44408089e-01 -2.94222236e-01 -6.16543405e-02 1.93230480e-01]	[9.845022201538086, -4.932728290557861]
1125f6cc-11ed-4abc-9f67-4462b352b1ae	correlated-initialization-for-correlated-data	2003.04422	null	https://arxiv.org/abs/2003.04422v2	https://arxiv.org/pdf/2003.04422v2.pdf	Correlated Initialization for Correlated Data	Spatial data exhibits the property that nearby points are correlated. This also holds for learnt representations across layers, but not for commonly used weight initialization methods. Our theoretical analysis quantifies the learning behavior of weights of a single spatial filter. It is thus in contrast to a large body of work that discusses statistical properties of weights. It shows that uncorrelated initialization (i) might lead to poor convergence behavior and (ii) training of (some) parameters is likely subject to slow convergence. Empirical analysis shows that these findings for a single spatial filter extend to networks with many spatial filters. The impact of (correlated) initialization depends strongly on learning rates and l2-regularization.	['Johannes Schneider']	2020-03-09	null	null	null	null	['l2-regularization']	['methodology']	[-2.95867957e-02 -1.14018455e-01 1.20604374e-02 -1.01416059e-01 -3.33974570e-01 -7.06618965e-01 7.33332574e-01 2.68618792e-01 -9.95642841e-01 6.02839708e-01 4.84254867e-01 -3.95925373e-01 -6.34655595e-01 -5.98295927e-01 -7.52415776e-01 -8.84357512e-01 -3.88548493e-01 1.85336974e-02 6.11357391e-01 2.96270065e-02 3.71210337e-01 6.51570439e-01 -1.37746871e+00 1.42236561e-01 5.03912270e-01 7.51982987e-01 4.64287072e-01 6.28356040e-01 6.32105544e-02 5.79357862e-01 -5.39597273e-01 -2.40137294e-01 3.63828510e-01 -2.40886182e-01 -7.58920670e-01 -2.53983825e-01 6.80271208e-01 -5.22667468e-02 -3.30220908e-01 1.17555046e+00 4.04323280e-01 5.68244219e-01 8.46548140e-01 -7.42096543e-01 -8.46113205e-01 6.03535473e-01 -5.92387319e-01 8.53521526e-01 -2.45546997e-01 1.37737438e-01 1.03280079e+00 -1.02102292e+00 4.62239861e-01 1.09527397e+00 1.10329354e+00 2.58353293e-01 -1.39904583e+00 -4.78023767e-01 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c4eda2b9-50cb-4ab3-9901-a31d5fa1b9c1	policy-tree-network	null	null	https://openreview.net/forum?id=HJlrS1rYwH	https://openreview.net/pdf?id=HJlrS1rYwH	Policy Tree Network	Decision-time planning policies with implicit dynamics models have been shown to work in discrete action spaces with Q learning. However, decision-time planning with implicit dynamics models in continuous action space has proven to be a difficult problem. Recent work in Reinforcement Learning has allowed for implicit model based approaches to be extended to Policy Gradient methods. In this work we propose Policy Tree Network (PTN). Policy Tree Network lies at the intersection of Model-Based Reinforcement Learning and Model-Free Reinforcement Learning. Policy Tree Network is a novel approach which, for the first time, demonstrates how to leverage an implicit model to perform decision-time planning with Policy Gradient methods in continuous action spaces. This work is empirically justified on 8 standard MuJoCo environments so that it can easily be compared with similar work done in this area. Additionally, we offer a lower bound on the worst case change in the mean of the policy when tree planning is used and theoretically justify our design choices.	['James Kwok', 'Sepanta Zeighami', 'Zac Wellmer']	2019-09-25	null	null	null	null	['policy-gradient-methods']	['methodology']	[ 9.87280235e-02 4.20579106e-01 -6.03747427e-01 3.34538445e-02 -5.37052214e-01 -3.41364354e-01 8.29566419e-01 1.46295717e-02 -7.43441403e-01 1.20613599e+00 2.17310503e-01 -7.96161473e-01 -5.37916541e-01 -6.96335137e-01 -3.58864427e-01 -6.02187753e-01 -6.91565454e-01 5.83776772e-01 3.33606124e-01 -4.89080995e-01 5.67365766e-01 3.70196819e-01 -1.14027488e+00 -1.12604223e-01 6.93126023e-01 6.06175184e-01 3.33894119e-02 9.37890828e-01 7.00592473e-02 1.24154711e+00 -4.44394201e-01 3.45697016e-01 5.95814168e-01 -5.52341342e-01 -9.23059225e-01 -1.18686616e-01 -2.96053942e-02 -7.80026734e-01 -1.87569737e-01 6.11372709e-01 6.57951176e-01 5.52976489e-01 5.04665792e-01 -1.41651666e+00 2.35821605e-01 4.46395606e-01 -2.62230515e-01 2.05692410e-01 2.96086788e-01 7.56871164e-01 7.66488254e-01 -1.54557288e-01 6.25030279e-01 1.59035552e+00 4.37638372e-01 6.78832471e-01 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-7.74425805e-01 -6.00476921e-01 -2.59841233e-02 3.80625337e-01 -1.04396212e+00 4.29945290e-02 -5.76653421e-01]	[4.139034271240234, 2.0785841941833496]
b218b831-5d85-41fb-8f7e-99a68b07da1f	query-centric-trajectory-prediction	null	null	http://openaccess.thecvf.com//content/CVPR2023/html/Zhou_Query-Centric_Trajectory_Prediction_CVPR_2023_paper.html	http://openaccess.thecvf.com//content/CVPR2023/papers/Zhou_Query-Centric_Trajectory_Prediction_CVPR_2023_paper.pdf	Query-Centric Trajectory Prediction	Predicting the future trajectories of surrounding agents is essential for autonomous vehicles to operate safely. This paper presents QCNet, a modeling framework toward pushing the boundaries of trajectory prediction. First, we identify that the agent-centric modeling scheme used by existing approaches requires re-normalizing and re-encoding the input whenever the observation window slides forward, leading to redundant computations during online prediction. To overcome this limitation and achieve faster inference, we introduce a query-centric paradigm for scene encoding, which enables the reuse of past computations by learning representations independent of the global spacetime coordinate system. Sharing the invariant scene features among all target agents further allows the parallelism of multi-agent trajectory decoding. Second, even given rich encodings of the scene, existing decoding strategies struggle to capture the multimodality inherent in agents' future behavior, especially when the prediction horizon is long. To tackle this challenge, we first employ anchor-free queries to generate trajectory proposals in a recurrent fashion, which allows the model to utilize different scene contexts when decoding waypoints at different horizons. A refinement module then takes the trajectory proposals as anchors and leverages anchor-based queries to refine the trajectories further. By supplying adaptive and high-quality anchors to the refinement module, our query-based decoder can better deal with the multimodality in the output of trajectory prediction. Our approach ranks 1st on Argoverse 1 and Argoverse 2 motion forecasting benchmarks, outperforming all methods on all main metrics by a large margin. Meanwhile, our model can achieve streaming scene encoding and parallel multi-agent decoding thanks to the query-centric design ethos.	['Yu-Kai Huang', 'Yung-Hui Li', 'JianPing Wang', 'Zikang Zhou']	2023-01-01	null	null	null	cvpr-2023-1	['motion-prediction', 'trajectory-prediction', 'self-driving-cars', 'motion-forecasting']	['computer-vision', 'computer-vision', 'computer-vision', 'computer-vision']	[-2.22817600e-01 7.97498077e-02 -4.68326241e-01 -1.79988772e-01 -8.52561295e-01 -6.47546291e-01 9.36747551e-01 2.78529793e-01 -3.06895643e-01 4.59592611e-01 4.54165101e-01 -3.32696825e-01 1.38896313e-02 -1.00035942e+00 -7.99416184e-01 -6.05811894e-01 -5.02580285e-01 5.14438868e-01 5.58512807e-01 -3.40583473e-01 2.30614722e-01 6.37466550e-01 -1.70141125e+00 2.50460267e-01 6.49016380e-01 8.41194272e-01 4.01448250e-01 9.17533398e-01 -2.95642056e-02 9.69301760e-01 -1.04937099e-01 -3.97344261e-01 3.61567527e-01 -3.91672142e-02 -6.35376275e-01 -1.26073256e-01 2.35512376e-01 -7.82778203e-01 -1.01498699e+00 7.49962807e-01 9.71722007e-02 3.24298292e-01 4.73844677e-01 -1.41169143e+00 -9.89985913e-02 4.25625443e-01 -5.75576462e-02 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8.41952413e-02 4.13233101e-01 1.09072673e+00 8.78629208e-01 2.66604871e-01 6.51240230e-01 -1.20532644e+00 -3.11928183e-01 -3.70178640e-01 -5.84605289e-07 1.41533390e-01 8.32878351e-01 -6.28567636e-01 1.02348877e-02 -9.09198076e-03]	[5.878512859344482, 0.791012167930603]
3f0f2455-d459-41ea-a120-9c6ad272ea8a	automated-essay-scoring-with-string-kernels	1804.07954	null	http://arxiv.org/abs/1804.07954v2	http://arxiv.org/pdf/1804.07954v2.pdf	Automated essay scoring with string kernels and word embeddings	In this work, we present an approach based on combining string kernels and word embeddings for automatic essay scoring. String kernels capture the similarity among strings based on counting common character n-grams, which are a low-level yet powerful type of feature, demonstrating state-of-the-art results in various text classification tasks such as Arabic dialect identification or native language identification. To our best knowledge, we are the first to apply string kernels to automatically score essays. We are also the first to combine them with a high-level semantic feature representation, namely the bag-of-super-word-embeddings. We report the best performance on the Automated Student Assessment Prize data set, in both in-domain and cross-domain settings, surpassing recent state-of-the-art deep learning approaches.	['Radu Tudor Ionescu', 'Mădălina Cozma', 'Andrei M. Butnaru']	2018-04-21	automated-essay-scoring-with-string-kernels-1	https://aclanthology.org/P18-2080	https://aclanthology.org/P18-2080.pdf	acl-2018-7	['automated-essay-scoring', 'native-language-identification']	['natural-language-processing', 'natural-language-processing']	[-4.75949273e-02 -3.43610466e-01 -3.58748436e-01 -3.16026628e-01 -8.74905825e-01 -9.98472452e-01 5.65467656e-01 9.19734120e-01 -8.42117786e-01 2.75981635e-01 2.71907806e-01 -4.13365722e-01 -2.63890982e-01 -1.00598705e+00 -1.02800205e-01 -2.51506954e-01 2.02701271e-01 5.59263945e-01 2.13947535e-01 -3.08823496e-01 7.90426314e-01 2.41413459e-01 -1.42834938e+00 2.76292562e-01 9.76498485e-01 7.08814323e-01 -3.82968187e-01 7.81619370e-01 -5.39521277e-01 5.79550743e-01 -4.35889035e-01 -1.08452153e+00 -1.96202248e-01 -2.01032549e-01 -9.21662688e-01 -5.27724922e-01 9.03544366e-01 -1.81091547e-01 -3.52643281e-01 1.21733415e+00 5.17651439e-01 1.64542601e-01 1.06879401e+00 -5.41945457e-01 -1.42946649e+00 7.26046860e-01 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b84fdaef-e39e-4b5d-ae53-292677ca3698	consistency-by-agreement-in-zero-shot-neural	1904.02338	null	http://arxiv.org/abs/1904.02338v2	http://arxiv.org/pdf/1904.02338v2.pdf	Consistency by Agreement in Zero-shot Neural Machine Translation	Generalization and reliability of multilingual translation often highly depend on the amount of available parallel data for each language pair of interest. In this paper, we focus on zero-shot generalization---a challenging setup that tests models on translation directions they have not been optimized for at training time. To solve the problem, we (i) reformulate multilingual translation as probabilistic inference, (ii) define the notion of zero-shot consistency and show why standard training often results in models unsuitable for zero-shot tasks, and (iii) introduce a consistent agreement-based training method that encourages the model to produce equivalent translations of parallel sentences in auxiliary languages. We test our multilingual NMT models on multiple public zero-shot translation benchmarks (IWSLT17, UN corpus, Europarl) and show that agreement-based learning often results in 2-3 BLEU zero-shot improvement over strong baselines without any loss in performance on supervised translation directions.	['Maruan Al-Shedivat', 'Ankur P. Parikh']	2019-04-04	consistency-by-agreement-in-zero-shot-neural-1	https://aclanthology.org/N19-1121	https://aclanthology.org/N19-1121.pdf	naacl-2019-6	['zero-shot-machine-translation']	['natural-language-processing']	[ 1.41066954e-01 6.27864897e-02 -6.23760939e-01 -4.00498927e-01 -1.97723138e+00 -7.87813902e-01 9.25054669e-01 -3.22809778e-02 -2.70254910e-01 1.18706691e+00 3.70909393e-01 -8.93267393e-01 3.20607603e-01 -3.59100908e-01 -1.09531474e+00 -5.13750553e-01 2.28148460e-01 1.19791150e+00 -1.20729715e-01 -6.22321069e-01 -1.12787940e-01 -1.85841277e-01 -7.93817222e-01 4.34597343e-01 1.15784574e+00 2.50186652e-01 9.45579931e-02 5.33252597e-01 3.63184586e-02 1.30306825e-01 -4.41490263e-01 -7.75846958e-01 4.27795559e-01 -7.68847704e-01 -1.01150739e+00 -5.89265078e-02 5.37221909e-01 1.08986743e-01 7.29382187e-02 1.18104613e+00 7.13148534e-01 -1.49355948e-01 7.59838998e-01 -1.06214178e+00 -7.76975155e-01 1.18540311e+00 -4.69748765e-01 2.70503551e-01 3.21134657e-01 3.37693334e-01 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56ccb151-73a9-4a6b-82b0-279aeea5c0b4	neuromorphic-event-based-facial-expression	2304.06351	null	https://arxiv.org/abs/2304.06351v1	https://arxiv.org/pdf/2304.06351v1.pdf	Neuromorphic Event-based Facial Expression Recognition	Recently, event cameras have shown large applicability in several computer vision fields especially concerning tasks that require high temporal resolution. In this work, we investigate the usage of such kind of data for emotion recognition by presenting NEFER, a dataset for Neuromorphic Event-based Facial Expression Recognition. NEFER is composed of paired RGB and event videos representing human faces labeled with the respective emotions and also annotated with face bounding boxes and facial landmarks. We detail the data acquisition process as well as providing a baseline method for RGB and event data. The collected data captures subtle micro-expressions, which are hard to spot with RGB data, yet emerge in the event domain. We report a double recognition accuracy for the event-based approach, proving the effectiveness of a neuromorphic approach for analyzing fast and hardly detectable expressions and the emotions they conceal.	['Alberto del Bimbo', 'Federico Becattini', 'Sara Picchioni', 'Andrea Leonardo', 'Lisa Cresti', 'Chiara Albisani', 'Luca Cultrera', 'Lorenzo Berlincioni']	2023-04-13	null	null	null	null	['facial-expression-recognition']	['computer-vision']	[ 4.03119385e-01 -5.66375963e-02 3.76353860e-01 -7.02812195e-01 -2.91139454e-01 -4.54972327e-01 6.45111620e-01 4.67902236e-02 -7.89366961e-01 6.77405298e-01 -1.90610141e-01 6.00292206e-01 3.17844818e-03 -4.09840852e-01 -7.84798026e-01 -9.94034946e-01 -2.19806716e-01 1.42928526e-01 -6.71187565e-02 -8.81663635e-02 -1.24736913e-01 9.03829336e-01 -2.31251407e+00 5.77229500e-01 -1.75941631e-01 1.73971462e+00 -3.60859185e-01 5.60303867e-01 3.48508693e-02 6.12706900e-01 -6.58898175e-01 -6.26408100e-01 6.62207976e-02 -5.64793646e-01 -2.32320905e-01 -1.34734124e-01 4.95668471e-01 -2.49119639e-01 -2.83557743e-01 9.79999185e-01 6.54585183e-01 -2.23763064e-01 4.89398718e-01 -1.69732761e+00 4.02381644e-02 5.25930524e-02 -3.65708619e-01 3.80615056e-01 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e416be0e-d677-436e-9a89-6af9f7543844	a-recurrent-and-compositional-model-for	null	null	https://aclanthology.org/W16-4303	https://aclanthology.org/W16-4303.pdf	A Recurrent and Compositional Model for Personality Trait Recognition from Short Texts	Many methods have been used to recognise author personality traits from text, typically combining linguistic feature engineering with shallow learning models, e.g. linear regression or Support Vector Machines. This work uses deep-learning-based models and atomic features of text, the characters, to build hierarchical, vectorial word and sentence representations for trait inference. This method, applied to a corpus of tweets, shows state-of-the-art performance across five traits compared with prior work. The results, supported by preliminary visualisation work, are encouraging for the ability to detect complex human traits.	['Fei Liu', 'Julien Perez', 'Scott Nowson']	2016-12-01	null	null	null	ws-2016-12	['personality-trait-recognition']	['computer-vision']	[-1.72608614e-01 2.69223213e-01 -1.05527312e-01 -7.60765254e-01 -2.91698396e-01 -2.11603194e-01 1.02271581e+00 6.49120748e-01 -3.89910787e-01 4.79453713e-01 6.94632828e-01 -1.83690608e-01 -3.21284354e-01 -7.08933175e-01 2.13711619e-01 -1.73347369e-01 -2.83041835e-01 5.94895720e-01 -2.87307739e-01 -4.07646835e-01 8.59904587e-01 3.50072652e-01 -1.64924347e+00 5.23212492e-01 4.82050478e-01 9.46017087e-01 -4.81682748e-01 1.03625858e+00 -5.73295772e-01 8.73958230e-01 -6.74976528e-01 -6.71551704e-01 -4.29658383e-01 1.61764190e-01 -9.19089139e-01 7.13790059e-02 6.41136885e-01 3.18510011e-02 2.91013476e-02 6.41732931e-01 7.00212359e-01 -1.24379866e-01 9.32054043e-01 -7.98247874e-01 -1.04854405e+00 5.67307293e-01 -4.52756375e-01 2.00186461e-01 9.29532468e-01 -1.43730760e-01 1.27190852e+00 -7.68498898e-01 2.21643165e-01 1.55939651e+00 1.30654454e+00 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983785d6-11bd-4195-ae7e-6294c4b55eb7	stasis-net-a-stacked-and-siamese-stereo	null	null	https://doi.org/10.1016/j.media.2022.102380	https://www.sciencedirect.com/science/article/pii/S1361841522000329	StaSiS-Net: a stacked and siamese stereo network for depth reconstruction in modern 3D laparoscopy.	Accurate and real-time methodologies for a non-invasive three-dimensional representa-tion and reconstruction of internal patient structures is one of the main research fieldsin computer-assisted surgery and endoscopy. Mono and stereo endoscopic images ofsoft tissues are converted into a three-dimensional representation by the estimation ofdepth maps. However, automatic, detailed, accurate and robust depth map estimationis a challenging problem which, moreover, is strictly dependent on a robust estimateof the disparity map. Many traditional algorithms are often inefficient or not accu-rate. In this work, novel self-supervised stacked and Siamese encoder/decoder neuralnetworks are proposed to compute accurate disparity maps for 3D laparoscopy depthreconstructions. These networks produce disparities in real-time on standard GPU-equipped desktop computers and after, with a minimal parameter configuration theirdepth reconstruction. We compare their performance on three different public datasetsand on a new challenging simulated dataset and they outperform state-of-the-art monoand stereo depth estimation methods. Extensive robustness and sensitivity analyses onmore than 30 000 frames has been performed. This work leads to important improve-ments in mono and stereo real-time depth estimations of soft tissues and organs with avery low average mean absolute disparity reconstruction error with respect to groundtruth.	['Roberto Tagliaferri', 'Alexandre Hostettler', 'Antonello Forgione', 'Toby Collins', 'Francesco Bardozzo']	2022-04-06	null	null	null	medical-image-analysis-2022-4	['stereo-depth-estimation']	['computer-vision']	[ 6.83233216e-02 1.10001549e-01 -7.13888705e-02 -3.25905681e-01 -9.01019990e-01 -3.89472604e-01 3.25442463e-01 4.18397516e-01 -8.14500332e-01 7.27099240e-01 -4.32243012e-03 -6.18926845e-02 -7.88270012e-02 -7.60420501e-01 -7.97678232e-01 -7.21970320e-01 -7.89154768e-02 6.45667970e-01 6.36145025e-02 -1.03474602e-01 1.45861968e-01 6.50889933e-01 -1.63788271e+00 1.78365171e-01 7.77835906e-01 1.12151396e+00 1.61589697e-01 9.85880673e-01 2.32718721e-01 3.90078217e-01 -8.02676976e-02 -2.35353619e-01 4.19243902e-01 -3.55768889e-01 -6.45591319e-01 1.38612986e-01 6.07596993e-01 -6.46980047e-01 -1.82877660e-01 1.12047684e+00 7.31337905e-01 -2.34794065e-01 5.79375267e-01 -3.88743371e-01 2.91026235e-01 1.54585794e-01 -5.51790535e-01 1.72582597e-01 4.72492337e-01 -7.55230412e-02 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398c6c9d-ccfa-48ea-b44e-a75ccd759acc	a-transformer-based-approach-towards	null	null	https://aclanthology.org/2021.disrpt-1.2	https://aclanthology.org/2021.disrpt-1.2.pdf	A Transformer Based Approach towards Identification of Discourse Unit Segments and Connectives	Discourse parsing, which involves understanding the structure, information flow, and modeling the coherence of a given text, is an important task in natural language processing. It forms the basis of several natural language processing tasks such as question-answering, text summarization, and sentiment analysis. Discourse unit segmentation is one of the fundamental tasks in discourse parsing and refers to identifying the elementary units of text that combine to form a coherent text. In this paper, we present a transformer based approach towards the automated identification of discourse unit segments and connectives. Early approaches towards segmentation relied on rule-based systems using POS tags and other syntactic information to identify discourse segments. Recently, transformer based neural systems have shown promising results in this domain. Our system, SegFormers, employs this transformer based approach to perform multilingual discourse segmentation and connective identification across 16 datasets encompassing 11 languages and 3 different annotation frameworks. We evaluate the system based on F1 scores for both tasks, with the best system reporting the highest F1 score of 97.02% for the treebanked English RST-DT dataset.	['Dipti Sharma', 'Sahil Bakshi']	null	null	null	null	emnlp-disrpt-2021-11	['discourse-segmentation', 'discourse-parsing']	['natural-language-processing', 'natural-language-processing']	[ 4.82289642e-01 7.11447895e-01 -2.42929295e-01 -4.17905807e-01 -8.99844170e-01 -8.76318455e-01 9.36063290e-01 8.17016900e-01 -3.75172287e-01 7.20697284e-01 9.14412379e-01 -5.55112064e-01 2.13644877e-01 -4.72244114e-01 -3.17622840e-01 -3.03016216e-01 7.48881400e-02 6.41676486e-01 5.68056166e-01 -4.34455246e-01 7.00956404e-01 -2.36284792e-01 -9.46905255e-01 1.01345801e+00 1.09805429e+00 6.49663270e-01 2.15859368e-01 8.52854550e-01 -8.26847970e-01 1.42565680e+00 -9.57974792e-01 -3.79630715e-01 -5.19980609e-01 -7.72768795e-01 -1.55896842e+00 -9.55783278e-02 7.78203830e-02 1.41063318e-01 3.43717933e-01 8.12534153e-01 3.57281454e-02 -1.07518084e-01 6.21731341e-01 -4.42879617e-01 -1.22603163e-01 1.36388564e+00 -8.25661048e-02 4.50476527e-01 7.14631259e-01 -5.58179438e-01 1.49115896e+00 -4.93661612e-01 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ddaa9178-64fc-40fd-911c-6f8a4636a4b2	non-monotonic-parsing-of-fluent-umm-i-mean	null	null	https://aclanthology.org/E14-4010	https://aclanthology.org/E14-4010.pdf	Non-Monotonic Parsing of Fluent Umm I mean Disfluent Sentences	null	['Joel Tetreault', 'Mohammad Sadegh Rasooli']	2014-04-01	null	null	null	eacl-2014-4	['transition-based-dependency-parsing']	['natural-language-processing']	[-8.63703638e-02 1.71006292e-01 -6.22772932e-01 -4.08054382e-01 -8.41685571e-03 -9.08429027e-01 6.55310392e-01 -6.53472245e-01 -2.85945535e-01 1.06888819e+00 -4.63127941e-02 -1.01159286e+00 -3.91567826e-01 -9.63214397e-01 -4.95059669e-01 -6.31337762e-01 -9.79754329e-01 7.25764990e-01 3.30370307e-01 -6.93831444e-01 7.03166842e-01 7.88774848e-01 -1.68942046e+00 7.18545914e-01 7.04417467e-01 8.52217197e-01 2.49141872e-01 1.14950800e+00 -1.95044339e-01 1.55633950e+00 -7.48382092e-01 -5.46825826e-01 3.13719302e-01 -1.23176083e-01 -7.22945035e-01 -1.01074085e-01 9.28529128e-02 -8.59008506e-02 -2.09758401e-01 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b1d4d438-5361-493a-a29b-c1e1953918c7	micse-mutual-information-contrastive-learning	2211.04928	null	https://arxiv.org/abs/2211.04928v2	https://arxiv.org/pdf/2211.04928v2.pdf	miCSE: Mutual Information Contrastive Learning for Low-shot Sentence Embeddings	This paper presents miCSE, a mutual information-based contrastive learning framework that significantly advances the state-of-the-art in few-shot sentence embedding. The proposed approach imposes alignment between the attention pattern of different views during contrastive learning. Learning sentence embeddings with miCSE entails enforcing the structural consistency across augmented views for every sentence, making contrastive self-supervised learning more sample efficient. As a result, the proposed approach shows strong performance in the few-shot learning domain. While it achieves superior results compared to state-of-the-art methods on multiple benchmarks in few-shot learning, it is comparable in the full-shot scenario. This study opens up avenues for efficient self-supervised learning methods that are more robust than current contrastive methods for sentence embedding.	['Moin Nabi', 'Tassilo Klein']	2022-11-09	null	null	null	null	['sentence-embeddings', 'sentence-embeddings']	['methodology', 'natural-language-processing']	[ 2.23877281e-01 7.19151041e-03 -4.51450050e-01 -4.52745527e-01 -8.45631778e-01 -3.77024105e-03 9.43172276e-01 5.35286546e-01 -7.03129828e-01 4.30880219e-01 7.01480329e-01 9.67615098e-02 -9.99765322e-02 -7.93696284e-01 -4.13214117e-01 -6.43039823e-01 -5.49611449e-02 1.91812366e-01 2.69532114e-01 -3.96005481e-01 4.39038366e-01 3.91971916e-02 -1.72472739e+00 3.08168292e-01 4.21225250e-01 3.15049469e-01 2.41943687e-01 6.41368270e-01 -4.02756512e-01 8.50580037e-01 -3.68767768e-01 -3.91724110e-01 -1.15232766e-01 -6.75921321e-01 -5.91403842e-01 -7.00292736e-02 9.34284389e-01 -1.39737114e-01 -5.71201146e-01 9.92820084e-01 7.32246995e-01 4.96794641e-01 7.73079276e-01 -9.63537395e-01 -1.19901216e+00 4.27495569e-01 -5.16060531e-01 7.15465188e-01 3.72151852e-01 4.21368964e-02 1.57026517e+00 -1.29970002e+00 8.03179741e-01 1.06596029e+00 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8a1a621b-bc40-4855-bc47-25fb076659f5	answer-uncertainty-and-unanswerability-in-1	null	null	https://aclanthology.org/2022.findings-acl.82	https://aclanthology.org/2022.findings-acl.82.pdf	Answer Uncertainty and Unanswerability in Multiple-Choice Machine Reading Comprehension	Machine reading comprehension (MRC) has drawn a lot of attention as an approach for assessing the ability of systems to understand natural language. Usually systems focus on selecting the correct answer to a question given a contextual paragraph. However, for many applications of multiple-choice MRC systems there are two additional considerations. For multiple-choice exams there is often a negative marking scheme; there is a penalty for an incorrect answer. In terms of an MRC system this means that the system is required to have an idea of the uncertainty in the predicted answer. The second consideration is that many multiple-choice questions have the option of none-of-the-above (NOA) indicating that none of the answers is applicable, rather than there always being the correct answer in the list of choices. This paper investigates both of these issues by making use of predictive uncertainty. Whether the system should propose an answer is a direct application of answer uncertainty. There are two possibilities when considering the NOA option. The simplest is to explicitly build a system on data that includes this option. Alternatively uncertainty can be applied to detect whether the other options include the correct answer. If the system is not sufficiently confident it will select NOA. As there is no standard corpus available to investigate these topics, the ReClor corpus is modified by removing the correct answer from a subset of possible answers. A high-performance MRC system is used to evaluate whether answer uncertainty can be applied in these situations. It is shown that uncertainty does allow questions that the system is not confident about to be detected. Additionally it is shown that uncertainty outperforms a system explicitly built with an NOA option.	['Mark Gales', 'Vatsal Raina']	null	null	null	null	findings-acl-2022-5	['machine-reading-comprehension']	['natural-language-processing']	[ 4.22033995e-01 5.62754750e-01 1.23819225e-02 -7.50325203e-01 -9.74315047e-01 -6.56802535e-01 5.25519907e-01 6.59786522e-01 -4.97487485e-01 9.20680225e-01 1.17706172e-01 -1.16710138e+00 -4.93421167e-01 -8.14133644e-01 -5.33199787e-01 -2.30431482e-01 5.18632054e-01 6.95869684e-01 6.55109584e-01 -1.91280022e-01 5.85959971e-01 2.25261390e-01 -1.71061647e+00 4.12239760e-01 1.01594329e+00 6.14302695e-01 2.62726814e-01 7.79981375e-01 -5.96394837e-01 7.57478595e-01 -8.82786572e-01 -1.57250598e-01 3.26786190e-03 -6.52904153e-01 -1.36178541e+00 -2.63746262e-01 3.48423719e-01 -2.44373187e-01 4.74420488e-01 1.02251828e+00 2.25100175e-01 2.84457803e-01 7.46818185e-01 -1.03957820e+00 9.45538878e-02 5.28591812e-01 5.59745766e-02 4.29635227e-01 1.10701108e+00 -1.15503326e-01 8.58011127e-01 -5.18749297e-01 3.38876486e-01 1.32311952e+00 1.66769817e-01 3.01196694e-01 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5e991be2-d97b-4f95-8235-107ebd4cfcd8	boosting-entity-aware-image-captioning-with	2107.11970	null	https://arxiv.org/abs/2107.11970v1	https://arxiv.org/pdf/2107.11970v1.pdf	Boosting Entity-aware Image Captioning with Multi-modal Knowledge Graph	Entity-aware image captioning aims to describe named entities and events related to the image by utilizing the background knowledge in the associated article. This task remains challenging as it is difficult to learn the association between named entities and visual cues due to the long-tail distribution of named entities. Furthermore, the complexity of the article brings difficulty in extracting fine-grained relationships between entities to generate informative event descriptions about the image. To tackle these challenges, we propose a novel approach that constructs a multi-modal knowledge graph to associate the visual objects with named entities and capture the relationship between entities simultaneously with the help of external knowledge collected from the web. Specifically, we build a text sub-graph by extracting named entities and their relationships from the article, and build an image sub-graph by detecting the objects in the image. To connect these two sub-graphs, we propose a cross-modal entity matching module trained using a knowledge base that contains Wikipedia entries and the corresponding images. Finally, the multi-modal knowledge graph is integrated into the captioning model via a graph attention mechanism. Extensive experiments on both GoodNews and NYTimes800k datasets demonstrate the effectiveness of our method.	['Jiebo Luo', 'Xinxiao wu', 'HeDa Wang', 'Yao Hu', 'Wentian Zhao']	2021-07-26	null	null	null	null	['multi-modal-knowledge-graph']	['knowledge-base']	[-1.56118020e-01 2.55213708e-01 -1.24786332e-01 -2.60087758e-01 -6.68487906e-01 -7.02797949e-01 5.65861583e-01 3.04950655e-01 -4.95253235e-01 5.53807497e-01 4.54673022e-01 9.48983133e-02 1.01901673e-01 -9.38046396e-01 -1.07526159e+00 -3.79896522e-01 2.25513428e-01 2.45781228e-01 4.41262245e-01 4.21050042e-02 2.94765327e-02 2.08520725e-01 -1.43141365e+00 3.95513684e-01 8.72401357e-01 9.81299400e-01 4.54282433e-01 3.22672129e-01 -5.85195959e-01 9.96807516e-01 -3.58180583e-01 -9.18751895e-01 -3.29301059e-02 -2.59504795e-01 -6.51684701e-01 5.49630523e-01 4.86496389e-01 -1.90519884e-01 -6.82819307e-01 1.12478113e+00 2.90447503e-01 1.80848554e-01 5.84617794e-01 -1.53598225e+00 -1.12988925e+00 5.39329529e-01 -7.55982399e-01 2.74243563e-01 3.03769022e-01 1.22866668e-01 1.21968579e+00 -1.07098329e+00 1.07534695e+00 1.22334039e+00 2.47833803e-01 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e246092e-1c4e-4e1f-bfeb-02149308bddb	latent-dirichlet-allocation-in-generative	1812.06571	null	https://arxiv.org/abs/1812.06571v5	https://arxiv.org/pdf/1812.06571v5.pdf	Latent Dirichlet Allocation in Generative Adversarial Networks	We study the problem of multimodal generative modelling of images based on generative adversarial networks (GANs). Despite the success of existing methods, they often ignore the underlying structure of vision data or its multimodal generation characteristics. To address this problem, we introduce the Dirichlet prior for multimodal image generation, which leads to a new Latent Dirichlet Allocation based GAN (LDAGAN). In detail, for the generative process modelling, LDAGAN defines a generative mode for each sample, determining which generative sub-process it belongs to. For the adversarial training, LDAGAN derives a variational expectation-maximization (VEM) algorithm to estimate model parameters. Experimental results on real-world datasets have demonstrated the outstanding performance of LDAGAN over other existing GANs.	['Jian Liu', 'Zenglin Xu', 'Yazhou Ren', 'Shen Cheng', 'Lili Pan']	2018-12-17	null	null	null	null	['multimodal-generation']	['natural-language-processing']	[ 2.75346994e-01 2.73418695e-01 1.88097760e-01 -2.27143869e-01 -7.43686199e-01 -3.83256346e-01 9.78705883e-01 -1.04395461e+00 3.01735997e-01 7.78317094e-01 3.41877103e-01 -1.60354823e-01 3.91184747e-01 -1.12118280e+00 -5.88785350e-01 -1.24349833e+00 6.93034053e-01 8.65782857e-01 -4.89069283e-01 1.59305885e-01 -1.95557162e-01 1.16580665e-01 -9.55928326e-01 7.64955729e-02 8.30318689e-01 6.51001036e-01 1.08194798e-01 7.33193815e-01 -2.57765979e-01 7.31289625e-01 -7.61835933e-01 -8.13112795e-01 7.22826347e-02 -1.04465127e+00 -4.70904678e-01 6.52874708e-01 -1.02842733e-01 -6.14606321e-01 -4.52554584e-01 1.17490053e+00 6.03340328e-01 -1.60715610e-01 1.33773351e+00 -1.76393056e+00 -1.18291867e+00 3.94302934e-01 -4.76380736e-01 -4.72481221e-01 -3.67672220e-02 2.85945922e-01 5.16769588e-01 -8.79069507e-01 5.60265839e-01 1.56370473e+00 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27ff165d-5d36-481d-b39c-56316e3ab11f	a-robust-scientific-machine-learning-for	2209.06642	null	https://arxiv.org/abs/2209.06642v1	https://arxiv.org/pdf/2209.06642v1.pdf	A Robust Scientific Machine Learning for Optimization: A Novel Robustness Theorem	Scientific machine learning (SciML) is a field of increasing interest in several different application fields. In an optimization context, SciML-based tools have enabled the development of more efficient optimization methods. However, implementing SciML tools for optimization must be rigorously evaluated and performed with caution. This work proposes the deductions of a robustness test that guarantees the robustness of multiobjective SciML-based optimization by showing that its results respect the universal approximator theorem. The test is applied in the framework of a novel methodology which is evaluated in a series of benchmarks illustrating its consistency. Moreover, the proposed methodology results are compared with feasible regions of rigorous optimization, which requires a significantly higher computational effort. Hence, this work provides a robustness test for guaranteed robustness in applying SciML tools in multiobjective optimization with lower computational effort than the existent alternative.	['Idelfonso B. R. Nogueira', 'Ana M. Ribeiro', 'Alirio E. Rodrigues', 'Vinicius V. Santana', 'Erber A. Costa', 'Carine M. Rebello', 'Luana P. Queiroz']	2022-09-13	null	null	null	null	['multiobjective-optimization']	['methodology']	[ 1.54615104e-01 1.03135087e-01 -1.77761480e-01 -1.99602529e-01 -4.07759815e-01 -4.11570787e-01 5.91763914e-01 6.38251126e-01 -4.63918597e-01 1.10185707e+00 -3.95539701e-01 -3.87610346e-01 -9.08631265e-01 -7.01606989e-01 -6.27681136e-01 -5.23802102e-01 5.42122424e-02 3.55213612e-01 -2.92902589e-01 -3.39687876e-02 4.53790218e-01 8.55685830e-01 -1.88765204e+00 -2.45897904e-01 1.26812935e+00 8.15453053e-01 9.32758301e-02 4.78397429e-01 -1.03678361e-01 3.24709445e-01 -7.19545841e-01 -3.52942556e-01 2.70068467e-01 -6.31715596e-01 -7.38943994e-01 1.79870576e-02 1.41736716e-01 2.68777430e-01 5.69640040e-01 9.82250392e-01 3.68396610e-01 3.76132250e-01 7.32415855e-01 -1.36515594e+00 -2.18116164e-01 4.02626097e-01 -2.20925035e-03 -2.83476889e-01 5.55143058e-01 1.14992343e-01 8.70802820e-01 -4.27140653e-01 5.66792190e-01 9.51818466e-01 3.11277121e-01 8.01068842e-02 -1.09366655e+00 -1.20873943e-01 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c3268da1-ea70-4e52-bdac-9d035178f82e	improving-embedded-knowledge-graph-multi-hop	2110.12679	null	https://arxiv.org/abs/2110.12679v3	https://arxiv.org/pdf/2110.12679v3.pdf	Improving Embedded Knowledge Graph Multi-hop Question Answering by introducing Relational Chain Reasoning	Knowledge Graph Question Answering (KGQA) aims to answer user-questions from a knowledge graph (KG) by identifying the reasoning relations between topic entity and answer. As a complex branch task of KGQA, multi-hop KGQA requires reasoning over the multi-hop relational chain preserved in KG to arrive at the right answer. Despite recent successes, the existing works on answering multi-hop complex questions still face the following challenges: i) The absence of an explicit relational chain order reflected in user-question stems from a misunderstanding of a user's intentions. ii) Incorrectly capturing relational types on weak supervision of which dataset lacks intermediate reasoning chain annotations due to expensive labeling cost. iii) Failing to consider implicit relations between the topic entity and the answer implied in structured KG because of limited neighborhoods size constraint in subgraph retrieval-based algorithms.To address these issues in multi-hop KGQA, we propose a novel model herein, namely Relational Chain based Embedded KGQA (Rce-KGQA), which simultaneously utilizes the explicit relational chain revealed in natural language question and the implicit relational chain stored in structured KG. Our extensive empirical study on three open-domain benchmarks proves that our method significantly outperforms the state-of-the-art counterparts like GraftNet, PullNet and EmbedKGQA. Comprehensive ablation experiments also verify the effectiveness of our method on the multi-hop KGQA task. We have made our model's source code available at github: https://github.com/albert-jin/Rce-KGQA.	['Guizhong Liu', 'Hang Yu', 'Biao Zhao', 'Ruiping Yin', 'Xi Tao', 'Weiqiang Jin']	2021-10-25	null	null	null	null	['graph-question-answering', 'multi-hop-question-answering', 'knowledge-base-question-answering', 'implicit-relations']	['graphs', 'knowledge-base', 'natural-language-processing', 'natural-language-processing']	[-1.92345828e-01 8.87212157e-01 -3.82478535e-01 -1.80822849e-01 -9.23300445e-01 -6.03091896e-01 3.40616912e-01 3.78402859e-01 7.22062662e-02 6.39770806e-01 3.93332362e-01 -6.32377028e-01 -4.76216078e-01 -1.28483748e+00 -9.44169044e-01 -5.22957072e-02 1.35701656e-01 8.08647037e-01 8.05611074e-01 -4.96160865e-01 -1.07672557e-01 -8.41699094e-02 -1.16060901e+00 5.19703031e-01 1.09714401e+00 8.89286935e-01 -1.43838525e-01 4.17410553e-01 -3.35548431e-01 1.43767321e+00 -6.82892799e-02 -1.01065183e+00 2.23692641e-01 -3.47090691e-01 -1.69705534e+00 -2.45681718e-01 8.21500957e-01 -2.12967396e-01 -4.80999529e-01 1.03020346e+00 1.14324532e-01 -9.83882099e-02 3.29869002e-01 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db1b75c0-191f-4c54-805c-ff58ba12c44d	encoding-feature-supervised-unet-redesigning	2211.08146	null	https://arxiv.org/abs/2211.08146v1	https://arxiv.org/pdf/2211.08146v1.pdf	Encoding feature supervised UNet++: Redesigning Supervision for liver and tumor segmentation	Liver tumor segmentation in CT images is a critical step in the diagnosis, surgical planning and postoperative evaluation of liver disease. An automatic liver and tumor segmentation method can greatly relieve physicians of the heavy workload of examining CT images and better improve the accuracy of diagnosis. In the last few decades, many modifications based on U-Net model have been proposed in the literature. However, there are relatively few improvements for the advanced UNet++ model. In our paper, we propose an encoding feature supervised UNet++(ES-UNet++) and apply it to the liver and tumor segmentation. ES-UNet++ consists of an encoding UNet++ and a segmentation UNet++. The well-trained encoding UNet++ can extract the encoding features of label map which are used to additionally supervise the segmentation UNet++. By adding supervision to the each encoder of segmentation UNet++, U-Nets of different depths that constitute UNet++ outperform the original version by average 5.7% in dice score and the overall dice score is thus improved by 2.1%. ES-UNet++ is evaluated with dataset LiTS, achieving 95.6% for liver segmentation and 67.4% for tumor segmentation in dice score. In this paper, we also concluded some valuable properties of ES-UNet++ by conducting comparative anaylsis between ES-UNet++ and UNet++:(1) encoding feature supervision can accelerate the convergence of the model.(2) encoding feature supervision enhances the effect of model pruning by achieving huge speedup while providing pruned models with fairly good performance.	['Yixuan Yu', 'Minnan Pei', 'Shiyuan Fang', 'Ruoxin Xiao', 'Jiahao Cui']	2022-11-15	null	null	null	null	['tumor-segmentation', 'liver-segmentation', 'automatic-liver-and-tumor-segmentation']	['computer-vision', 'medical', 'medical']	[ 6.85405880e-02 2.34516263e-01 -4.51024950e-01 -3.55109990e-01 -5.18311739e-01 -3.23080570e-01 1.83797538e-01 3.32621813e-01 -4.11110014e-01 5.66850662e-01 2.72254080e-01 -4.04129028e-01 -5.84659688e-02 -8.87687504e-01 -4.68238384e-01 -7.90638447e-01 -4.03412670e-01 5.88061213e-01 3.00035238e-01 2.36052349e-01 -4.41087075e-02 6.45470679e-01 -6.85250401e-01 3.49037439e-01 9.23607945e-01 1.09678924e+00 1.86213568e-01 6.80507123e-01 -5.49335070e-02 7.88859189e-01 -2.82674164e-01 7.39890477e-03 5.46465814e-01 -7.13866115e-01 -8.73645186e-01 1.95417672e-01 -6.80931881e-02 -3.66461933e-01 -4.31418061e-01 1.14105201e+00 7.13302672e-01 -3.13854575e-01 7.55746126e-01 -8.32036257e-01 -1.37392655e-01 1.02524912e+00 -6.47602916e-01 3.40781122e-01 -8.71784464e-02 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