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paperID stringlengths 36 36	pwc_id stringlengths 8 47	arxiv_id stringlengths 6 16 ⌀	nips_id float64	url_abs stringlengths 18 329	url_pdf stringlengths 18 742	title stringlengths 8 325	abstract stringlengths 1 7.27k ⌀	authors stringlengths 2 7.06k	published stringlengths 10 10 ⌀	conference stringlengths 12 47 ⌀	conference_url_abs stringlengths 16 198 ⌀	conference_url_pdf stringlengths 27 199 ⌀	proceeding stringlengths 6 47 ⌀	taskID stringlengths 7 1.44k	areaID stringclasses 688 values	embedding stringlengths 9.26k 12.5k	umap_embedding stringlengths 29 44
f3d897ac-ad58-4a83-920a-bc9f33f27d51	cancer-metastasis-detection-with-neural	1806.07064	null	http://arxiv.org/abs/1806.07064v1	http://arxiv.org/pdf/1806.07064v1.pdf	Cancer Metastasis Detection With Neural Conditional Random Field	Breast cancer diagnosis often requires accurate detection of metastasis in lymph nodes through Whole-slide Images (WSIs). Recent advances in deep convolutional neural networks (CNNs) have shown significant successes in medical image analysis and particularly in computational histopathology. Because of the outrageous large size of WSIs, most of the methods divide one slide into lots of small image patches and perform classification on each patch independently. However, neighboring patches often share spatial correlations, and ignoring these spatial correlations may result in inconsistent predictions. In this paper, we propose a neural conditional random field (NCRF) deep learning framework to detect cancer metastasis in WSIs. NCRF considers the spatial correlations between neighboring patches through a fully connected CRF which is directly incorporated on top of a CNN feature extractor. The whole deep network can be trained end-to-end with standard back-propagation algorithm with minor computational overhead from the CRF component. The CNN feature extractor can also benefit from considering spatial correlations via the CRF component. Compared to the baseline method without considering spatial correlations, we show that the proposed NCRF framework obtains probability maps of patch predictions with better visual quality. We also demonstrate that our method outperforms the baseline in cancer metastasis detection on the Camelyon16 dataset and achieves an average FROC score of 0.8096 on the test set. NCRF is open sourced at https://github.com/baidu-research/NCRF.	['Yi Li', 'Wei Ping']	2018-06-19	null	null	null	null	['cancer-metastasis-detection']	['medical']	[ 1.48922756e-01 2.65248299e-01 -4.16915685e-01 -2.79054761e-01 -1.09853697e+00 -2.11356074e-01 3.65897745e-01 4.34382528e-01 -5.10059714e-01 7.09081769e-01 -1.67302608e-01 -5.42018235e-01 6.64052516e-02 -8.84859681e-01 -7.54591227e-01 -1.17893291e+00 7.30379075e-02 2.75409192e-01 4.92124319e-01 3.24033201e-01 8.97134002e-03 6.71103179e-01 -7.97613561e-01 3.69387805e-01 6.28838181e-01 1.09153378e+00 4.99801010e-01 9.44295824e-01 1.49145173e-02 7.51345277e-01 -3.74911726e-01 -1.29679620e-01 -1.76593259e-01 4.18181755e-02 -7.88844585e-01 -1.81506008e-01 3.03941816e-01 -1.80774346e-01 -3.34994167e-01 9.78411436e-01 4.11629289e-01 -3.48102927e-01 7.32953608e-01 -7.79379845e-01 -1.81817263e-01 2.66800523e-01 -8.96570683e-01 1.65017247e-01 -3.70766670e-01 4.07582261e-02 7.65093565e-01 -8.38046789e-01 7.07988381e-01 5.35746455e-01 8.48482788e-01 3.30515295e-01 -1.06854665e+00 -7.99838841e-01 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a20f05cd-f06e-4158-b15c-0daf9cff030f	improving-aspect-sentiment-quad-prediction	2210.10291	null	https://arxiv.org/abs/2210.10291v1	https://arxiv.org/pdf/2210.10291v1.pdf	Improving Aspect Sentiment Quad Prediction via Template-Order Data Augmentation	Recently, aspect sentiment quad prediction (ASQP) has become a popular task in the field of aspect-level sentiment analysis. Previous work utilizes a predefined template to paraphrase the original sentence into a structure target sequence, which can be easily decoded as quadruplets of the form (aspect category, aspect term, opinion term, sentiment polarity). The template involves the four elements in a fixed order. However, we observe that this solution contradicts with the order-free property of the ASQP task, since there is no need to fix the template order as long as the quadruplet is extracted correctly. Inspired by the observation, we study the effects of template orders and find that some orders help the generative model achieve better performance. It is hypothesized that different orders provide various views of the quadruplet. Therefore, we propose a simple but effective method to identify the most proper orders, and further combine multiple proper templates as data augmentation to improve the ASQP task. Specifically, we use the pre-trained language model to select the orders with minimal entropy. By fine-tuning the pre-trained language model with these template orders, our approach improves the performance of quad prediction, and outperforms state-of-the-art methods significantly in low-resource settings.	['Shiwan Zhao', 'Yinhao Bai', 'Hang Gao', 'Yike Wu', 'Mengting Hu']	2022-10-19	null	null	null	null	['aspect-based-sentiment-analysis']	['natural-language-processing']	[ 3.02635103e-01 -8.20152387e-02 -2.82908261e-01 -4.11055803e-01 -6.89025521e-01 -8.11227083e-01 4.97681379e-01 -6.66958652e-03 -1.21574342e-01 3.13949198e-01 5.82214773e-01 -2.75254637e-01 1.89123243e-01 -8.22127461e-01 -6.19437754e-01 -6.87837362e-01 5.18305719e-01 2.62859643e-01 7.73381591e-02 -5.42277873e-01 4.50046897e-01 -1.73570663e-01 -1.43183506e+00 4.36541855e-01 1.02882457e+00 9.60105121e-01 9.46898982e-02 2.11941004e-01 -2.07330614e-01 4.88163739e-01 -5.78314066e-01 -9.32577252e-01 3.14713895e-01 -6.52643859e-01 -4.57059950e-01 2.74951041e-01 6.75845519e-02 4.41377237e-03 1.35223880e-01 1.12503314e+00 3.08073550e-01 4.31553723e-04 7.56819963e-01 -1.00533772e+00 -5.04287124e-01 5.00105858e-01 -6.52097285e-01 -1.87532589e-01 4.47170109e-01 -1.31705180e-01 1.54732788e+00 -9.93266582e-01 4.71594602e-01 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a6f69172-6cd8-4ee1-a980-e49c7f68fc0a	non-parametric-active-learning-and-rate	2107.00195	null	https://arxiv.org/abs/2107.00195v2	https://arxiv.org/pdf/2107.00195v2.pdf	Non-parametric Semi-Supervised Learning in Many-body Hilbert Space with Rescaled Logarithmic Fidelity	In quantum and quantum-inspired machine learning, the very first step is to embed the data in quantum space known as Hilbert space. Developing quantum kernel function (QKF), which defines the distances among the samples in the Hilbert space, belongs to the fundamental topics for machine learning. In this work, we propose the rescaled logarithmic fidelity (RLF) and non-parametric semi-supervised learning in the quantum space, which we name as RLF-NSSL. The rescaling takes advantage of the non-linearity of the kernel to tune the mutual distances of samples in the Hilbert space, and meanwhile avoids the exponentially-small fidelities between quantum many-qubit states. Being non-parametric excludes the possible effects from the variational parameters, and evidently demonstrates the advantages from the space itself. We compare RLF-NSSL with several well-known non-parametric algorithms including naive Bayes classifiers, k-nearest neighbors, and spectral clustering. Our method exhibits better accuracy particularly for the unsupervised case with no labeled samples and the few-shot cases with small numbers of labeled samples. With the visualizations by t-stochastic neighbor embedding, our results imply that the machine learning in the Hilbert space complies with the principles of maximal coding rate reduction, where the low-dimensional data exhibit within-class compressibility, between-class discrimination, and overall diversity. Our proposals can be applied to other quantum and quantum-inspired machine learning, including the methods using the parametric models such as tensor networks, quantum circuits, and quantum neural networks.	['Shi-Ju Ran', 'Wei-Ming Li']	2021-07-01	null	null	null	null	['tensor-networks']	['methodology']	[ 6.99886978e-02 -1.24634795e-01 -2.10745111e-01 -4.11937714e-01 -3.74728113e-01 -3.80505145e-01 5.55034220e-01 -1.18328445e-01 -5.61225235e-01 7.63326764e-01 -8.74067936e-03 -1.33815765e-01 -7.87025213e-01 -1.13290286e+00 -2.41295844e-01 -1.29003966e+00 -2.05372289e-01 3.91368449e-01 9.30664539e-02 -3.56135130e-01 5.45013011e-01 5.07176936e-01 -1.55652475e+00 1.46029135e-02 9.49116588e-01 7.46111691e-01 -3.95592839e-01 4.87693042e-01 -1.93830147e-01 4.00628000e-01 1.94180422e-02 -2.79727638e-01 3.04607064e-01 -7.50720322e-01 -8.13064396e-01 -3.70938241e-01 -4.85610142e-02 -1.61975082e-02 -8.54823709e-01 1.60501039e+00 3.24990511e-01 2.91313410e-01 1.17413831e+00 -1.23551011e+00 -9.39098120e-01 6.60849929e-01 -1.01585157e-01 4.61960733e-02 3.53662558e-02 -7.08945841e-03 1.06602597e+00 -6.17295325e-01 4.70484853e-01 1.13977897e+00 4.47478652e-01 5.26611626e-01 -1.54195857e+00 -5.22498906e-01 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e205e78a-82d3-448c-a422-721bebdba3ca	convolutional-feature-extraction-and-neural	1905.07581	null	https://arxiv.org/abs/1905.07581v1	https://arxiv.org/pdf/1905.07581v1.pdf	Convolutional Feature Extraction and Neural Arithmetic Logic Units for Stock Prediction	Stock prediction is a topic undergoing intense study for many years. Finance experts and mathematicians have been working on a way to predict the future stock price so as to decide to buy the stock or sell it to make profit. Stock experts or economists, usually analyze on the previous stock values using technical indicators, sentiment analysis etc to predict the future stock price. In recent years, many researches have extensively used machine learning for predicting the stock behaviour. In this paper we propose data driven deep learning approach to predict the future stock value with the previous price with the feature extraction property of convolutional neural network and to use Neural Arithmetic Logic Units with it.	['Jajati Keshari Sahoo', 'Shangeth Rajaa']	2019-05-18	null	null	null	null	['stock-prediction']	['time-series']	[-7.59260535e-01 -2.58341551e-01 -3.14441890e-01 -6.12897694e-01 2.20091790e-01 -4.29344118e-01 4.87024426e-01 1.84518486e-01 -4.79513288e-01 9.71024275e-01 1.99385554e-01 -3.97780776e-01 6.46647885e-02 -1.51816535e+00 -3.78350377e-01 -3.97556245e-01 -1.24491349e-01 4.23657507e-01 1.76644787e-01 -6.32767081e-01 1.00853169e+00 7.47578740e-01 -1.52763355e+00 1.50782466e-01 2.15751261e-01 1.64654398e+00 -1.54717878e-01 3.77571106e-01 -8.09627414e-01 1.56148767e+00 -6.05169833e-01 -6.14890635e-01 7.58924067e-01 -1.78149939e-01 -3.65285099e-01 -2.24027604e-01 -3.17532063e-01 -7.32301295e-01 -3.58851440e-02 1.16484439e+00 1.25941187e-02 -3.58810544e-01 5.07586956e-01 -1.12901759e+00 -8.12980235e-01 1.05029416e+00 -2.94652343e-01 7.03078389e-01 -2.76898026e-01 1.35100111e-01 1.05900669e+00 -7.53728926e-01 2.64812052e-01 6.67366505e-01 3.91474783e-01 -1.56657398e-03 -5.98986685e-01 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7.52608478e-02 2.87307799e-01 -4.03918266e-01 -1.85167804e-01 -6.50375932e-02]	[4.442723274230957, 4.2397589683532715]
f9eab37d-2e0f-4438-9f03-507f30d0aef0	a-convolutional-neural-network-for-gaze	2007.14432	null	https://arxiv.org/abs/2007.14432v1	https://arxiv.org/pdf/2007.14432v1.pdf	A Convolutional Neural Network for gaze preference detection: A potential tool for diagnostics of autism spectrum disorder in children	Early diagnosis of autism spectrum disorder (ASD) is known to improve the quality of life of affected individuals. However, diagnosis is often delayed even in wealthier countries including the US, largely due to the fact that gold standard diagnostic tools such as the Autism Diagnostic Observation Schedule (ADOS) and the Autism Diagnostic Interview-Revised (ADI-R) are time consuming and require expertise to administer. This trend is even more pronounced lower resources settings due to a lack of trained experts. As a result, alternative, less technical methods that leverage the unique ways in which children with ASD react to visual stimulation in a controlled environment have been developed to help facilitate early diagnosis. Previous studies have shown that, when exposed to a video that presents both social and abstract scenes side by side, a child with ASD will focus their attention towards the abstract images on the screen to a greater extent than a child without ASD. Such differential responses make it possible to implement an algorithm for the rapid diagnosis of ASD based on eye tracking against different visual stimuli. Here we propose a convolutional neural network (CNN) algorithm for gaze prediction using images extracted from a one-minute stimulus video. Our model achieved a high accuracy rate and robustness for prediction of gaze direction with independent persons and employing a different camera than the one used during testing. In addition to this, the proposed algorithm achieves a fast response time, providing a near real-time evaluation of ASD. Thereby, by applying the proposed method, we could significantly reduce the diagnosis time and facilitate the diagnosis of ASD in low resource regions.	['Mirko Zimic', 'Macarena Vittet Mondonedo', 'Robert H. Gilman', 'Franklin Barrientos Porras', 'Dennis Núñez Fernández', 'Patricia Sheen']	2020-07-28	null	null	null	null	['eye-tracking']	['computer-vision']	[ 2.56265312e-01 8.17137435e-02 3.23117554e-01 -2.28057295e-01 1.39382690e-01 -3.03207159e-01 -7.26569816e-02 3.52271408e-01 -5.86355746e-01 3.96408200e-01 -1.05542913e-01 2.38474458e-02 -2.94729412e-01 -3.79988730e-01 -2.59443223e-01 -4.29235488e-01 1.12659544e-01 3.81227344e-01 1.15410797e-01 9.17932391e-02 2.96338975e-01 6.15766048e-01 -2.11892319e+00 8.54493454e-02 1.02875733e+00 7.51551807e-01 7.03640640e-01 4.94471997e-01 7.01034889e-02 4.41354722e-01 -6.03983104e-01 6.96804142e-03 4.43433002e-02 -3.27382475e-01 -5.26610553e-01 1.02103256e-01 1.96375623e-01 -1.02528489e+00 -1.07250959e-02 1.01177824e+00 6.33518934e-01 -5.07167019e-02 6.16640568e-01 -1.23710465e+00 -4.55128282e-01 5.14062308e-02 -7.97080278e-01 1.60738409e-01 8.70719373e-01 4.57544744e-01 4.28659678e-01 -5.63933790e-01 1.57618091e-01 9.67200458e-01 2.07282990e-01 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e2d9e361-f1bc-4bee-825c-f1a28afcec63	neural-world-models-for-computer-vision	2306.09179	null	https://arxiv.org/abs/2306.09179v1	https://arxiv.org/pdf/2306.09179v1.pdf	Neural World Models for Computer Vision	Humans navigate in their environment by learning a mental model of the world through passive observation and active interaction. Their world model allows them to anticipate what might happen next and act accordingly with respect to an underlying objective. Such world models hold strong promises for planning in complex environments like in autonomous driving. A human driver, or a self-driving system, perceives their surroundings with their eyes or their cameras. They infer an internal representation of the world which should: (i) have spatial memory (e.g. occlusions), (ii) fill partially observable or noisy inputs (e.g. when blinded by sunlight), and (iii) be able to reason about unobservable events probabilistically (e.g. predict different possible futures). They are embodied intelligent agents that can predict, plan, and act in the physical world through their world model. In this thesis we present a general framework to train a world model and a policy, parameterised by deep neural networks, from camera observations and expert demonstrations. We leverage important computer vision concepts such as geometry, semantics, and motion to scale world models to complex urban driving scenes. First, we propose a model that predicts important quantities in computer vision: depth, semantic segmentation, and optical flow. We then use 3D geometry as an inductive bias to operate in the bird's-eye view space. We present for the first time a model that can predict probabilistic future trajectories of dynamic agents in bird's-eye view from 360{\deg} surround monocular cameras only. Finally, we demonstrate the benefits of learning a world model in closed-loop driving. Our model can jointly predict static scene, dynamic scene, and ego-behaviour in an urban driving environment.	['Anthony Hu']	2023-06-15	null	null	null	null	['navigate']	['reasoning']	[ 8.08018744e-02 6.47504270e-01 1.42289093e-03 -5.83454251e-01 -3.06409299e-02 -6.67083144e-01 1.03007340e+00 -2.30575740e-01 -5.89238048e-01 5.83209336e-01 2.24191561e-01 -2.96049505e-01 -2.29588337e-02 -1.12400699e+00 -9.07333314e-01 -6.40118361e-01 2.72811800e-02 7.32523084e-01 2.87196785e-01 -2.47562990e-01 2.52268404e-01 6.34695351e-01 -1.87626505e+00 -6.49672374e-02 2.75896817e-01 4.66188282e-01 7.38858819e-01 1.14222109e+00 4.48889941e-01 1.28672707e+00 -7.67136142e-02 -3.22300456e-02 1.78830415e-01 2.69461982e-02 -6.09757662e-01 3.26725364e-01 -1.67971894e-01 -5.14234424e-01 -9.08232093e-01 8.59405518e-01 2.52064783e-02 4.83439863e-01 5.16547859e-01 -1.26478028e+00 -3.18972111e-01 -5.85231297e-02 6.74247146e-02 3.43288034e-02 6.61418915e-01 1.04990387e+00 5.92658520e-01 -3.12033385e-01 9.92505074e-01 1.46448803e+00 1.10647716e-02 7.64421761e-01 -1.12313950e+00 -3.93461771e-02 5.68720758e-01 3.90205860e-01 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3c1f0cfd-ddc2-4e8a-a710-77780864e5d9	depression-status-estimation-by-deep-learning	2011.14966	null	https://arxiv.org/abs/2011.14966v1	https://arxiv.org/pdf/2011.14966v1.pdf	Depression Status Estimation by Deep Learning based Hybrid Multi-Modal Fusion Model	Preliminary detection of mild depression could immensely help in effective treatment of the common mental health disorder. Due to the lack of proper awareness and the ample mix of stigmas and misconceptions present within the society, mental health status estimation has become a truly difficult task. Due to the immense variations in character level traits from person to person, traditional deep learning methods fail to generalize in a real world setting. In our study we aim to create a human allied AI workflow which could efficiently adapt to specific users and effectively perform in real world scenarios. We propose a Hybrid deep learning approach that combines the essence of one shot learning, classical supervised deep learning methods and human allied interactions for adaptation. In order to capture maximum information and make efficient diagnosis video, audio, and text modalities are utilized. Our Hybrid Fusion model achieved a high accuracy of 96.3% on the Dataset; and attained an AUC of 0.9682 which proves its robustness in discriminating classes in complex real-world scenarios making sure that no cases of mild depression are missed during diagnosis. The proposed method is deployed in a cloud-based smartphone application for robust testing. With user-specific adaptations and state of the art methodologies, we present a state-of-the-art model with user friendly experience.	['Juned Kadiwala', 'Anugyan Das', 'Subin Mathew MS', 'Arnhav Datar', 'Saptarshi Majumder', 'Akash Das', 'Hari Sankar CN', 'Harikrishnan P', 'Hrithwik Shalu']	2020-11-30	null	null	null	null	['misconceptions']	['miscellaneous']	[ 2.80428350e-01 -1.02672470e-03 1.13272024e-02 -4.08080995e-01 -5.42924345e-01 5.50018400e-02 2.80594349e-01 2.97795206e-01 -4.15166289e-01 8.04116607e-01 -9.84046236e-02 -2.89790565e-03 -3.93295854e-01 -7.84020007e-01 -4.85173799e-02 -4.20427352e-01 4.20457385e-02 7.64172435e-01 -4.16581221e-02 -4.31357801e-01 -1.47955827e-02 5.30066550e-01 -1.73182499e+00 6.29291832e-01 1.18386066e+00 7.60578334e-01 6.67097606e-03 6.66317284e-01 1.39170960e-01 5.31880796e-01 -7.56222785e-01 -7.77716219e-01 -1.02656163e-01 -2.56596059e-01 -5.32737732e-01 4.47557382e-02 1.90847352e-01 -6.67337358e-01 -5.06420620e-02 8.62993658e-01 1.33392429e+00 1.60689168e-02 5.93007743e-01 -1.28497076e+00 -4.58071202e-01 2.65262634e-01 -5.14504492e-01 2.67288655e-01 7.29283273e-01 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58671457-430d-405e-be2d-6860730c4a42	a-latent-variable-recurrent-neural-network	1603.01913	null	http://arxiv.org/abs/1603.01913v2	http://arxiv.org/pdf/1603.01913v2.pdf	A Latent Variable Recurrent Neural Network for Discourse Relation Language Models	This paper presents a novel latent variable recurrent neural network architecture for jointly modeling sequences of words and (possibly latent) discourse relations between adjacent sentences. A recurrent neural network generates individual words, thus reaping the benefits of discriminatively-trained vector representations. The discourse relations are represented with a latent variable, which can be predicted or marginalized, depending on the task. The resulting model can therefore employ a training objective that includes not only discourse relation classification, but also word prediction. As a result, it outperforms state-of-the-art alternatives for two tasks: implicit discourse relation classification in the Penn Discourse Treebank, and dialog act classification in the Switchboard corpus. Furthermore, by marginalizing over latent discourse relations at test time, we obtain a discourse informed language model, which improves over a strong LSTM baseline.	['Jacob Eisenstein', 'Gholamreza Haffari', 'Yangfeng Ji']	2016-03-07	null	null	null	null	['dialog-act-classification', 'implicit-discourse-relation-classification']	['natural-language-processing', 'natural-language-processing']	[ 6.08059406e-01 1.08229911e+00 -6.33406579e-01 -3.66287887e-01 -8.14965427e-01 -5.29994488e-01 1.19085944e+00 1.43919483e-01 -2.70946085e-01 9.45638597e-01 7.63128936e-01 -5.98484993e-01 4.09482360e-01 -7.99507022e-01 -5.74132860e-01 -7.72520483e-01 -7.12456107e-02 8.28192055e-01 -1.02761472e-02 -3.11234355e-01 4.77472357e-02 -1.10193351e-02 -1.17829835e+00 7.86354363e-01 6.48046613e-01 5.59900939e-01 3.38393599e-01 8.35597932e-01 -4.08857048e-01 1.69088316e+00 -9.73604143e-01 -1.40096053e-01 -5.70943773e-01 -7.55674064e-01 -1.33356977e+00 1.50470018e-01 2.40987316e-02 -2.79284567e-01 -1.09176636e-01 6.16954327e-01 3.06501687e-02 5.03889322e-01 9.22415257e-01 -6.11929595e-01 -8.16358328e-01 1.14399457e+00 4.65558767e-02 8.17200169e-02 5.18826783e-01 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2f233a19-d539-479b-87a5-5a3e259deb44	tensor-networks-for-quantum-machine-learning	2303.11735	null	https://arxiv.org/abs/2303.11735v1	https://arxiv.org/pdf/2303.11735v1.pdf	Tensor networks for quantum machine learning	Once developed for quantum theory, tensor networks have been established as a successful machine learning paradigm. Now, they have been ported back to the quantum realm in the emerging field of quantum machine learning to assess problems that classical computers are unable to solve efficiently. Their nature at the interface between physics and machine learning makes tensor networks easily deployable on quantum computers. In this review article, we shed light on one of the major architectures considered to be predestined for variational quantum machine learning. In particular, we discuss how layouts like MPS, PEPS, TTNs and MERA can be mapped to a quantum computer, how they can be used for machine learning and data encoding and which implementation techniques improve their performance.	['Arne Peter Raulf', 'Frank Köster', 'Hans-Martin Rieser']	2023-03-21	null	null	null	null	['tensor-networks']	['methodology']	[-4.44489457e-02 1.24737181e-01 -2.27498978e-01 -2.91629910e-01 -3.98307264e-01 -7.06381023e-01 5.00803471e-01 1.54926330e-01 -2.40140602e-01 4.14446622e-01 -1.13016024e-01 -7.84067154e-01 -3.16022128e-01 -1.12261689e+00 -3.79360139e-01 -5.67284405e-01 -2.84617215e-01 5.36646307e-01 1.70933709e-01 -7.62466192e-01 4.00428951e-01 3.75598311e-01 -1.39249229e+00 4.89336640e-01 5.91497421e-01 7.83386528e-01 -3.26554924e-01 6.91469610e-01 -7.77613372e-02 6.01170897e-01 -1.18068643e-01 -5.54557741e-01 1.98595017e-01 -1.87574491e-01 -1.03673887e+00 -6.00327909e-01 2.88726985e-01 -3.06165218e-01 -1.01254630e+00 1.15266514e+00 6.51540980e-02 -1.99590418e-02 6.08192563e-01 -1.22742260e+00 -5.75019419e-01 8.03346753e-01 -2.45711370e-03 4.74351734e-01 2.12691694e-01 2.29180157e-01 1.39423478e+00 -4.53595281e-01 5.06113172e-01 1.18968606e+00 2.25054532e-01 4.99706477e-01 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bfc55ddb-9b81-46de-821d-dd859bb80979	a-performance-evaluation-of-correspondence	1907.02890	null	https://arxiv.org/abs/1907.02890v1	https://arxiv.org/pdf/1907.02890v1.pdf	A Performance Evaluation of Correspondence Grouping Methods for 3D Rigid Data Matching	Seeking consistent point-to-point correspondences between 3D rigid data (point clouds, meshes, or depth maps) is a fundamental problem in 3D computer vision. While a number of correspondence selection methods have been proposed in recent years, their advantages and shortcomings remain unclear regarding different applications and perturbations. To fill this gap, this paper gives a comprehensive evaluation of nine state-of-the-art 3D correspondence grouping methods. A good correspondence grouping algorithm is expected to retrieve as many as inliers from initial feature matches, giving a rise in both precision and recall as well as facilitating accurate transformation estimation. Toward this rule, we deploy experiments on three benchmarks with different application contexts including shape retrieval, 3D object recognition, and point cloud registration together with various perturbations such as noise, point density variation, clutter, occlusion, partial overlap, different scales of initial correspondences, and different combinations of keypoint detectors and descriptors. The rich variety of application scenarios and nuisances result in different spatial distributions and inlier ratios of initial feature correspondences, thus enabling a thorough evaluation. Based on the outcomes, we give a summary of the traits, merits, and demerits of evaluated approaches and indicate some potential future research directions.	['Yanning Zhang', 'Peng Wang', 'Ke Xian', 'Jiaqi Yang']	2019-07-05	null	null	null	null	['3d-object-recognition']	['computer-vision']	[-9.55998302e-02 -5.61441243e-01 3.53521928e-02 -3.38306576e-01 -7.67629147e-01 -7.23069787e-01 8.91844690e-01 3.78397107e-01 -1.47788378e-03 2.25108370e-01 -1.19579516e-01 1.37871101e-01 -3.68276447e-01 -5.52182555e-01 -4.86207455e-01 -5.31225443e-01 -1.36982039e-01 9.30312276e-01 4.63477075e-01 -1.86939478e-01 6.82357311e-01 1.28720427e+00 -1.87529683e+00 -3.49934369e-01 6.44256234e-01 9.01991427e-01 -3.57750095e-02 1.69770807e-01 -1.94865808e-01 -4.26680773e-01 -6.04573905e-01 -2.62213260e-01 6.83537722e-01 1.09324321e-01 -2.33885705e-01 3.59329790e-01 8.27187538e-01 -4.75158840e-02 -1.59314692e-01 1.15477574e+00 7.34561384e-01 9.84199569e-02 7.93703914e-01 -1.24820232e+00 -4.68741417e-01 -1.55885294e-01 -7.87465394e-01 2.94726677e-02 8.55605781e-01 6.57088906e-02 8.48334908e-01 -1.50364578e+00 6.74194217e-01 1.46569467e+00 8.26693892e-01 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6be1a668-58da-4860-afc2-e550605d7077	cyber-risk-in-health-facilities-a-systematic	2102.04093	null	https://arxiv.org/abs/2102.04093v1	https://arxiv.org/pdf/2102.04093v1.pdf	Cyber Risk in Health Facilities: A Systematic Literature Review	The current world challenges include issues such as infectious disease pandemics, environmental health risks, food safety, and crime prevention. Through this article, a special emphasis is given to one of the main challenges in the healthcare sector during the COVID-19 pandemic, the cyber risk. Since the beginning of the Covid-19 pandemic, the World Health Organization has detected a dramatic increase in the number of cyber-attacks. For instance, in Italy the COVID-19 emergency has heavily affected cybersecurity; from January to April 2020, the total of attacks, accidents, and violations of privacy to the detriment of companies and individuals has doubled. Using a systematic and rigorous approach, this paper aims to analyze the literature on the cyber risk in the healthcare sector to understand the real knowledge on this topic. The findings highlight the poor attention of the scientific community on this topic, except in the United States. The literature lacks research contributions to support cyber risk management in subject areas such as Business, Management and Accounting; Social Science; and Mathematics. This research outlines the need to empirically investigate the cyber risk, giving a practical solution to health facilities. Keywords: cyber risk; cyber-attack; cybersecurity; computer security; COVID-19; coronavirus;information technology risk; risk management; risk assessment; health facilities; healthcare sector;systematic literature review; insurance	['Anna Guerrieri', 'Enrico Sorano', 'Alessandro Rizzi', 'Alberto Sardi']	2021-02-08	null	null	null	null	['computer-security']	['miscellaneous']	[ 1.46344349e-01 4.64163840e-01 -8.69574174e-02 6.55026197e-01 6.26925901e-02 -4.50437397e-01 5.22081971e-01 9.19995487e-01 -5.76054752e-01 4.08028513e-01 4.09057766e-01 -9.90827620e-01 -5.62134326e-01 -7.95107722e-01 -1.94789514e-01 -4.26641732e-01 4.22416739e-02 1.58025101e-01 -3.86131376e-01 -2.43739203e-01 6.51704490e-01 4.36851650e-01 -8.34787726e-01 -2.47475371e-01 4.72385138e-01 8.51702809e-01 -7.39799216e-02 3.64117682e-01 1.72032505e-01 5.66413105e-01 -7.51795888e-01 -5.34651160e-01 3.86593699e-01 -1.01546206e-01 -5.70893824e-01 -7.95321405e-01 -8.53609443e-01 -3.66079688e-01 4.34903800e-01 1.20637548e+00 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8cd5d937-9ddb-4c32-bd29-7ac1e90dcd0c	plan-explicability-and-predictability-for	1511.08158	null	http://arxiv.org/abs/1511.08158v2	http://arxiv.org/pdf/1511.08158v2.pdf	Plan Explicability and Predictability for Robot Task Planning	Intelligent robots and machines are becoming pervasive in human populated environments. A desirable capability of these agents is to respond to goal-oriented commands by autonomously constructing task plans. However, such autonomy can add significant cognitive load and potentially introduce safety risks to humans when agents behave unexpectedly. Hence, for such agents to be helpful, one important requirement is for them to synthesize plans that can be easily understood by humans. While there exists previous work that studied socially acceptable robots that interact with humans in "natural ways", and work that investigated legible motion planning, there lacks a general solution for high level task planning. To address this issue, we introduce the notions of plan {\it explicability} and {\it predictability}. To compute these measures, first, we postulate that humans understand agent plans by associating abstract tasks with agent actions, which can be considered as a labeling process. We learn the labeling scheme of humans for agent plans from training examples using conditional random fields (CRFs). Then, we use the learned model to label a new plan to compute its explicability and predictability. These measures can be used by agents to proactively choose or directly synthesize plans that are more explicable and predictable to humans. We provide evaluations on a synthetic domain and with human subjects using physical robots to show the effectiveness of our approach	['Sarath Sreedharan', 'Yu Zhang', 'Tathagata Chakraborti', 'Subbarao Kambhampati', 'Hankz Hankui Zhuo', 'Anagha Kulkarni']	2015-11-25	null	null	null	null	['robot-task-planning']	['robots']	[ 3.71721357e-01 1.03139186e+00 1.61738023e-01 -6.26401246e-01 -3.15302461e-02 -3.86934638e-01 1.03918946e+00 5.35579808e-02 -4.14814085e-01 1.04764378e+00 3.49817604e-01 -1.78412482e-01 -1.47920594e-01 -8.32207918e-01 -4.11248863e-01 -4.45402145e-01 -3.17347437e-01 1.05708754e+00 2.43636459e-01 -3.34448874e-01 1.53972477e-01 5.24164140e-01 -1.61419952e+00 -9.50484127e-02 1.02846801e+00 2.79801369e-01 5.69180608e-01 4.59168255e-01 3.65615427e-01 1.24818945e+00 -4.16839927e-01 1.70223087e-01 1.94515303e-01 -4.02565569e-01 -1.44556713e+00 4.98356134e-01 -5.60483396e-01 -2.93820590e-01 1.26122922e-01 8.66517007e-01 -1.37393728e-01 4.85735804e-01 8.80836487e-01 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1a2d26c1-0f8b-4c07-87e3-ebcfd72300d6	neural-network-ensembles-to-real-time	1802.06963	null	http://arxiv.org/abs/1802.06963v1	http://arxiv.org/pdf/1802.06963v1.pdf	Neural Network Ensembles to Real-time Identification of Plug-level Appliance Measurements	The problem of identifying end-use electrical appliances from their individual consumption profiles, known as the appliance identification problem, is a primary stage in both Non-Intrusive Load Monitoring (NILM) and automated plug-wise metering. Therefore, appliance identification has received dedicated studies with various electric appliance signatures, classification models, and evaluation datasets. In this paper, we propose a neural network ensembles approach to address this problem using high resolution measurements. The models are trained on the raw current and voltage waveforms, and thus, eliminating the need for well engineered appliance signatures. We evaluate the proposed model on a publicly available appliance dataset from 55 residential buildings, 11 appliance categories, and over 1000 measurements. We further study the stability of the trained models with respect to training dataset, sampling frequency, and variations in the steady-state operation of appliances.	['Bin Yang', 'Karim Said Barsim', 'Lukas Mauch']	2018-02-20	null	null	null	null	['non-intrusive-load-monitoring', 'non-intrusive-load-monitoring', 'non-intrusive-load-monitoring']	['knowledge-base', 'miscellaneous', 'time-series']	[ 3.41301858e-01 -3.23288083e-01 -1.82642639e-01 -5.88745892e-01 -7.11819172e-01 -5.26734293e-01 4.33600903e-01 -9.02105868e-03 3.16312522e-01 7.31588185e-01 -1.49506897e-01 -3.02437454e-01 -5.86364329e-01 -6.41869068e-01 -9.78438109e-02 -9.84991550e-01 -5.76554686e-02 3.28601450e-01 -4.95766222e-01 2.95899779e-01 -6.80442974e-02 5.53716302e-01 -1.35567009e+00 -1.47451088e-01 1.04103220e+00 1.31844842e+00 4.64458112e-03 4.84287769e-01 4.08070654e-01 8.81142080e-01 -1.05721319e+00 1.90651432e-01 -1.24773696e-01 -4.61232454e-01 -5.68174839e-01 2.41040140e-01 -1.28794879e-01 -3.05889726e-01 4.43187319e-02 9.88458812e-01 4.95639265e-01 1.35907471e-01 7.80953825e-01 -1.68478203e+00 -5.18270314e-01 9.66291547e-01 -3.05021584e-01 1.01021849e-01 2.26051286e-01 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29ed0cbb-6bb0-4932-a56b-37f6bb82af65	can-lies-be-faked-comparing-low-stakes-and	2211.13035	null	https://arxiv.org/abs/2211.13035v1	https://arxiv.org/pdf/2211.13035v1.pdf	Can lies be faked? Comparing low-stakes and high-stakes deception video datasets from a Machine Learning perspective	Despite the great impact of lies in human societies and a meager 54% human accuracy for Deception Detection (DD), Machine Learning systems that perform automated DD are still not viable for proper application in real-life settings due to data scarcity. Few publicly available DD datasets exist and the creation of new datasets is hindered by the conceptual distinction between low-stakes and high-stakes lies. Theoretically, the two kinds of lies are so distinct that a dataset of one kind could not be used for applications for the other kind. Even though it is easier to acquire data on low-stakes deception since it can be simulated (faked) in controlled settings, these lies do not hold the same significance or depth as genuine high-stakes lies, which are much harder to obtain and hold the practical interest of automated DD systems. To investigate whether this distinction holds true from a practical perspective, we design several experiments comparing a high-stakes DD dataset and a low-stakes DD dataset evaluating their results on a Deep Learning classifier working exclusively from video data. In our experiments, a network trained in low-stakes lies had better accuracy classifying high-stakes deception than low-stakes, although using low-stakes lies as an augmentation strategy for the high-stakes dataset decreased its accuracy.	['Gustavo Paetzold', 'Tomas Henrique Maul', 'Adriana Postal', 'Mateus Karvat Camara']	2022-11-23	null	null	null	null	['deception-detection']	['miscellaneous']	[-8.43131542e-02 3.46724719e-01 -2.82265723e-01 -4.41222280e-01 -6.26802683e-01 -5.00550330e-01 8.13156307e-01 -8.62583071e-02 -8.20457757e-01 1.11757660e+00 -1.33836389e-01 -4.48529333e-01 -1.66859597e-01 -7.59673655e-01 -8.69931936e-01 -5.20988464e-01 2.08703339e-01 1.90459237e-01 2.60869898e-02 -1.15504175e-01 2.37377182e-01 3.79321545e-01 -1.60850990e+00 4.85448480e-01 8.55045855e-01 1.09659803e+00 -3.95687789e-01 3.25124383e-01 5.17240286e-01 1.07113659e+00 -1.13277459e+00 -1.19256413e+00 6.69575036e-01 -5.05003095e-01 -6.09843075e-01 -4.89299774e-01 5.68512738e-01 -8.44428182e-01 -3.48822981e-01 1.22216034e+00 3.73048872e-01 -5.13769984e-01 6.60730779e-01 -1.66127229e+00 -8.06573510e-01 3.82715642e-01 -3.51096779e-01 3.20628196e-01 5.42599082e-01 3.16580713e-01 9.49566245e-01 -4.03391957e-01 6.72933698e-01 1.01694131e+00 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32f2e7dd-daa2-47f3-89eb-4567ba6e7b06	learning-cross-lingual-ir-from-an-english	2112.08185	null	https://arxiv.org/abs/2112.08185v3	https://arxiv.org/pdf/2112.08185v3.pdf	Learning Cross-Lingual IR from an English Retriever	We present DR.DECR (Dense Retrieval with Distillation-Enhanced Cross-Lingual Representation), a new cross-lingual information retrieval (CLIR) system trained using multi-stage knowledge distillation (KD). The teacher of DR.DECR relies on a highly effective but computationally expensive two-stage inference process consisting of query translation and monolingual IR, while the student, DR.DECR, executes a single CLIR step. We teach DR.DECR powerful multilingual representations as well as CLIR by optimizing two corresponding KD objectives. Learning useful representations of non-English text from an English-only retriever is accomplished through a cross-lingual token alignment algorithm that relies on the representation capabilities of the underlying multilingual encoders. In both in-domain and zero-shot out-of-domain evaluation, DR.DECR demonstrates far superior accuracy over direct fine-tuning with labeled CLIR data. It is also the best single-model retriever on the XOR-TyDi benchmark at the time of this writing.	['Avirup Sil', 'Young-suk Lee', 'Bhavani Iyer', 'Md Arafat Sultan', 'Martin Franz', 'Yulong Li']	2021-12-15	null	https://aclanthology.org/2022.naacl-main.329	https://aclanthology.org/2022.naacl-main.329.pdf	naacl-2022-7	['cross-lingual-information-retrieval']	['natural-language-processing']	[-1.23125680e-01 -2.84393191e-01 -6.49314880e-01 -2.71292269e-01 -2.06795168e+00 -9.52048957e-01 8.04202616e-01 2.49089241e-01 -9.03671920e-01 7.35008538e-01 2.26471901e-01 -5.69876194e-01 -1.22363836e-01 -4.89880443e-01 -8.88309479e-01 -2.72112787e-01 4.08538401e-01 1.21434724e+00 -2.30296865e-01 -6.72258496e-01 -1.78195611e-01 2.74518281e-01 -1.13190246e+00 4.85212833e-01 8.40210497e-01 6.11631870e-01 1.84364706e-01 5.69196939e-01 -4.35270928e-02 9.66782153e-01 -3.79389375e-01 -5.90513945e-01 1.17379539e-01 -2.05826182e-02 -9.93974209e-01 -8.20650458e-01 5.52401841e-01 -4.64229941e-01 -5.26371121e-01 7.09405124e-01 8.88089299e-01 3.18333298e-01 8.93098593e-01 -4.88980830e-01 -1.52297354e+00 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41d781ae-3cdf-41a6-badd-9ebce42b193d	neural-duplicate-question-detection-without-1	1911.05594	null	https://arxiv.org/abs/1911.05594v2	https://arxiv.org/pdf/1911.05594v2.pdf	Neural Duplicate Question Detection without Labeled Training Data	Supervised training of neural models to duplicate question detection in community Question Answering (cQA) requires large amounts of labeled question pairs, which are costly to obtain. To minimize this cost, recent works thus often used alternative methods, e.g., adversarial domain adaptation. In this work, we propose two novel methods: (1) the automatic generation of duplicate questions, and (2) weak supervision using the title and body of a question. We show that both can achieve improved performances even though they do not require any labeled data. We provide comprehensive comparisons of popular training strategies, which provides important insights on how to best train models in different scenarios. We show that our proposed approaches are more effective in many cases because they can utilize larger amounts of unlabeled data from cQA forums. Finally, we also show that our proposed approach for weak supervision with question title and body information is also an effective method to train cQA answer selection models without direct answer supervision.	['Andreas Rücklé', 'Iryna Gurevych', 'Nafise Sadat Moosavi']	2019-11-13	neural-duplicate-question-detection-without	https://aclanthology.org/D19-1171	https://aclanthology.org/D19-1171.pdf	ijcnlp-2019-11	['answer-selection']	['natural-language-processing']	[ 1.46852925e-01 1.01222105e-01 1.49780229e-01 -3.87143612e-01 -1.37852681e+00 -8.05461466e-01 5.58194399e-01 1.80840686e-01 -4.62528199e-01 1.04156530e+00 -4.27418724e-02 -5.05984128e-01 1.44521266e-01 -9.29224968e-01 -7.62611449e-01 -3.60054344e-01 5.88470757e-01 5.07326365e-01 4.26943958e-01 -4.96534079e-01 1.90927178e-01 1.07711971e-01 -1.38866115e+00 3.50134373e-01 1.49151027e+00 7.57190824e-01 2.21857242e-02 7.48429418e-01 -4.46714550e-01 1.14016759e+00 -1.14084673e+00 -7.27484643e-01 2.28362441e-01 -8.21677625e-01 -1.32234645e+00 -1.86375473e-02 7.85275638e-01 -4.34370190e-01 -2.65887976e-01 7.93743312e-01 6.19054615e-01 1.89213723e-01 4.98262614e-01 -1.09932876e+00 -8.55298519e-01 3.41500819e-01 -1.81943759e-01 2.48303398e-01 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1.01868427e+00 4.84223634e-01 1.58295676e-01 7.50516772e-01 -3.60224620e-02 -1.07681239e+00 -1.27017152e+00 -2.43031427e-01 7.29017138e-01 2.50769198e-01 -2.86425292e-01 -5.23928165e-01 -3.57519127e-02]	[11.329436302185059, 8.140533447265625]
f5d28fae-4fd4-43ef-a8ad-424f90044908	kg-bert-bert-for-knowledge-graph-completion	1909.03193	null	https://arxiv.org/abs/1909.03193v2	https://arxiv.org/pdf/1909.03193v2.pdf	KG-BERT: BERT for Knowledge Graph Completion	Knowledge graphs are important resources for many artificial intelligence tasks but often suffer from incompleteness. In this work, we propose to use pre-trained language models for knowledge graph completion. We treat triples in knowledge graphs as textual sequences and propose a novel framework named Knowledge Graph Bidirectional Encoder Representations from Transformer (KG-BERT) to model these triples. Our method takes entity and relation descriptions of a triple as input and computes scoring function of the triple with the KG-BERT language model. Experimental results on multiple benchmark knowledge graphs show that our method can achieve state-of-the-art performance in triple classification, link prediction and relation prediction tasks.	['Liang Yao', 'Yuan Luo', 'Chengsheng Mao']	2019-09-07	null	null	null	null	['triple-classification']	['graphs']	[-1.07450277e-01 6.80087805e-01 -1.01066852e+00 -2.59749800e-01 -4.97414976e-01 -3.84874225e-01 6.29841626e-01 4.45479959e-01 5.82895130e-02 9.96087313e-01 2.49788210e-01 -6.12797976e-01 -1.71095848e-01 -1.37022424e+00 -1.27167130e+00 1.75009519e-01 -1.04723506e-01 8.35205257e-01 5.04031420e-01 -4.52667296e-01 -3.62224281e-01 7.40713440e-03 -1.14145947e+00 7.54489899e-01 8.54594111e-01 8.11873734e-01 -2.27172554e-01 4.12168354e-01 -4.79112953e-01 1.73010039e+00 -2.63893493e-02 -1.34910417e+00 -1.20482557e-02 -5.31388074e-02 -1.41313267e+00 -2.78831989e-01 1.56378180e-01 -5.55219464e-02 -9.02370989e-01 8.80029798e-01 8.50429293e-03 -7.59493858e-02 4.79597181e-01 -1.47699666e+00 -1.25117803e+00 1.33090866e+00 -2.28938833e-01 1.00729018e-01 7.77255177e-01 -3.45771313e-01 1.57409132e+00 -9.89787042e-01 1.07969463e+00 1.14696574e+00 7.47057676e-01 1.42376363e-01 -1.02490866e+00 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ec4159b2-b72c-48df-8e2e-e85e89d44c1c	sensible-at-semeval-2016-task-11-neural	null	null	https://aclanthology.org/S16-1148	https://aclanthology.org/S16-1148.pdf	Sensible at SemEval-2016 Task 11: Neural Nonsense Mangled in Ensemble Mess	null	['Gillin Nat']	2016-06-01	null	null	null	semeval-2016-6	['complex-word-identification']	['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 9.22211111e-01 5.37373662e-01 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72de5dd5-2021-4c46-8448-87a56bd9a544	4k-haze-a-dehazing-benchmark-with-4k	2303.15848	null	https://arxiv.org/abs/2303.15848v1	https://arxiv.org/pdf/2303.15848v1.pdf	4K-HAZE: A Dehazing Benchmark with 4K Resolution Hazy and Haze-Free Images	Currently, mobile and IoT devices are in dire need of a series of methods to enhance 4K images with limited resource expenditure. The absence of large-scale 4K benchmark datasets hampers progress in this area, especially for dehazing. The challenges in building ultra-high-definition (UHD) dehazing datasets are the absence of estimation methods for UHD depth maps, high-quality 4K depth estimation datasets, and migration strategies for UHD haze images from synthetic to real domains. To address these problems, we develop a novel synthetic method to simulate 4K hazy images (including nighttime and daytime scenes) from clear images, which first estimates the scene depth, simulates the light rays and object reflectance, then migrates the synthetic images to real domains by using a GAN, and finally yields the hazy effects on 4K resolution images. We wrap these synthesized images into a benchmark called the 4K-HAZE dataset. Specifically, we design the CS-Mixer (an MLP-based model that integrates \textbf{C}hannel domain and \textbf{S}patial domain) to estimate the depth map of 4K clear images, the GU-Net to migrate a 4K synthetic image to the real hazy domain. The most appealing aspect of our approach (depth estimation and domain migration) is the capability to run a 4K image on a single GPU with 24G RAM in real-time (33fps). Additionally, this work presents an objective assessment of several state-of-the-art single-image dehazing methods that are evaluated using the 4K-HAZE dataset. At the end of the paper, we discuss the limitations of the 4K-HAZE dataset and its social implications.	['Xiuyi Jia', 'Zhuoran Zheng']	2023-03-28	null	null	null	null	['image-dehazing']	['computer-vision']	[ 2.75646180e-01 -1.93711311e-01 6.17841482e-01 -1.86828405e-01 -7.23484993e-01 -2.46936291e-01 4.26259726e-01 -5.31142354e-01 -2.70880669e-01 7.28698075e-01 -1.31267473e-01 -2.00497463e-01 7.55796134e-02 -1.24285412e+00 -5.97602487e-01 -1.21819866e+00 2.08760992e-01 1.63845867e-01 3.76437366e-01 -3.29292476e-01 -1.59193501e-02 2.80867547e-01 -1.93506134e+00 2.43076310e-01 1.11207426e+00 1.09128392e+00 4.59629416e-01 1.20603871e+00 2.87577361e-01 9.77918565e-01 -7.26368308e-01 -1.09673791e-01 5.13629317e-01 -4.77156311e-01 -4.93456662e-01 1.07304901e-01 7.03059256e-01 -9.79704618e-01 -1.33603290e-01 8.59213471e-01 7.43716776e-01 1.50616929e-01 5.47797501e-01 -1.18235767e+00 -5.34636796e-01 -2.48831943e-01 -5.17430484e-01 2.08284393e-01 -1.05647340e-01 3.49559039e-01 1.20933838e-02 -8.17194164e-01 1.60938248e-01 7.74124682e-01 5.38672745e-01 4.05891150e-01 -8.50740373e-01 -8.36257994e-01 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012df7ec-d35d-4d2a-823a-f97b45380369	learning-over-families-of-sets-hypergraph	2101.07773	null	https://arxiv.org/abs/2101.07773v1	https://arxiv.org/pdf/2101.07773v1.pdf	Learning over Families of Sets -- Hypergraph Representation Learning for Higher Order Tasks	Graph representation learning has made major strides over the past decade. However, in many relational domains, the input data are not suited for simple graph representations as the relationships between entities go beyond pairwise interactions. In such cases, the relationships in the data are better represented as hyperedges (set of entities) of a non-uniform hypergraph. While there have been works on principled methods for learning representations of nodes of a hypergraph, these approaches are limited in their applicability to tasks on non-uniform hypergraphs (hyperedges with different cardinalities). In this work, we exploit the incidence structure to develop a hypergraph neural network to learn provably expressive representations of variable sized hyperedges which preserve local-isomorphism in the line graph of the hypergraph, while also being invariant to permutations of its constituent vertices. Specifically, for a given vertex set, we propose frameworks for (1) hyperedge classification and (2) variable sized expansion of partially observed hyperedges which captures the higher order interactions among vertices and hyperedges. We evaluate performance on multiple real-world hypergraph datasets and demonstrate consistent, significant improvement in accuracy, over state-of-the-art models.	['George Karypis', 'Da Zheng', 'Balasubramaniam Srinivasan']	2021-01-19	null	null	null	null	['hyperedge-classification']	['graphs']	[ 3.23408902e-01 7.55432606e-01 -5.51833212e-01 -3.34157616e-01 -1.63352624e-01 -8.64019036e-01 6.65661693e-01 5.83503783e-01 1.71562448e-01 7.48685479e-01 3.06092829e-01 -5.50643921e-01 -5.40043831e-01 -1.52314723e+00 -8.63022089e-01 -5.55023193e-01 -6.48834527e-01 1.03233981e+00 -2.96755806e-02 -1.13705315e-01 -1.53255150e-01 7.16743886e-01 -1.44131017e+00 2.58175135e-01 3.74800205e-01 5.01133740e-01 -4.71795738e-01 5.53933144e-01 -1.02972738e-01 5.65379560e-01 -3.80845696e-01 -6.72997117e-01 3.22133094e-01 -1.03548475e-01 -9.82910633e-01 1.09299965e-01 7.79448569e-01 1.99320927e-01 -9.52237189e-01 1.02443576e+00 9.15538669e-02 9.69788581e-02 1.03883433e+00 -1.56629157e+00 -1.00721288e+00 9.87064302e-01 -7.83715546e-01 9.81126577e-02 5.65328240e-01 -3.43005925e-01 1.91720581e+00 -2.45893016e-01 8.61877441e-01 1.28209162e+00 3.94130617e-01 1.64460525e-01 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60b4c8de-05f6-4dd5-8c62-b48d4992b6cc	skeleton-based-action-recognition-using-lstm	1707.02356	null	http://arxiv.org/abs/1707.02356v1	http://arxiv.org/pdf/1707.02356v1.pdf	Skeleton-based Action Recognition Using LSTM and CNN	Recent methods based on 3D skeleton data have achieved outstanding performance due to its conciseness, robustness, and view-independent representation. With the development of deep learning, Convolutional Neural Networks (CNN) and Long Short Term Memory (LSTM)-based learning methods have achieved promising performance for action recognition. However, for CNN-based methods, it is inevitable to loss temporal information when a sequence is encoded into images. In order to capture as much spatial-temporal information as possible, LSTM and CNN are adopted to conduct effective recognition with later score fusion. In addition, experimental results show that the score fusion between CNN and LSTM performs better than that between LSTM and LSTM for the same feature. Our method achieved state-of-the-art results on NTU RGB+D datasets for 3D human action analysis. The proposed method achieved 87.40% in terms of accuracy and ranked $1^{st}$ place in Large Scale 3D Human Activity Analysis Challenge in Depth Videos.	['Yonghong Hou', 'Shuang Wang', 'Chuankun Li', 'Wanqing Li', 'Pichao Wang']	2017-07-06	null	null	null	null	['action-analysis']	['computer-vision']	[ 2.66474366e-01 -4.60297614e-01 -2.67320067e-01 -4.00636792e-01 -6.46102250e-01 2.08982304e-01 3.55152011e-01 -1.47883311e-01 -6.36606455e-01 4.06200171e-01 5.21321237e-01 2.82388508e-01 6.70359060e-02 -5.20207524e-01 -4.30208027e-01 -5.96800685e-01 -1.91372663e-01 -8.25272352e-02 4.52417672e-01 -7.55880401e-02 1.38692603e-01 5.39162099e-01 -1.57575846e+00 5.48950553e-01 2.99399376e-01 1.43976355e+00 6.42155781e-02 5.31863451e-01 -1.70323268e-01 1.38165355e+00 -5.32403886e-01 -9.38567147e-02 2.68854320e-01 -3.10653657e-01 -8.50580812e-01 2.17694893e-01 3.58945042e-01 -6.05228961e-01 -9.00882363e-01 7.40567863e-01 7.24896908e-01 3.30881208e-01 3.12681109e-01 -9.51089203e-01 -1.40257522e-01 -3.69280875e-02 -6.86454415e-01 4.74588454e-01 6.16673827e-01 1.67922065e-01 5.92824578e-01 -7.30833292e-01 3.85434449e-01 9.69335318e-01 5.23983181e-01 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f532d330-f6c1-4891-970c-47207bb9c590	glad-groningen-lightweight-authorship	null	null	https://github.com/pan-webis-de/huerlimann15/blob/master/Notebook/pan15-notebook.pdf	https://github.com/pan-webis-de/huerlimann15/blob/master/Notebook/pan15-notebook.pdf	GLAD: Groningen Lightweight Authorship Detection	We present a simple and effective approach to authorship verification for Dutch, English, Spanish and Greek, which can be easily ported to yet other languages.We train a binary linear classifier both on the features describing known and unknown documents individually, and on the joint features comparing these two types of documents. The list of feature types includes, among others, character n-grams, the lexical overlap, visual text properties and a compression measure. We obtain competitive results that outperform the baseline and position our system among the top PAN shared task participants.	['and Malvina Nissim', 'Simon Šuster', 'Esther van den Berg', 'Benno Weck', 'Manuela Hürlimann']	2015-09-06	null	null	null	null	['authorship-verification']	['natural-language-processing']	[-7.26340935e-02 -4.08874780e-01 -3.30595940e-01 -2.39411548e-01 -8.65090430e-01 -1.12213266e+00 1.13760555e+00 6.06434345e-01 -7.57158637e-01 8.48508477e-01 1.63736999e-01 -2.87923217e-01 -1.73189789e-02 -2.42298201e-01 -2.55919956e-02 -2.22728699e-01 -1.35222852e-01 8.11254919e-01 1.21779583e-01 6.67957738e-02 9.78552759e-01 8.23220849e-01 -1.47084510e+00 4.86540288e-01 6.17346346e-01 6.90780342e-01 -3.44593942e-01 1.02749765e+00 -1.16452500e-01 3.76025587e-01 -8.17118168e-01 -9.92382765e-01 1.05175689e-01 -2.10697144e-01 -9.16765153e-01 -2.12360024e-01 7.75811732e-01 -2.82526642e-01 -3.14573646e-01 7.38554776e-01 6.04637027e-01 -3.39924365e-01 1.21881211e+00 -1.41685879e+00 -7.47975945e-01 6.57591224e-01 -4.55732614e-01 4.17380720e-01 9.33826745e-01 -5.99678978e-02 1.20482922e+00 -1.09626710e+00 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b6b351cd-d041-4734-a955-7489e114921f	equivalence-of-two-expressions-of-principal	2301.03039	null	https://arxiv.org/abs/2301.03039v1	https://arxiv.org/pdf/2301.03039v1.pdf	Equivalence of Two Expressions of Principal Line	Geometry-based camera calibration using principal line is more precise and robust than calibration using optimization approaches; therefore, several researches try to re-derive the principal line from different views of 2D projective geometry to increase alternatives of the calibration process. In this report, algebraical equivalence of two expressions of principal line, one derived w.r.t homography and the other using for two sets of orthogonal vanishing points, is proved. Moreover, the extension of the second expression to incorporate infinite vanishing point is carried out with simple mathematics.	['Jen-Hui Chuang', 'Hsin-Yi Chen', 'Cheng-Yen Hsu']	2023-01-08	null	null	null	null	['camera-calibration']	['computer-vision']	[-2.40301624e-01 -1.05900936e-01 -4.90321890e-02 -3.80347520e-01 -8.65361691e-02 -7.06646979e-01 3.85576546e-01 -1.26950175e-01 -2.52637476e-01 6.16732776e-01 -3.09384912e-01 -3.70144188e-01 -2.53631920e-01 -6.46064699e-01 -5.01402140e-01 -5.26337802e-01 5.19838870e-01 3.51028144e-01 4.62793224e-02 -3.22180778e-01 5.51636100e-01 9.76791084e-01 -9.10967469e-01 -7.53084898e-01 4.84418482e-01 4.57297772e-01 -2.06338793e-01 6.23405337e-01 -1.33344889e-01 5.90882078e-02 -3.47761840e-01 -8.82717550e-01 7.83487320e-01 -3.85348707e-01 -5.87123811e-01 3.28162074e-01 5.99964201e-01 -4.19524640e-01 -1.41877428e-01 1.16354311e+00 2.89223313e-01 -2.66428828e-01 5.76002657e-01 -1.06379616e+00 -3.71580780e-01 1.61652073e-01 -7.87854016e-01 -2.56880701e-01 9.15032089e-01 -5.00420928e-01 6.85552597e-01 -7.94303119e-01 9.12017405e-01 1.04834473e+00 7.39233971e-01 1.78295240e-01 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9a94c7bd-c095-43ed-947c-d274d09b8a01	dynamic-diagnosis-of-the-progress-and	2204.13989	null	https://arxiv.org/abs/2204.13989v1	https://arxiv.org/pdf/2204.13989v1.pdf	Dynamic Diagnosis of the Progress and Shortcomings of Student Learning using Machine Learning based on Cognitive, Social, and Emotional Features	Student diversity, like academic background, learning styles, career and life goals, ethnicity, age, social and emotional characteristics, course load and work schedule, offers unique opportunities in education, like learning new skills, peer mentoring and example setting. But student diversity can be challenging too as it adds variability in the way in which students learn and progress over time. A single teaching approach is likely to be ineffective and result in students not meeting their potential. Automated support could address limitations of traditional teaching by continuously assessing student learning and implementing needed interventions. This paper discusses a novel methodology based on data analytics and Machine Learning to measure and causally diagnose the progress and shortcomings of student learning, and then utilizes the insight gained on individuals to optimize learning. Diagnosis pertains to dynamic diagnostic formative assessment, which aims to uncover the causes of learning shortcomings. The methodology groups learning difficulties into four categories: recall from memory, concept adjustment, concept modification, and problem decomposition into sub-goals (sub-problems) and concept combination. Data models are predicting the occurrence of each of the four challenge types, as well as a student's learning trajectory. The models can be used to automatically create real-time, student-specific interventions (e.g., learning cues) to address less understood concepts. We envision that the system will enable new adaptive pedagogical approaches to unleash student learning potential through customization of the course material to the background, abilities, situation, and progress of each student; and leveraging diversity-related learning experiences.	['Wendy Tang', 'Sangjin Hong', 'Ryan Duke', 'Simona Doboli', 'Alex Doboli']	2022-04-13	null	null	null	null	['problem-decomposition']	['miscellaneous']	[-1.13389656e-01 9.88947526e-02 -5.58395505e-01 -3.37639093e-01 -3.59993935e-01 -6.21661246e-01 -3.28722671e-02 1.13527286e+00 -1.18032865e-01 4.51398462e-01 4.37022775e-01 -5.78416646e-01 -9.70048666e-01 -8.65073025e-01 -1.60480499e-01 -2.70090044e-01 2.14524209e-01 5.30249059e-01 1.13112181e-01 -4.53201354e-01 7.88922548e-01 1.00822484e+00 -2.19546843e+00 1.56283632e-01 1.39294064e+00 1.89168587e-01 3.02665025e-01 5.11404395e-01 -6.58084571e-01 7.83650160e-01 -7.28119969e-01 3.05586006e-03 -4.36605066e-01 -7.34792948e-01 -6.27709091e-01 1.48980603e-01 5.30983031e-01 -3.45472753e-01 2.71302491e-01 7.41388738e-01 5.74338853e-01 4.19670910e-01 3.08344781e-01 -9.61345673e-01 -4.28638995e-01 4.80786830e-01 -1.34898826e-01 3.04025084e-01 7.39591539e-01 -1.85840800e-01 8.54323357e-02 -4.94000405e-01 3.12035859e-01 1.08475316e+00 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2416975f-47cb-4422-bb06-c74326981788	automatic-keyboard-layout-design-for-low	1901.06039	null	http://arxiv.org/abs/1901.06039v1	http://arxiv.org/pdf/1901.06039v1.pdf	Automatic Keyboard Layout Design for Low-Resource Latin-Script Languages	We present our approach to automatically designing and implementing keyboard layouts on mobile devices for typing low-resource languages written in the Latin script. For many speakers, one of the barriers in accessing and creating text content on the web is the absence of input tools for their language. Ease in typing in these languages would lower technological barriers to online communication and collaboration, likely leading to the creation of more web content. Unfortunately, it can be time-consuming to develop layouts manually even for language communities that use a keyboard layout very similar to English; starting from scratch requires many configuration files to describe multiple possible behaviors for each key. With our approach, we only need a small amount of data in each language to generate keyboard layouts with very little human effort. This process can help serve speakers of low-resource languages in a scalable way, allowing us to develop input tools for more languages. Having input tools that reflect the linguistic diversity of the world will let as many people as possible use technology to learn, communicate, and express themselves in their own native languages.	["Jeremy O'Brien", 'Daan van Esch', 'Chieu Nguyen', 'Theresa Breiner']	2019-01-18	null	null	null	null	['layout-design']	['computer-vision']	[-1.38140887e-01 -3.25131536e-01 -1.81859195e-01 -1.62025884e-01 -3.93980622e-01 -1.16648483e+00 4.72522527e-02 3.42235953e-01 -5.13165832e-01 4.30104405e-01 3.78356427e-01 -1.18955183e+00 8.76328275e-02 -7.90609658e-01 -1.75623015e-01 1.23283558e-01 6.12698495e-01 2.94011623e-01 2.48216018e-01 -4.59337205e-01 2.91307241e-01 4.23300207e-01 -1.64587831e+00 2.02657595e-01 1.07641149e+00 -8.38429928e-02 8.32952142e-01 7.73236632e-01 -8.70567739e-01 3.24075639e-01 -6.59746706e-01 -3.71439159e-01 -1.09110139e-01 -2.88548261e-01 -6.02464020e-01 -3.56190860e-01 4.26969290e-01 -4.65530545e-01 1.98415741e-01 6.68007612e-01 3.90743494e-01 -1.32039726e-01 4.35356200e-01 -5.97703755e-01 -4.69947428e-01 7.82050371e-01 -1.91022500e-01 -2.23448798e-01 8.75360072e-01 9.71072018e-02 5.35651267e-01 -6.88691080e-01 6.78001523e-01 1.09413910e+00 8.08609009e-01 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6451a013-26fd-4f72-9f63-a90dd8ca0fa5	structure-pretraining-and-prompt-tuning-for	2303.03922	null	https://arxiv.org/abs/2303.03922v1	https://arxiv.org/pdf/2303.03922v1.pdf	Structure Pretraining and Prompt Tuning for Knowledge Graph Transfer	Knowledge graphs (KG) are essential background knowledge providers in many tasks. When designing models for KG-related tasks, one of the key tasks is to devise the Knowledge Representation and Fusion (KRF) module that learns the representation of elements from KGs and fuses them with task representations. While due to the difference of KGs and perspectives to be considered during fusion across tasks, duplicate and ad hoc KRF modules design are conducted among tasks. In this paper, we propose a novel knowledge graph pretraining model KGTransformer that could serve as a uniform KRF module in diverse KG-related tasks. We pretrain KGTransformer with three self-supervised tasks with sampled sub-graphs as input. For utilization, we propose a general prompt-tuning mechanism regarding task data as a triple prompt to allow flexible interactions between task KGs and task data. We evaluate pretrained KGTransformer on three tasks, triple classification, zero-shot image classification, and question answering. KGTransformer consistently achieves better results than specifically designed task models. Through experiments, we justify that the pretrained KGTransformer could be used off the shelf as a general and effective KRF module across KG-related tasks. The code and datasets are available at https://github.com/zjukg/KGTransformer.	['Huajun Chen', 'Wenting Song', 'Yajing Xu', 'Yufeng Huang', 'Yuxia Geng', 'Mingyang Chen', 'Yushan Zhu', 'Wen Zhang']	2023-03-03	null	null	null	null	['triple-classification']	['graphs']	[ 1.32818744e-01 3.17064941e-01 -2.36155272e-01 -4.56377685e-01 -5.06398141e-01 -2.58320093e-01 4.83720332e-01 1.80228025e-01 -2.91048259e-01 4.11396384e-01 1.95904747e-01 -1.41574666e-01 -3.92203659e-01 -8.09490144e-01 -8.40835929e-01 -3.71060610e-01 3.39520872e-01 3.71684998e-01 3.24010700e-01 -1.27690107e-01 4.72548157e-02 -3.50736156e-02 -1.73146176e+00 6.82410777e-01 1.28732383e+00 1.18282628e+00 6.47320628e-01 4.96335208e-01 -3.73937786e-01 9.85982656e-01 -5.07945061e-01 -7.28854537e-01 1.21546336e-01 -1.51999623e-01 -1.14812768e+00 -6.43042848e-02 6.34717047e-01 3.00062355e-02 -1.51180580e-01 8.17789972e-01 5.94558835e-01 3.69297832e-01 8.85678887e-01 -1.64304352e+00 -1.14081705e+00 1.02635467e+00 -1.98094591e-01 2.31773350e-02 1.39317632e-01 7.43551031e-02 1.07336223e+00 -1.08860028e+00 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3f031b40-c05b-4eaf-ab3f-414095cac5df	bayesian-and-neural-inference-on-lstm-based	2306.06423	null	https://arxiv.org/abs/2306.06423v1	https://arxiv.org/pdf/2306.06423v1.pdf	Bayesian and Neural Inference on LSTM-based Object Recognition from Tactile and Kinesthetic Information	Recent advances in the field of intelligent robotic manipulation pursue providing robotic hands with touch sensitivity. Haptic perception encompasses the sensing modalities encountered in the sense of touch (e.g., tactile and kinesthetic sensations). This letter focuses on multimodal object recognition and proposes analytical and data-driven methodologies to fuse tactile- and kinesthetic-based classification results. The procedure is as follows: a three-finger actuated gripper with an integrated high-resolution tactile sensor performs squeeze-and-release Exploratory Procedures (EPs). The tactile images and kinesthetic information acquired using angular sensors on the finger joints constitute the time-series datasets of interest. Each temporal dataset is fed to a Long Short-term Memory (LSTM) Neural Network, which is trained to classify in-hand objects. The LSTMs provide an estimation of the posterior probability of each object given the corresponding measurements, which after fusion allows to estimate the object through Bayesian and Neural inference approaches. An experiment with 36-classes is carried out to evaluate and compare the performance of the fused, tactile, and kinesthetic perception systems.The results show that the Bayesian-based classifiers improves capabilities for object recognition and outperforms the Neural-based approach.	['Jesús M. Gómez-de-Gabriel', 'Alfonso J. García-Cerezo', 'Pau Closas', 'Daniel Medina', 'Juan M. Gandarias', 'Jorge García-González', 'Francisco Pastor']	2023-06-10	null	null	null	null	['object-recognition']	['computer-vision']	[ 3.61725569e-01 -2.50021219e-01 -1.21327467e-01 -2.05738872e-01 -5.48152566e-01 -1.32987633e-01 3.93196732e-01 -2.54630387e-01 -6.28781676e-01 5.94328344e-01 -3.30777645e-01 8.34892988e-02 -7.97769129e-01 -6.09691858e-01 -7.32435226e-01 -1.03042555e+00 -7.51130506e-02 3.52679342e-01 6.26777261e-02 2.40166083e-01 5.50198793e-01 3.47403914e-01 -2.08057213e+00 3.79149616e-01 7.40395129e-01 1.95825052e+00 9.49786365e-01 8.09268951e-01 -2.81627446e-01 1.31014362e-01 -3.42527241e-01 3.94974314e-02 -3.42467017e-02 2.15473801e-01 -3.42141151e-01 -3.88475060e-01 -1.52532579e-02 -5.60729623e-01 -6.39047101e-02 9.33260381e-01 6.16944909e-01 2.36396030e-01 1.13401687e+00 -8.97237241e-01 -8.35695803e-01 5.24653196e-01 -1.33924529e-01 -4.90723908e-01 2.67881781e-01 -1.44690424e-01 4.59749609e-01 -1.04785156e+00 3.01923424e-01 1.38012028e+00 2.62644827e-01 3.81776452e-01 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7d0bf5ab-3605-4af2-8797-93252ca4a4b1	adversarial-learning-for-improved-onsets-and	1906.08512	null	https://arxiv.org/abs/1906.08512v1	https://arxiv.org/pdf/1906.08512v1.pdf	Adversarial Learning for Improved Onsets and Frames Music Transcription	Automatic music transcription is considered to be one of the hardest problems in music information retrieval, yet recent deep learning approaches have achieved substantial improvements on transcription performance. These approaches commonly employ supervised learning models that predict various time-frequency representations, by minimizing element-wise losses such as the cross entropy function. However, applying the loss in this manner assumes conditional independence of each label given the input, and thus cannot accurately express inter-label dependencies. To address this issue, we introduce an adversarial training scheme that operates directly on the time-frequency representations and makes the output distribution closer to the ground-truth. Through adversarial learning, we achieve a consistent improvement in both frame-level and note-level metrics over Onsets and Frames, a state-of-the-art music transcription model. Our results show that adversarial learning can significantly reduce the error rate while increasing the confidence of the model estimations. Our approach is generic and applicable to any transcription model based on multi-label predictions, which are very common in music signal analysis.	['Juan Pablo Bello', 'Jong Wook Kim']	2019-06-20	null	null	null	null	['music-transcription']	['music']	[ 4.74845529e-01 -7.93496743e-02 -1.50623098e-01 -2.30913565e-01 -1.45663452e+00 -7.98964441e-01 2.97092736e-01 -1.84382927e-02 -8.68586972e-02 5.04618585e-01 2.10068330e-01 2.09450826e-01 -1.50063455e-01 -5.84622085e-01 -7.50649393e-01 -7.59548843e-01 -2.13315301e-02 3.56465608e-01 -3.27865809e-01 1.65451355e-02 2.31484175e-02 1.44145578e-01 -1.33285260e+00 5.36977172e-01 5.20462394e-01 1.19848752e+00 -3.50011677e-01 7.16842771e-01 2.69311816e-01 8.93425345e-01 -8.27515364e-01 -6.69902921e-01 2.32922137e-01 -6.48290038e-01 -7.10860014e-01 -1.94361836e-01 5.12806475e-01 9.08611342e-02 -5.20876169e-01 1.06732345e+00 7.57509351e-01 1.70451954e-01 8.89718354e-01 -1.11537576e+00 -3.42117667e-01 6.83072090e-01 -3.50065023e-01 -1.56865567e-01 3.85639608e-01 -2.01243803e-01 1.43559670e+00 -7.30736971e-01 2.91544110e-01 9.92063761e-01 1.06289947e+00 2.62945652e-01 -1.51095569e+00 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7.23599374e-01 4.28536057e-01 -6.11037374e-01 -3.38959098e-01 4.60109979e-01]	[15.700902938842773, 5.36078405380249]
8e6197d2-9a6c-4051-a752-9b6bf16c9794	object-cosegmentation-using-deep-siamese	1803.02555	null	http://arxiv.org/abs/1803.02555v2	http://arxiv.org/pdf/1803.02555v2.pdf	Object cosegmentation using deep Siamese network	Object cosegmentation addresses the problem of discovering similar objects from multiple images and segmenting them as foreground simultaneously. In this paper, we propose a novel end-to-end pipeline to segment the similar objects simultaneously from relevant set of images using supervised learning via deep-learning framework. We experiment with multiple set of object proposal generation techniques and perform extensive numerical evaluations by training the Siamese network with generated object proposals. Similar objects proposals for the test images are retrieved using the ANNOY (Approximate Nearest Neighbor) library and deep semantic segmentation is performed on them. Finally, we form a collage from the segmented similar objects based on the relative importance of the objects.	['Snehith Lattupally', 'Brejesh lall', 'Prerana Mukherjee']	2018-03-07	null	null	null	null	['object-proposal-generation']	['computer-vision']	[ 2.27152199e-01 5.14528826e-02 -9.42803845e-02 -7.35515535e-01 -1.42714083e+00 -6.51608586e-01 3.65292937e-01 2.20682904e-01 -6.58769965e-01 5.18264890e-01 -2.02652469e-01 4.65041250e-01 -1.94551438e-01 -4.40835416e-01 -9.05765951e-01 -5.35002768e-01 -1.73204362e-01 1.23018885e+00 1.10901546e+00 4.57763731e-01 6.38964832e-01 7.59809196e-01 -1.41766977e+00 3.84420186e-01 6.93397820e-01 9.43250120e-01 4.39946413e-01 8.55190277e-01 -2.83018053e-01 3.87032688e-01 -8.75228047e-01 -4.11985308e-01 6.13827944e-01 -3.04757237e-01 -1.23888540e+00 3.90752971e-01 1.14531004e+00 -5.33578634e-01 1.93040207e-01 1.19247985e+00 2.57108122e-01 4.93520051e-01 7.48803377e-01 -1.36735928e+00 -6.98054060e-02 5.92881739e-01 -8.71883094e-01 6.40456378e-01 -1.05917171e-01 1.38004735e-01 8.84069026e-01 -1.28300047e+00 7.46717632e-01 1.50689816e+00 3.69442374e-01 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-5.51277995e-01 -4.10705417e-01 4.55007315e-01 1.17149197e-01 -3.55848789e-01 8.80545825e-02 2.28332683e-01]	[9.33638858795166, 0.4514539837837219]
906442a4-0fef-40db-a4ee-99ff43cf5fcb	differentiable-iterative-surface-normal	1904.07172	null	https://arxiv.org/abs/1904.07172v3	https://arxiv.org/pdf/1904.07172v3.pdf	Deep Iterative Surface Normal Estimation	This paper presents an end-to-end differentiable algorithm for robust and detail-preserving surface normal estimation on unstructured point-clouds. We utilize graph neural networks to iteratively parameterize an adaptive anisotropic kernel that produces point weights for weighted least-squares plane fitting in local neighborhoods. The approach retains the interpretability and efficiency of traditional sequential plane fitting while benefiting from adaptation to data set statistics through deep learning. This results in a state-of-the-art surface normal estimator that is robust to noise, outliers and point density variation, preserves sharp features through anisotropic kernels and equivariance through a local quaternion-based spatial transformer. Contrary to previous deep learning methods, the proposed approach does not require any hand-crafted features or preprocessing. It improves on the state-of-the-art results while being more than two orders of magnitude faster and more parameter efficient.	['Christian Osendorfer', 'Jan Eric Lenssen', 'Jonathan Masci']	2019-04-15	deep-iterative-surface-normal-estimation	http://openaccess.thecvf.com/content_CVPR_2020/html/Lenssen_Deep_Iterative_Surface_Normal_Estimation_CVPR_2020_paper.html	http://openaccess.thecvf.com/content_CVPR_2020/papers/Lenssen_Deep_Iterative_Surface_Normal_Estimation_CVPR_2020_paper.pdf	cvpr-2020-6	['surface-normals-estimation']	['computer-vision']	[ 2.42239255e-02 4.21073027e-02 8.85856375e-02 -3.14976066e-01 -8.03582668e-01 -2.66490132e-01 5.94919562e-01 2.86318332e-01 -5.30377865e-01 3.58670115e-01 -4.47403818e-01 -1.62283212e-01 -3.30128491e-01 -8.34042847e-01 -1.02462590e+00 -6.29718006e-01 -3.06386024e-01 9.41705763e-01 3.27491075e-01 -2.98405707e-01 6.21972561e-01 1.19915342e+00 -1.38672996e+00 -3.18274468e-01 8.07259500e-01 1.08811402e+00 -4.14239168e-01 5.95812201e-01 5.92711978e-02 2.47898530e-02 -5.39038368e-02 -3.18470746e-01 3.56834203e-01 3.87578934e-01 -3.83216739e-01 1.82293430e-01 1.08909595e+00 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aca9432e-4b6d-40d4-b3f1-9a224e163e31	mind-at-semeval-2021-task-6-propaganda	null	null	https://aclanthology.org/2021.semeval-1.150	https://aclanthology.org/2021.semeval-1.150.pdf	MinD at SemEval-2021 Task 6: Propaganda Detection using Transfer Learning and Multimodal Fusion	We describe our systems of subtask1 and subtask3 for SemEval-2021 Task 6 on Detection of Persuasion Techniques in Texts and Images. The purpose of subtask1 is to identify propaganda techniques given textual content, and the goal of subtask3 is to detect them given both textual and visual content. For subtask1, we investigate transfer learning based on pre-trained language models (PLMs) such as BERT, RoBERTa to solve data sparsity problems. For subtask3, we extract heterogeneous visual representations (i.e., face features, OCR features, and multimodal representations) and explore various multimodal fusion strategies to combine the textual and visual representations. The official evaluation shows our ensemble model ranks 1st for subtask1 and 2nd for subtask3.	['Wenming Xiao', 'Ming Yan', 'Chenliang Li', 'Min Gui', 'Junfeng Tian']	2021-08-01	null	null	null	semeval-2021	['propaganda-detection']	['natural-language-processing']	[ 4.88070816e-01 5.75029291e-02 -2.79058486e-01 -2.57530212e-01 -1.05313349e+00 -4.02062863e-01 1.31257057e+00 7.05866814e-02 -4.52705622e-01 4.18780923e-01 5.55347919e-01 -6.17534280e-01 2.91189700e-01 -1.26278684e-01 -5.33164620e-01 -3.88409764e-01 4.16130483e-01 -3.08696628e-02 -1.63141549e-01 -1.92928284e-01 7.81580746e-01 2.56486177e-01 -1.20058775e+00 1.26929986e+00 6.18579388e-01 9.94245648e-01 -8.98920447e-02 1.06827629e+00 -2.00846359e-01 1.51653290e+00 -7.32351601e-01 -6.95937097e-01 -2.85958469e-01 -6.37994707e-01 -1.03059042e+00 2.55611390e-01 9.24368262e-01 -2.19840720e-01 -3.51074815e-01 1.03992522e+00 4.46266413e-01 -2.44377200e-02 1.37623441e+00 -1.21249807e+00 -1.03487134e+00 6.43827498e-01 -1.03788018e+00 3.05263132e-01 5.84380984e-01 1.61996439e-01 7.82503009e-01 -1.32178712e+00 6.19731128e-01 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b51d1447-404a-4f5d-a844-d370abda5d0d	emotion-prediction-oriented-method-with	2302.12417	null	https://arxiv.org/abs/2302.12417v1	https://arxiv.org/pdf/2302.12417v1.pdf	Emotion Prediction Oriented method with Multiple Supervisions for Emotion-Cause Pair Extraction	Emotion-cause pair extraction (ECPE) task aims to extract all the pairs of emotions and their causes from an unannotated emotion text. The previous works usually extract the emotion-cause pairs from two perspectives of emotion and cause. However, emotion extraction is more crucial to the ECPE task than cause extraction. Motivated by this analysis, we propose an end-to-end emotion-cause extraction approach oriented toward emotion prediction (EPO-ECPE), aiming to fully exploit the potential of emotion prediction to enhance emotion-cause pair extraction. Considering the strong dependence between emotion prediction and emotion-cause pair extraction, we propose a synchronization mechanism to share their improvement in the training process. That is, the improvement of emotion prediction can facilitate the emotion-cause pair extraction, and then the results of emotion-cause pair extraction can also be used to improve the accuracy of emotion prediction simultaneously. For the emotion-cause pair extraction, we divide it into genuine pair supervision and fake pair supervision, where the genuine pair supervision learns from the pairs with more possibility to be emotion-cause pairs. In contrast, fake pair supervision learns from other pairs. In this way, the emotion-cause pairs can be extracted directly from the genuine pair, thereby reducing the difficulty of extraction. Experimental results show that our approach outperforms the 13 compared systems and achieves new state-of-the-art performance.	['Guangming Lu', 'Yi Zhao', 'Guimin Hu']	2023-02-24	null	null	null	null	['emotion-cause-pair-extraction', 'emotion-cause-extraction']	['natural-language-processing', 'natural-language-processing']	[ 4.81398068e-02 3.76476884e-01 -1.13278575e-01 -5.77987075e-01 -6.87513769e-01 -5.73937654e-01 4.47224796e-01 1.00675877e-02 3.31309182e-03 7.42803693e-01 2.11149350e-01 3.04896891e-01 7.42380992e-02 -5.77482104e-01 -3.07035834e-01 -7.14856625e-01 4.48722616e-02 -8.46909359e-02 -3.22262585e-01 -3.42630506e-01 -2.86789387e-02 1.40251532e-01 -1.58189964e+00 5.12685478e-01 8.42614710e-01 1.23218155e+00 -3.66357446e-01 3.32495838e-01 -2.60274976e-01 1.21146870e+00 -7.62439787e-01 -6.08055711e-01 5.19053005e-02 -8.95256639e-01 -7.20988035e-01 -9.20837298e-02 -3.03361505e-01 -2.14405224e-01 8.95089880e-02 1.03603649e+00 4.23220664e-01 -1.22874454e-01 5.98505497e-01 -2.02061224e+00 -1.63830042e-01 5.49150586e-01 -4.71655667e-01 -2.66249418e-01 5.43971956e-01 -2.60158390e-01 1.21207607e+00 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6.46258414e-01 1.22382379e+00 -6.30922377e-01 1.75107419e-02 -8.73742998e-01 -6.16087496e-01 7.09213078e-01 1.96633175e-01 -5.51724434e-01 -5.56329787e-01 5.05252220e-02]	[12.628851890563965, 6.212127208709717]
8522b746-ed09-475c-9fb1-e3f0abbe8386	bareskinnet-de-makeup-and-de-lighting-via-3d	2209.09029	null	https://arxiv.org/abs/2209.09029v1	https://arxiv.org/pdf/2209.09029v1.pdf	BareSkinNet: De-makeup and De-lighting via 3D Face Reconstruction	We propose BareSkinNet, a novel method that simultaneously removes makeup and lighting influences from the face image. Our method leverages a 3D morphable model and does not require a reference clean face image or a specified light condition. By combining the process of 3D face reconstruction, we can easily obtain 3D geometry and coarse 3D textures. Using this information, we can infer normalized 3D face texture maps (diffuse, normal, roughness, and specular) by an image-translation network. Consequently, reconstructed 3D face textures without undesirable information will significantly benefit subsequent processes, such as re-lighting or re-makeup. In experiments, we show that BareSkinNet outperforms state-of-the-art makeup removal methods. In addition, our method is remarkably helpful in removing makeup to generate consistent high-fidelity texture maps, which makes it extendable to many realistic face generation applications. It can also automatically build graphic assets of face makeup images before and after with corresponding 3D data. This will assist artists in accelerating their work, such as 3D makeup avatar creation.	['Takafumi Taketomi', 'Xingchao Yang']	2022-09-19	null	null	null	null	['3d-face-reconstruction', 'face-generation', 'face-reconstruction']	['computer-vision', 'computer-vision', 'computer-vision']	[ 3.72699142e-01 8.07981566e-02 3.32810789e-01 -4.09184605e-01 -3.90681297e-01 -4.44104046e-01 5.22391915e-01 -6.83663428e-01 4.37184662e-01 3.88460368e-01 2.47054294e-01 3.46934721e-02 1.80108905e-01 -1.08272338e+00 -8.07350695e-01 -5.94034493e-01 5.06852508e-01 4.24478054e-01 -2.63258487e-01 -3.51254791e-01 9.78381112e-02 9.45935071e-01 -1.96163356e+00 2.49822915e-01 7.15308905e-01 9.83526826e-01 -7.48120472e-02 4.35881644e-01 -3.18718612e-01 2.43910506e-01 -3.49514753e-01 -5.76791704e-01 5.05047798e-01 -4.50803667e-01 -9.54749882e-02 3.46821308e-01 8.62933099e-01 -5.42865753e-01 7.50752389e-02 8.50717843e-01 5.80909610e-01 -2.78659575e-02 7.46218026e-01 -8.99500787e-01 -9.49011445e-01 -6.34104013e-02 -9.88546252e-01 -7.98771679e-01 5.63058794e-01 1.30836248e-01 2.76001543e-01 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1956d7a1-2e0c-4adb-8b00-37ba6d584342	attending-to-entities-for-better-text	1911.04361	null	https://arxiv.org/abs/1911.04361v1	https://arxiv.org/pdf/1911.04361v1.pdf	Attending to Entities for Better Text Understanding	Recent progress in NLP witnessed the development of large-scale pre-trained language models (GPT, BERT, XLNet, etc.) based on Transformer (Vaswani et al. 2017), and in a range of end tasks, such models have achieved state-of-the-art results, approaching human performance. This demonstrates the power of the stacked self-attention architecture when paired with a sufficient number of layers and a large amount of pre-training data. However, on tasks that require complex and long-distance reasoning where surface-level cues are not enough, there is still a large gap between the pre-trained models and human performance. Strubell et al. (2018) recently showed that it is possible to inject knowledge of syntactic structure into a model through supervised self-attention. We conjecture that a similar injection of semantic knowledge, in particular, coreference information, into an existing model would improve performance on such complex problems. On the LAMBADA (Paperno et al. 2016) task, we show that a model trained from scratch with coreference as auxiliary supervision for self-attention outperforms the largest GPT-2 model, setting the new state-of-the-art, while only containing a tiny fraction of parameters compared to GPT-2. We also conduct a thorough analysis of different variants of model architectures and supervision configurations, suggesting future directions on applying similar techniques to other problems.	['Katrin Erk', 'Pengxiang Cheng']	2019-11-11	null	null	null	null	['lambada']	['natural-language-processing']	[ 1.41477883e-01 7.97618747e-01 -2.13144436e-01 -3.99148554e-01 -1.05891168e+00 -6.56924844e-01 8.92858803e-01 1.76432714e-01 -5.40403783e-01 6.37600899e-01 6.46611214e-01 -5.84686577e-01 -6.18568212e-02 -5.93047678e-01 -1.06252861e+00 -2.97572732e-01 1.79874208e-02 9.97231364e-01 3.86809081e-01 -5.67667723e-01 5.34081645e-02 2.05743492e-01 -1.28309381e+00 4.66286242e-01 8.33495319e-01 6.83418572e-01 3.68926883e-01 5.46553612e-01 -1.88751966e-01 8.60129356e-01 -4.13430542e-01 -6.64873719e-01 1.63326249e-01 -1.12079844e-01 -1.19950819e+00 -4.14077044e-01 7.94013679e-01 -5.06385714e-02 -3.33951890e-01 7.09219396e-01 3.79417539e-01 2.00916544e-01 4.03185368e-01 -9.50726271e-01 -9.00191307e-01 1.09394884e+00 -3.54619294e-01 2.49317482e-01 1.60556152e-01 2.31984198e-01 1.40177536e+00 -8.32294762e-01 6.01724207e-01 1.49440062e+00 8.21734667e-01 5.35571516e-01 -1.36799252e+00 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-1.33124495e+00 -1.73098594e-01 4.10167634e-01 3.01612467e-01 -6.82039261e-01 -4.91584301e-01 -2.78911740e-01]	[10.605146408081055, 8.569756507873535]
2b474f27-1373-4488-b934-5b1a0d2800b9	efficient-signed-graph-sampling-via-balancing	2208.08726	null	https://arxiv.org/abs/2208.08726v2	https://arxiv.org/pdf/2208.08726v2.pdf	Efficient Signed Graph Sampling via Balancing & Gershgorin Disc Perfect Alignment	A basic premise in graph signal processing (GSP) is that a graph encoding pairwise (anti-)correlations of the targeted signal as edge weights is exploited for graph filtering. However, existing fast graph sampling schemes are designed and tested only for positive graphs describing positive correlations. In this paper, we show that for datasets with strong inherent anti-correlations, a suitable graph contains both positive and negative edge weights. In response, we propose a linear-time signed graph sampling method centered on the concept of balanced signed graphs. Specifically, given an empirical covariance data matrix $\bar{\bf{C}}$, we first learn a sparse inverse matrix (graph Laplacian) $\mathcal{L}$ corresponding to a signed graph $\mathcal{G}$. We define the eigenvectors of Laplacian $\mathcal{L}_B$ for a balanced signed graph $\mathcal{G}_B$ -- approximating $\mathcal{G}$ via edge weight augmentation -- as graph frequency components. Next, we choose samples to minimize the low-pass filter reconstruction error in two steps. We first align all Gershgorin disc left-ends of Laplacian $\mathcal{L}_B$ at smallest eigenvalue $\lambda_{\min}(\mathcal{L}_B)$ via similarity transform $\mathcal{L}_p = \S \mathcal{L}_B \S^{-1}$, leveraging a recent linear algebra theorem called Gershgorin disc perfect alignment (GDPA). We then perform sampling on $\mathcal{L}_p$ using a previous fast Gershgorin disc alignment sampling (GDAS) scheme. Experimental results show that our signed graph sampling method outperformed existing fast sampling schemes noticeably on various datasets.	['Ivan V. Bajic', 'Saghar Bagheri', 'Gene Cheung', 'Chinthaka Dinesh']	2022-08-18	null	null	null	null	['graph-sampling']	['graphs']	[ 3.17326903e-01 4.52814639e-01 6.66603297e-02 -1.17408350e-01 -8.39677453e-01 -5.67494869e-01 -3.35060060e-02 1.29318967e-01 8.67820904e-02 6.36864781e-01 1.83309484e-02 -3.13229799e-01 -9.07319903e-01 -9.82960582e-01 -8.06118608e-01 -7.88089156e-01 -9.53248203e-01 1.24657042e-01 -1.99775957e-02 -4.02773499e-01 1.05021648e-01 2.46919751e-01 -1.25865602e+00 4.85047558e-03 9.68924582e-01 1.09330165e+00 -5.37111750e-03 7.75174856e-01 1.73940673e-01 3.32244486e-01 -4.37592596e-01 -5.43724775e-01 5.83303809e-01 -9.65478897e-01 -4.53873396e-01 3.09218559e-02 6.33104801e-01 4.72376764e-01 -6.30468607e-01 1.67615521e+00 3.97655219e-01 9.24059004e-02 8.59580457e-01 -1.12835157e+00 -7.37183273e-01 8.11577082e-01 -1.17211330e+00 1.34417310e-01 3.72669190e-01 1.04909755e-01 1.41725469e+00 -8.50646973e-01 6.54287815e-01 1.00199926e+00 9.41912174e-01 6.03700690e-02 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a8c14469-be09-4f24-b355-a49a1538d1aa	trainable-class-prototypes-for-few-shot	2106.10846	null	https://arxiv.org/abs/2106.10846v1	https://arxiv.org/pdf/2106.10846v1.pdf	Trainable Class Prototypes for Few-Shot Learning	Metric learning is a widely used method for few shot learning in which the quality of prototypes plays a key role in the algorithm. In this paper we propose the trainable prototypes for distance measure instead of the artificial ones within the meta-training and task-training framework. Also to avoid the disadvantages that the episodic meta-training brought, we adopt non-episodic meta-training based on self-supervised learning. Overall we solve the few-shot tasks in two phases: meta-training a transferable feature extractor via self-supervised learning and training the prototypes for metric classification. In addition, the simple attention mechanism is used in both meta-training and task-training. Our method achieves state-of-the-art performance in a variety of established few-shot tasks on the standard few-shot visual classification dataset, with about 20% increase compared to the available unsupervised few-shot learning methods.	['Guizhong Liu', 'Jianyi Li']	2021-06-21	null	null	null	null	['unsupervised-few-shot-learning', 'unsupervised-few-shot-image-classification']	['computer-vision', 'computer-vision']	[ 1.12660795e-01 -1.49476141e-01 -2.53922999e-01 -4.34737206e-01 -9.04648066e-01 2.47176364e-01 8.10701311e-01 1.40258506e-01 -6.56635821e-01 5.90096891e-01 1.35399103e-01 4.86057669e-01 -3.27278256e-01 -9.41586852e-01 -4.00028139e-01 -6.17796004e-01 8.95489305e-02 5.54950237e-01 5.06547034e-01 -2.23233387e-01 4.72869039e-01 -3.79082258e-03 -2.26949763e+00 2.23729610e-01 9.28503036e-01 1.10688734e+00 3.63502800e-01 6.25453472e-01 -2.25668818e-01 1.07158303e+00 -4.93394792e-01 -2.91433662e-01 4.46272828e-03 -7.78937817e-01 -6.17056608e-01 7.77982846e-02 3.15526634e-01 -3.26192886e-01 -3.67879182e-01 1.01230729e+00 6.89645886e-01 7.79219568e-01 9.50903356e-01 -1.17051291e+00 -8.04694474e-01 2.90617377e-01 -3.32303703e-01 3.84276956e-01 1.39771983e-01 2.41491608e-02 1.04814124e+00 -1.32298422e+00 6.52847052e-01 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ecb0303d-0138-4cd2-b90e-80ad76323293	movie-summarization-via-sparse-graph	2012.07536	null	https://arxiv.org/abs/2012.07536v1	https://arxiv.org/pdf/2012.07536v1.pdf	Movie Summarization via Sparse Graph Construction	We summarize full-length movies by creating shorter videos containing their most informative scenes. We explore the hypothesis that a summary can be created by assembling scenes which are turning points (TPs), i.e., key events in a movie that describe its storyline. We propose a model that identifies TP scenes by building a sparse movie graph that represents relations between scenes and is constructed using multimodal information. According to human judges, the summaries created by our approach are more informative and complete, and receive higher ratings, than the outputs of sequence-based models and general-purpose summarization algorithms. The induced graphs are interpretable, displaying different topology for different movie genres.	['Mirella Lapata', 'Frank Keller', 'Pinelopi Papalampidi']	2020-12-14	null	null	null	null	['turning-point-identification']	['natural-language-processing']	[ 2.53082812e-01 2.27225184e-01 -2.05253467e-01 -5.72147012e-01 -6.05621934e-01 -1.02487636e+00 7.44936347e-01 5.08565724e-01 2.65158355e-01 7.17716217e-01 1.51991725e+00 5.43641329e-01 -1.56170428e-01 -4.02182192e-01 -7.33055115e-01 -2.83427030e-01 -7.31027946e-02 2.77788728e-01 4.75093834e-02 1.43635627e-02 5.77361166e-01 1.67961374e-01 -1.54520547e+00 1.10957360e+00 5.51924109e-01 6.20837510e-01 3.41284126e-01 9.78801250e-01 -2.35984519e-01 1.31024718e+00 -1.06117463e+00 -7.22206891e-01 -2.61605769e-01 -9.92847323e-01 -6.90693021e-01 7.65002847e-01 7.45179236e-01 -2.51325488e-01 -4.31460619e-01 1.00249529e+00 -1.68117706e-03 5.42406976e-01 7.07391620e-01 -9.92973208e-01 -5.99593401e-01 1.08382249e+00 -2.83371210e-01 1.58472344e-01 1.24723792e+00 -1.50059342e-01 1.37820864e+00 -7.75321007e-01 1.67157495e+00 1.31825686e+00 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a4c6b16b-0df4-47aa-bdf8-471f57d7c302	robust-double-encoder-network-for-rgb-d	2210.02834	null	https://arxiv.org/abs/2210.02834v2	https://arxiv.org/pdf/2210.02834v2.pdf	Robust Double-Encoder Network for RGB-D Panoptic Segmentation	Perception is crucial for robots that act in real-world environments, as autonomous systems need to see and understand the world around them to act properly. Panoptic segmentation provides an interpretation of the scene by computing a pixelwise semantic label together with instance IDs. In this paper, we address panoptic segmentation using RGB-D data of indoor scenes. We propose a novel encoder-decoder neural network that processes RGB and depth separately through two encoders. The features of the individual encoders are progressively merged at different resolutions, such that the RGB features are enhanced using complementary depth information. We propose a novel merging approach called ResidualExcite, which reweighs each entry of the feature map according to its importance. With our double-encoder architecture, we are robust to missing cues. In particular, the same model can train and infer on RGB-D, RGB-only, and depth-only input data, without the need to train specialized models. We evaluate our method on publicly available datasets and show that our approach achieves superior results compared to other common approaches for panoptic segmentation.	['Cyrill Stachniss', 'Jens Behley', 'Tiziano Guadagnino', 'Federico Magistri', 'Matteo Sodano']	2022-10-06	null	null	null	null	['panoptic-segmentation']	['computer-vision']	[ 5.42878866e-01 3.38324085e-02 1.40304416e-01 -6.62255108e-01 -4.55085486e-01 -7.12411821e-01 4.85365480e-01 2.75734842e-01 -6.76400721e-01 3.53608549e-01 7.53710717e-02 -1.05371036e-01 1.35646135e-01 -1.11411250e+00 -8.02351654e-01 -6.33104622e-01 2.39288688e-01 3.95753086e-01 5.73784888e-01 1.22320959e-02 1.87071025e-01 4.21767563e-01 -1.82240391e+00 2.68926978e-01 7.78677285e-01 1.48149705e+00 5.96348822e-01 9.31363583e-01 -1.83916353e-02 7.38906562e-01 -4.42050219e-01 -5.82574606e-02 5.87732434e-01 -1.91223890e-01 -8.32718015e-01 6.17358446e-01 4.60458249e-01 -5.98128915e-01 -3.21336746e-01 1.01754773e+00 2.91149225e-02 -1.48614282e-02 3.47139984e-01 -8.42548370e-01 -2.95054168e-01 7.77703002e-02 -4.38956529e-01 -1.73209220e-01 1.21268697e-01 2.13040754e-01 1.08646393e+00 -4.51806009e-01 4.22419876e-01 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deba0b2f-dc36-48c0-9e05-10ea63c02d0b	boundary-adversarial-examples-against	2211.14088	null	https://arxiv.org/abs/2211.14088v1	https://arxiv.org/pdf/2211.14088v1.pdf	Boundary Adversarial Examples Against Adversarial Overfitting	Standard adversarial training approaches suffer from robust overfitting where the robust accuracy decreases when models are adversarially trained for too long. The origin of this problem is still unclear and conflicting explanations have been reported, i.e., memorization effects induced by large loss data or because of small loss data and growing differences in loss distribution of training samples as the adversarial training progresses. Consequently, several mitigation approaches including early stopping, temporal ensembling and weight perturbations on small loss data have been proposed to mitigate the effect of robust overfitting. However, a side effect of these strategies is a larger reduction in clean accuracy compared to standard adversarial training. In this paper, we investigate if these mitigation approaches are complimentary to each other in improving adversarial training performance. We further propose the use of helper adversarial examples that can be obtained with minimal cost in the adversarial example generation, and show how they increase the clean accuracy in the existing approaches without compromising the robust accuracy.	['Beat Buesser', 'Muhammad Zaid Hameed']	2022-11-25	null	null	null	null	['memorization']	['natural-language-processing']	[ 2.63848633e-01 2.59684265e-01 2.69010067e-01 -2.38832667e-01 -8.53803158e-01 -7.52532542e-01 5.59039533e-01 2.60879815e-01 -4.08254743e-01 1.19058895e+00 -8.02891795e-03 -9.89537165e-02 -8.15689787e-02 -8.94763231e-01 -9.71381426e-01 -7.72079647e-01 1.58572675e-05 7.15383813e-02 2.70139724e-01 -3.20304871e-01 1.51041478e-01 3.51604491e-01 -1.34205222e+00 1.43470213e-01 1.23106861e+00 7.12784410e-01 -3.69797856e-01 6.58330202e-01 -9.77071524e-02 8.04949701e-01 -1.11562097e+00 -6.81631982e-01 6.29162073e-01 -5.27848780e-01 -4.94139612e-01 -2.54986167e-01 4.81331170e-01 -2.89439499e-01 -1.88795909e-01 1.18103421e+00 7.17581928e-01 1.35553211e-01 6.09946489e-01 -1.31521213e+00 -5.10196686e-01 6.96398377e-01 -3.87712270e-01 3.29300091e-02 1.65210485e-01 1.72562614e-01 4.42771435e-01 -5.94764054e-01 4.16285694e-01 1.04521465e+00 1.09092617e+00 8.57147932e-01 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91f31922-fed3-49bb-a2a6-419e582283ff	deep-learning-in-lexical-analysis-and-parsing	null	null	https://aclanthology.org/I17-5001	https://aclanthology.org/I17-5001.pdf	Deep Learning in Lexical Analysis and Parsing	Neural networks, also with a fancy name deep learning, just right can overcome the above {``}feature engineering{''} problem. In theory, they can use non-linear activation functions and multiple layers to automatically find useful features. The novel network structures, such as convolutional or recurrent, help to reduce the difficulty further. These deep learning models have been successfully used for lexical analysis and parsing. In this tutorial, we will give a review of each line of work, by contrasting them with traditional statistical methods, and organizing them in consistent orders.	['Yue Zhang', 'Wanxiang Che']	2017-11-01	deep-learning-in-lexical-analysis-and-parsing-1	https://aclanthology.org/I17-5001	https://aclanthology.org/I17-5001.pdf	ijcnlp-2017-11	['lexical-analysis']	['natural-language-processing']	[-1.52575478e-01 2.43565246e-01 -4.36922371e-01 -7.10777700e-01 -4.01805788e-01 -3.51813793e-01 1.59220874e-01 -4.17478420e-02 -5.69883227e-01 6.27996087e-01 6.61087409e-02 -5.72731674e-01 -3.06726962e-01 -7.66978145e-01 -3.75132501e-01 -4.30324256e-01 -1.73298512e-02 1.96092770e-01 3.35548967e-02 -4.03190941e-01 3.46426874e-01 4.84463543e-01 -1.61178792e+00 4.99599725e-01 4.26505506e-01 1.03305960e+00 1.11136422e-01 5.24594843e-01 -8.62088025e-01 1.07318556e+00 -7.40257263e-01 -4.02103484e-01 -1.87614277e-01 -2.86064982e-01 -1.05531681e+00 -5.83079755e-01 1.17681406e-01 -1.06221460e-01 -5.56691229e-01 8.74859989e-01 5.43888509e-01 1.14958502e-01 3.84263545e-01 -8.02347839e-01 -8.13164234e-01 1.18039310e+00 -3.33996862e-01 5.53289115e-01 2.13617027e-01 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f437e5a4-4236-4446-9136-ec2e16ad10a3	pneumonia-detection-in-chest-x-ray-images	2301.08479	null	https://arxiv.org/abs/2301.08479v1	https://arxiv.org/pdf/2301.08479v1.pdf	Pneumonia Detection in Chest X-Ray Images : Handling Class Imbalance	People all over the globe are affected by pneumonia but deaths due to it are highest in Sub-Saharan Asia and South Asia. In recent years, the overall incidence and mortality rate of pneumonia regardless of the utilization of effective vaccines and compelling antibiotics has escalated. Thus, pneumonia remains a disease that needs spry prevention and treatment. The widespread prevalence of pneumonia has caused the research community to come up with a framework that helps detect, diagnose and analyze diseases accurately and promptly. One of the major hurdles faced by the Artificial Intelligence (AI) research community is the lack of publicly available datasets for chest diseases, including pneumonia . Secondly, few of the available datasets are highly imbalanced (normal examples are over sampled, while samples with ailment are in severe minority) making the problem even more challenging. In this article we present a novel framework for the detection of pneumonia. The novelty of the proposed methodology lies in the tackling of class imbalance problem. The Generative Adversarial Network (GAN), specifically a combination of Deep Convolutional Generative Adversarial Network (DCGAN) and Wasserstein GAN gradient penalty (WGAN-GP) was applied on the minority class ``Pneumonia'' for augmentation, whereas Random Under-Sampling (RUS) was done on the majority class ``No Findings'' to deal with the imbalance problem. The ChestX-Ray8 dataset, one of the biggest datasets, is used to validate the performance of the proposed framework. The learning phase is completed using transfer learning on state-of-the-art deep learning models i.e. ResNet-50, Xception, and VGG-16. Results obtained exceed state-of-the-art.	['Rizwan Ahmed Khan', 'Omama Ahmed Farooqi', 'Eesha Qureshi', 'Wardah Ali']	2023-01-20	null	null	null	null	['pneumonia-detection']	['medical']	[ 3.06621075e-01 -8.79538581e-02 6.33875560e-03 5.17073879e-03 -4.96463954e-01 -1.48682699e-01 3.51627380e-01 -3.34899761e-02 -3.49160850e-01 1.07477212e+00 1.20557852e-01 -2.15759769e-01 -1.83206141e-01 -9.28757787e-01 -5.00181437e-01 -1.06958675e+00 2.43569866e-01 8.51760924e-01 -6.96160123e-02 5.24372943e-02 -1.06912747e-01 4.86256540e-01 -1.35493731e+00 3.94803703e-01 9.84819651e-01 7.37376750e-01 8.83330777e-02 8.75689149e-01 -1.86283976e-01 9.11300063e-01 -5.60066104e-01 -2.00128689e-01 2.56256044e-01 -4.87151563e-01 -6.21369720e-01 -3.34075928e-01 1.26288563e-01 -5.63216746e-01 -1.80589497e-01 6.22623384e-01 9.12863731e-01 -2.63638377e-01 9.58169281e-01 -1.27935457e+00 -4.46173012e-01 5.87267317e-02 -5.23498714e-01 4.20304865e-01 -2.19410181e-01 2.43205920e-01 5.41782737e-01 -6.71006680e-01 4.28190798e-01 1.08058536e+00 9.59181070e-01 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-1.04111922e+00 -3.88780206e-01 6.18597150e-01 4.11136895e-01 -4.80442494e-01 -6.59997106e-01 -1.05878063e-01]	[15.534918785095215, -1.7472325563430786]
e8d5e6a5-171e-4446-a06a-d78c1bbd7c6f	simtreels-simulating-aerial-and-terrestrial	2011.11954	null	https://arxiv.org/abs/2011.11954v1	https://arxiv.org/pdf/2011.11954v1.pdf	SimTreeLS: Simulating aerial and terrestrial laser scans of trees	There are numerous emerging applications for digitizing trees using terrestrial and aerial laser scanning, particularly in the fields of agriculture and forestry. Interpretation of LiDAR point clouds is increasingly relying on data-driven methods (such as supervised machine learning) that rely on large quantities of hand-labelled data. As this data is potentially expensive to capture, and difficult to clearly visualise and label manually, a means of supplementing real LiDAR scans with simulated data is becoming a necessary step in realising the potential of these methods. We present an open source tool, SimTreeLS (Simulated Tree Laser Scans), for generating point clouds which simulate scanning with user-defined sensor, trajectory, tree shape and layout parameters. Upon simulation, material classification is kept in a pointwise fashion so leaf and woody matter are perfectly known, and unique identifiers separate individual trees, foregoing post-simulation labelling. This allows for an endless supply of procedurally generated data with similar characteristics to real LiDAR captures, which can then be used for development of data processing techniques or training of machine learning algorithms. To validate our method, we compare the characteristics of a simulated scan with a real scan using similar trees and the same sensor and trajectory parameters. Results suggest the simulated data is significantly more similar to real data than a sample-based control. We also demonstrate application of SimTreeLS on contexts beyond the real data available, simulating scans of new tree shapes, new trajectories and new layouts, with results presenting well. SimTreeLS is available as an open source resource built on publicly available libraries.	['James Underwood', 'Mitch Bryson', 'Fredrik Westling']	2020-11-24	null	null	null	null	['material-classification']	['computer-vision']	[ 7.77915120e-01 -1.40356645e-01 1.01716757e-01 -3.97390366e-01 -3.20164144e-01 -9.09471333e-01 6.14232183e-01 5.79690635e-01 -1.77760869e-01 6.25290513e-01 -4.21543568e-01 -9.76150155e-01 -3.62323910e-01 -1.26613688e+00 -3.40088934e-01 -1.85378104e-01 -3.54575485e-01 1.04184020e+00 5.75381875e-01 -1.50446802e-01 2.43398637e-01 1.30539358e+00 -2.15002441e+00 -6.21344037e-02 6.38565183e-01 5.32507539e-01 7.19283104e-01 8.96466196e-01 -5.01599371e-01 -1.48951530e-01 -1.42600432e-01 7.92690516e-02 5.31353354e-01 5.58184460e-03 -4.93652552e-01 3.18383187e-01 4.56544429e-01 -2.62126297e-01 5.85984826e-01 6.16222799e-01 4.62327361e-01 -2.43421108e-01 5.86249053e-01 -1.08420789e+00 1.55633539e-01 1.97475404e-01 -6.21575356e-01 -5.06955028e-01 1.72826648e-01 3.89491946e-01 6.30111814e-01 -6.37683690e-01 6.85079336e-01 1.21728063e+00 8.16860557e-01 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e54b5fc1-64e3-4e1a-9f43-d5b4adf1d46e	extending-implicit-discourse-relation	2010.06294	null	https://arxiv.org/abs/2010.06294v1	https://arxiv.org/pdf/2010.06294v1.pdf	Extending Implicit Discourse Relation Recognition to the PDTB-3	The PDTB-3 contains many more Implicit discourse relations than the previous PDTB-2. This is in part because implicit relations have now been annotated within sentences as well as between them. In addition, some now co-occur with explicit discourse relations, instead of standing on their own. Here we show that while this can complicate the problem of identifying the location of implicit discourse relations, it can in turn simplify the problem of identifying their senses. We present data to support this claim, as well as methods that can serve as a non-trivial baseline for future state-of-the-art recognizers for implicit discourse relations.	['Bonnie Webber', 'Zheng Zhao', 'Li Liang']	2020-10-13	null	https://aclanthology.org/2020.codi-1.14	https://aclanthology.org/2020.codi-1.14.pdf	emnlp-codi-2020-11	['implicit-relations']	['natural-language-processing']	[ 3.40247452e-01 9.86654878e-01 -4.33245629e-01 -2.73130208e-01 -5.93398333e-01 -8.21116209e-01 9.43302810e-01 6.30859733e-01 -2.99568743e-01 1.15960360e+00 9.49412465e-01 -7.02991128e-01 -1.23673141e-01 -6.03140295e-01 -2.06753418e-01 -4.12262768e-01 -1.49053916e-01 8.20743203e-01 5.11663854e-01 -5.14556825e-01 4.08453912e-01 3.22173350e-02 -1.02418482e+00 7.31883109e-01 7.42873967e-01 1.93248704e-01 4.04180177e-02 5.43495595e-01 -3.01300824e-01 1.63592935e+00 -1.24029601e+00 -4.67136025e-01 -4.24886703e-01 -3.84962678e-01 -1.55130744e+00 -1.92389451e-02 4.49676245e-01 -1.99116901e-01 -4.46746022e-01 6.42206132e-01 1.22090708e-02 2.71790504e-01 8.56704950e-01 -9.11571205e-01 -6.76526546e-01 9.27293658e-01 -4.06468868e-01 5.67225873e-01 6.16797507e-01 -1.94292381e-01 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46a70ebd-7aec-40a2-b9be-191c66ca298c	learning-viewpoint-agnostic-visual	2206.11895	null	https://arxiv.org/abs/2206.11895v4	https://arxiv.org/pdf/2206.11895v4.pdf	Learning Viewpoint-Agnostic Visual Representations by Recovering Tokens in 3D Space	Humans are remarkably flexible in understanding viewpoint changes due to visual cortex supporting the perception of 3D structure. In contrast, most of the computer vision models that learn visual representation from a pool of 2D images often fail to generalize over novel camera viewpoints. Recently, the vision architectures have shifted towards convolution-free architectures, visual Transformers, which operate on tokens derived from image patches. However, these Transformers do not perform explicit operations to learn viewpoint-agnostic representation for visual understanding. To this end, we propose a 3D Token Representation Layer (3DTRL) that estimates the 3D positional information of the visual tokens and leverages it for learning viewpoint-agnostic representations. The key elements of 3DTRL include a pseudo-depth estimator and a learned camera matrix to impose geometric transformations on the tokens, trained in an unsupervised fashion. These enable 3DTRL to recover the 3D positional information of the tokens from 2D patches. In practice, 3DTRL is easily plugged-in into a Transformer. Our experiments demonstrate the effectiveness of 3DTRL in many vision tasks including image classification, multi-view video alignment, and action recognition. The models with 3DTRL outperform their backbone Transformers in all the tasks with minimal added computation. Our code is available at https://github.com/elicassion/3DTRL.	['Michael S. Ryoo', 'Srijan Das', 'Jinghuan Shang']	2022-06-23	null	null	null	null	['video-alignment']	['computer-vision']	[-3.38038690e-02 -7.17678368e-02 -8.34669769e-02 -4.99349147e-01 -3.84935975e-01 -7.52747059e-01 6.46402717e-01 -4.02524769e-01 -1.01599380e-01 4.53896448e-02 1.84458628e-01 -3.39525864e-02 2.61197805e-01 -4.55188364e-01 -1.03996432e+00 -6.07018471e-01 3.63411129e-01 1.96253702e-01 1.55434623e-01 5.79087399e-02 3.51764143e-01 5.64682066e-01 -1.37696660e+00 4.87932771e-01 4.75474924e-01 1.00755942e+00 4.31292355e-01 5.05667090e-01 -1.99543498e-02 7.85073996e-01 -2.03029379e-01 -2.07042515e-01 6.67031884e-01 -1.49005368e-01 -5.63893497e-01 4.89491194e-01 7.83150613e-01 -6.46434009e-01 -6.85176611e-01 8.27538192e-01 2.01277032e-01 2.83136219e-02 6.47043765e-01 -1.20923889e+00 -9.55938339e-01 9.64879617e-02 -7.72103786e-01 2.55744547e-01 3.55297983e-01 3.86270165e-01 9.79076385e-01 -1.25040531e+00 5.14860094e-01 1.28454447e+00 5.55830479e-01 4.78174329e-01 -1.10243785e+00 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27a4b834-7f48-4742-9caa-fcacd35157a6	a-system-for-generating-cloze-test-items-from	null	null	https://aclanthology.org/R13-2016	https://aclanthology.org/R13-2016.pdf	A System for Generating Cloze Test Items from Russian-Language Text	null	['Andrey Kurtasov']	2013-09-01	a-system-for-generating-cloze-test-items-from-1	https://aclanthology.org/R13-2016	https://aclanthology.org/R13-2016.pdf	ranlp-2013-9	['cloze-test']	['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 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09c782e1-6b7b-44b7-a0f3-e7c9f3c1035d	temporality-and-causality-in-abstract	2303.09197	null	https://arxiv.org/abs/2303.09197v1	https://arxiv.org/pdf/2303.09197v1.pdf	Temporality and Causality in Abstract Argumentation	In the context of abstract argumentation, we present the benefits of considering temporality, i.e. the order in which arguments are enunciated, as well as causality. We propose a formal method to rewrite the concepts of acyclic abstract argumentation frameworks into an action language, that allows us to model the evolution of the world, and to establish causal relationships between the enunciation of arguments and their consequences, whether direct or indirect. An Answer Set Programming implementation is also proposed, as well as perspectives towards explanations.	['M. -J. Lesot', 'G. Bourgne', 'I. Bloch', 'C. Sarmiento', 'Y. Munro']	2023-03-16	null	null	null	null	['abstract-argumentation', 'abstract-argumentation']	['natural-language-processing', 'reasoning']	[-4.12233323e-02 9.65212524e-01 -1.52057305e-01 -3.46447289e-01 5.32659054e-01 -9.29497421e-01 1.24767685e+00 7.11420536e-01 -2.49787524e-01 8.35088611e-01 5.62790036e-01 -6.91011131e-01 -5.20686746e-01 -1.23921692e+00 -4.81327116e-01 -1.61401510e-01 2.91912904e-04 3.44921768e-01 4.99928147e-01 -6.24413848e-01 3.79197031e-01 3.47875357e-01 -1.45358515e+00 4.12567586e-01 6.21585071e-01 5.62383294e-01 -3.34334731e-01 5.26011705e-01 -5.05577624e-01 1.20887256e+00 -8.90773535e-01 -8.80603313e-01 -5.87010205e-01 -4.58723098e-01 -1.37112772e+00 1.01102039e-01 -3.11109990e-01 -3.04216862e-01 3.60688448e-01 7.64029562e-01 -2.88985372e-01 -1.75750434e-01 5.58481038e-01 -1.45253932e+00 -3.81948054e-01 1.16562343e+00 -4.94553335e-02 -1.93986997e-01 7.92368591e-01 -1.95627153e-01 1.04025733e+00 -3.84412199e-01 9.94303405e-01 1.41545439e+00 2.19005108e-01 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56223c15-9bdc-4828-946c-49ab0ee032ed	understanding-open-set-recognition-by	2209.11436	null	https://arxiv.org/abs/2209.11436v1	https://arxiv.org/pdf/2209.11436v1.pdf	Understanding Open-Set Recognition by Jacobian Norm of Representation	In contrast to conventional closed-set recognition, open-set recognition (OSR) assumes the presence of an unknown class, which is not seen to a model during training. One predominant approach in OSR is metric learning, where a model is trained to separate the inter-class representations of known class data. Numerous works in OSR reported that, even though the models are trained only with the known class data, the models become aware of the unknown, and learn to separate the unknown class representations from the known class representations. This paper analyzes this emergent phenomenon by observing the Jacobian norm of representation. We theoretically show that minimizing the intra-class distances within the known set reduces the Jacobian norm of known class representations while maximizing the inter-class distances within the known set increases the Jacobian norm of the unknown class. The closed-set metric learning thus separates the unknown from the known by forcing their Jacobian norm values to differ. We empirically validate our theoretical framework with ample pieces of evidence using standard OSR datasets. Moreover, under our theoretical framework, we explain how the standard deep learning techniques can be helpful for OSR and use the framework as a guiding principle to develop an effective OSR model.	['Andrew Beng Jin Teoh', 'Eunju Jeong', 'Hojin Park', 'Jaewoo Park']	2022-09-23	null	null	null	null	['open-set-learning']	['miscellaneous']	[ 3.32403302e-01 3.55404407e-01 -2.10949436e-01 -3.70969683e-01 -5.44789076e-01 -7.71072567e-01 5.67908525e-01 -1.13718271e-01 -1.13598138e-01 5.75262487e-01 -8.48944113e-02 1.29556060e-01 -4.04531062e-01 -9.12728548e-01 -7.58173227e-01 -9.34478998e-01 2.69676410e-02 6.21949911e-01 -1.89014882e-01 -3.19051117e-01 3.55499089e-01 6.01929963e-01 -1.96556997e+00 2.24466205e-01 5.65181077e-01 1.27403104e+00 -1.16383746e-01 6.50775850e-01 -6.25704229e-02 6.70980036e-01 -8.74975741e-01 1.35867923e-01 8.06710958e-01 -4.07861978e-01 -7.03856528e-01 8.06305707e-02 7.53267288e-01 -3.00932117e-02 -6.92401052e-01 1.14952850e+00 3.70146483e-01 3.41634393e-01 1.08628953e+00 -1.32693982e+00 -1.25263369e+00 4.11582589e-01 -6.91943467e-02 -1.50196217e-02 3.74840081e-01 -2.23246172e-01 1.23145187e+00 -8.82597268e-01 5.47890902e-01 8.76697361e-01 5.56599498e-01 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f4bcac22-d92b-4728-931d-007b509400c0	structure-aware-pre-training-for-table-to	null	null	https://aclanthology.org/2021.findings-acl.200	https://aclanthology.org/2021.findings-acl.200.pdf	Structure-Aware Pre-Training for Table-to-Text Generation	null	['Xiaojun Wan', 'Xinyu Xing']	null	null	null	null	findings-acl-2021-8	['table-to-text-generation']	['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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c8f9169b-fb83-4d39-b718-380c46500bfd	robust-action-segmentation-from-timestamp	2210.06501	null	https://arxiv.org/abs/2210.06501v1	https://arxiv.org/pdf/2210.06501v1.pdf	Robust Action Segmentation from Timestamp Supervision	Action segmentation is the task of predicting an action label for each frame of an untrimmed video. As obtaining annotations to train an approach for action segmentation in a fully supervised way is expensive, various approaches have been proposed to train action segmentation models using different forms of weak supervision, e.g., action transcripts, action sets, or more recently timestamps. Timestamp supervision is a promising type of weak supervision as obtaining one timestamp per action is less expensive than annotating all frames, but it provides more information than other forms of weak supervision. However, previous works assume that every action instance is annotated with a timestamp, which is a restrictive assumption since it assumes that annotators do not miss any action. In this work, we relax this restrictive assumption and take missing annotations for some action instances into account. We show that our approach is more robust to missing annotations compared to other approaches and various baselines.	['Juergen Gall', 'Gianpiero Francesca', 'Emad Bahrami', 'Yazan Abu Farha', 'Yaser Souri']	2022-10-12	null	null	null	null	['action-segmentation']	['computer-vision']	[ 5.95388114e-01 3.37664515e-01 -7.23413587e-01 -7.07898080e-01 -6.87631071e-01 -6.84314668e-01 6.79421961e-01 1.59919634e-01 -6.31817043e-01 9.60439324e-01 3.51063967e-01 -1.13313340e-01 4.16124165e-01 -4.49591994e-01 -8.24081361e-01 -6.62897348e-01 1.11474715e-01 2.78027326e-01 8.19937408e-01 2.73863941e-01 2.51910925e-01 3.93134981e-01 -1.32010233e+00 4.65824097e-01 5.22218168e-01 8.23364615e-01 -4.31041196e-02 7.16311872e-01 -4.21884134e-02 1.10667777e+00 -8.10121834e-01 -2.89908290e-01 4.74828154e-01 -7.56390572e-01 -1.35690057e+00 5.52319109e-01 3.04883897e-01 -6.35325015e-01 1.77408177e-02 9.20233846e-01 -5.05411439e-02 1.77580193e-01 3.29571307e-01 -1.58233607e+00 5.60051464e-02 7.17662454e-01 -5.40321350e-01 2.10442811e-01 6.49366677e-01 4.34545130e-02 9.64521945e-01 -2.52789974e-01 8.21578562e-01 9.71995592e-01 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7e355090-e4a5-4d1f-8b6b-c905306789d3	robust-data-association-for-object-level	1909.13493	null	https://arxiv.org/abs/1909.13493v1	https://arxiv.org/pdf/1909.13493v1.pdf	Robust Data Association for Object-level Semantic SLAM	Simultaneous mapping and localization (SLAM) in an real indoor environment is still a challenging task. Traditional SLAM approaches rely heavily on low-level geometric constraints like corners or lines, which may lead to tracking failure in textureless surroundings or cluttered world with dynamic objects. In this paper, a compact semantic SLAM framework is proposed, with utilization of both geometric and object-level semantic constraints jointly, a more consistent mapping result, and more accurate pose estimation can be obtained. Two main contributions are presented int the paper, a) a robust and efficient SLAM data association and optimization framework is proposed, it models both discrete semantic labeling and continuous pose. b) a compact map representation, combining 2D Lidar map with object detection is presented. Experiments on public indoor datasets, TUM-RGBD, ICL-NUIM, and our own collected datasets prove the improving of SLAM robustness and accuracy compared to other popular SLAM systems, meanwhile a map maintenance efficiency can be achieved.	['Xueyang Kang', 'Shunying Yuan']	2019-09-30	null	null	null	null	['semantic-slam']	['computer-vision']	[ 3.07845678e-02 -5.28480232e-01 -4.88655455e-02 -6.30756915e-01 -3.11370850e-01 -4.82451826e-01 4.79126006e-01 3.49204004e-01 -5.57652295e-01 1.06069314e+00 -4.17854011e-01 -1.43319648e-02 -5.50313175e-01 -9.25895154e-01 -6.07228935e-01 -3.49516422e-01 -1.36515439e-01 8.15361083e-01 6.72616422e-01 -1.50906190e-01 4.22632933e-01 6.42668009e-01 -1.54385841e+00 -7.32085109e-01 1.23889935e+00 1.12686694e+00 8.45194757e-01 1.12039983e-01 -2.51854330e-01 2.51050264e-01 -2.48122707e-01 1.54893890e-01 3.18335712e-01 1.28601030e-01 -3.71923596e-01 3.40693034e-02 4.21848804e-01 -9.13450047e-02 -1.29624397e-01 1.27732146e+00 4.31594223e-01 1.96943223e-01 2.72144020e-01 -1.61676240e+00 -1.39781713e-01 -1.79032125e-02 -4.95949298e-01 -4.04998869e-01 6.61944628e-01 -3.64953041e-01 4.14640188e-01 -9.37855721e-01 6.51169419e-01 1.18707550e+00 1.02771139e+00 -1.48063734e-01 -1.11631656e+00 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0a9a4c90-63f0-4fcf-9f45-f8d10122d81e	co-bed-information-theoretic-contextual	2302.14015	null	https://arxiv.org/abs/2302.14015v1	https://arxiv.org/pdf/2302.14015v1.pdf	CO-BED: Information-Theoretic Contextual Optimization via Bayesian Experimental Design	We formalize the problem of contextual optimization through the lens of Bayesian experimental design and propose CO-BED -- a general, model-agnostic framework for designing contextual experiments using information-theoretic principles. After formulating a suitable information-based objective, we employ black-box variational methods to simultaneously estimate it and optimize the designs in a single stochastic gradient scheme. We further introduce a relaxation scheme to allow discrete actions to be accommodated. As a result, CO-BED provides a general and automated solution to a wide range of contextual optimization problems. We illustrate its effectiveness in a number of experiments, where CO-BED demonstrates competitive performance even when compared to bespoke, model-specific alternatives.	['Adam Foster', 'Cheng Zhang', 'Tom Rainforth', 'Joel Jennings', 'Desi R. Ivanova']	2023-02-27	null	null	null	null	['experimental-design']	['methodology']	[ 1.44075915e-01 -3.18213105e-01 -3.79314482e-01 -4.19740379e-01 -1.12209952e+00 -6.33194625e-01 5.50957859e-01 -2.09721863e-01 -6.43644571e-01 7.74263203e-01 2.12048411e-01 -5.64370453e-01 -6.63718283e-01 -2.36764997e-01 -7.52297580e-01 -6.74809098e-01 7.44404420e-02 2.83398688e-01 -2.27770224e-01 6.03145622e-02 7.05514133e-01 3.90262365e-01 -1.38984573e+00 -7.42546245e-02 6.24052882e-01 5.52812159e-01 3.24689895e-01 7.02696562e-01 5.38539827e-01 1.54759288e-01 -1.04366913e-01 -3.02643627e-01 2.98767626e-01 -4.96053874e-01 -5.68011999e-01 2.86830664e-01 1.78241894e-01 -2.78998584e-01 -2.32172817e-01 9.37810183e-01 7.17115462e-01 4.96105224e-01 6.89619303e-01 -1.08918107e+00 -4.99410152e-01 5.11501968e-01 -4.32834268e-01 1.14373296e-01 2.31439278e-01 5.99200964e-01 1.58247232e+00 -9.85534668e-01 4.71862465e-01 1.56947184e+00 4.83509570e-01 2.76007354e-01 -2.10560656e+00 -1.10689797e-01 4.75439072e-01 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c9715fb4-a276-4f7d-93b6-4acab9bc10ba	investigating-conversational-search-behavior	2301.04098	null	https://arxiv.org/abs/2301.04098v2	https://arxiv.org/pdf/2301.04098v2.pdf	Investigating Conversational Search Behavior For Domain Exploration	Conversational search has evolved as a new information retrieval paradigm, marking a shift from traditional search systems towards interactive dialogues with intelligent search agents. This change especially affects exploratory information-seeking contexts, where conversational search systems can guide the discovery of unfamiliar domains. In these scenarios, users find it often difficult to express their information goals due to insufficient background knowledge. Conversational interfaces can provide assistance by eliciting information needs and narrowing down the search space. However, due to the complexity of information-seeking behavior, the design of conversational interfaces for retrieving information remains a great challenge. Although prior work has employed user studies to empirically ground the system design, most existing studies are limited to well-defined search tasks or known domains, thus being less exploratory in nature. Therefore, we conducted a laboratory study to investigate open-ended search behavior for navigation through unknown information landscapes. The study comprised of 26 participants who were restricted in their search to a text chat interface. Based on the collected dialogue transcripts, we applied statistical analyses and process mining techniques to uncover general information-seeking patterns across five different domains. We not only identify core dialogue acts and their interrelations that enable users to discover domain knowledge, but also derive design suggestions for conversational search systems.	['Florian Matthes', 'Daniel Braun', 'Juraj Vladika', 'Anum Afzal', 'Phillip Schneider']	2023-01-10	null	null	null	null	['conversational-search']	['natural-language-processing']	[ 1.35590687e-01 2.77361989e-01 -2.75005728e-01 -1.35691717e-01 -3.57155174e-01 -8.15360010e-01 7.93921769e-01 3.94994885e-01 -3.79253417e-01 5.76433659e-01 5.40755272e-01 -7.90449619e-01 -5.62842548e-01 -2.60499448e-01 4.67351079e-01 -5.71062155e-02 1.16435900e-01 6.41468108e-01 1.50423661e-01 -6.35484338e-01 1.00067735e+00 2.42388621e-01 -1.61255002e+00 4.30163413e-01 1.09308660e+00 4.38024253e-01 8.53601277e-01 5.33223271e-01 -7.44153738e-01 5.42665660e-01 -6.60569847e-01 -1.69637039e-01 -1.73338950e-01 -5.60019135e-01 -1.47227848e+00 -1.09165333e-01 -2.55676568e-01 -5.43450296e-01 -5.47241680e-02 6.27864480e-01 4.71616924e-01 5.52931488e-01 4.01162297e-01 -1.10049152e+00 -5.60397446e-01 3.44789296e-01 2.21710667e-01 4.06915575e-01 1.22459447e+00 1.42257109e-01 1.07421422e+00 -5.18953443e-01 7.65236676e-01 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3.34545702e-01 -5.18348217e-01 -5.10547578e-01 -1.46700537e+00 -7.58448690e-02 4.46887672e-01 7.76629746e-02 -6.67664349e-01 -4.07488167e-01 -3.45136046e-01]	[12.256207466125488, 7.780663013458252]
57d3a1d5-f3ea-4b87-bd63-b233c98cdb63	evaluating-bert-based-scientific-relation	2305.02291	null	https://arxiv.org/abs/2305.02291v1	https://arxiv.org/pdf/2305.02291v1.pdf	Evaluating BERT-based Scientific Relation Classifiers for Scholarly Knowledge Graph Construction on Digital Library Collections	The rapid growth of research publications has placed great demands on digital libraries (DL) for advanced information management technologies. To cater to these demands, techniques relying on knowledge-graph structures are being advocated. In such graph-based pipelines, inferring semantic relations between related scientific concepts is a crucial step. Recently, BERT-based pre-trained models have been popularly explored for automatic relation classification. Despite significant progress, most of them were evaluated in different scenarios, which limits their comparability. Furthermore, existing methods are primarily evaluated on clean texts, which ignores the digitization context of early scholarly publications in terms of machine scanning and optical character recognition (OCR). In such cases, the texts may contain OCR noise, in turn creating uncertainty about existing classifiers' performances. To address these limitations, we started by creating OCR-noisy texts based on three clean corpora. Given these parallel corpora, we conducted a thorough empirical evaluation of eight Bert-based classification models by focusing on three factors: (1) Bert variants; (2) classification strategies; and, (3) OCR noise impacts. Experiments on clean data show that the domain-specific pre-trained Bert is the best variant to identify scientific relations. The strategy of predicting a single relation each time outperforms the one simultaneously identifying multiple relations in general. The optimal classifier's performance can decline by around 10% to 20% in F-score on the noisy corpora. Insights discussed in this study can help DL stakeholders select techniques for building optimal knowledge-graph-based systems.	['J. Stephen Downie', 'Sören Auer', "Jennifer D'Souza", 'Ming Jiang']	2023-05-03	null	null	null	null	['optical-character-recognition', 'graph-construction', 'relation-classification']	['computer-vision', 'graphs', 'natural-language-processing']	[ 3.20816249e-01 1.33641049e-01 -1.76193774e-01 2.84573026e-02 -8.81181657e-01 -7.33579159e-01 7.49838829e-01 7.01710224e-01 -3.20272774e-01 7.13608801e-01 -1.12755582e-01 -5.85414886e-01 -4.30808961e-01 -8.35836470e-01 -4.18062568e-01 -4.99330580e-01 6.16065748e-02 5.09719253e-01 2.92781889e-01 -3.68214841e-03 6.91017568e-01 6.77556694e-01 -1.49065065e+00 2.80136883e-01 1.17158425e+00 5.93984365e-01 4.59348708e-01 6.63927972e-01 -6.72823787e-01 6.35504425e-01 -9.75427151e-01 -7.32619822e-01 -6.62632585e-02 -5.44454455e-01 -9.90346789e-01 -1.24775045e-01 1.49822488e-01 4.56646174e-01 -1.38563752e-01 1.21473563e+00 2.46842310e-01 -7.64124244e-02 5.79170525e-01 -9.58107352e-01 -6.12247109e-01 7.45304465e-01 -2.51567364e-01 4.40442473e-01 5.04750729e-01 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3.98390055e-01 7.72470772e-01 1.07540774e+00 3.29974204e-01 6.74144804e-01 7.47349560e-02 -4.94632602e-01 -3.64412457e-01 -1.11008632e+00 -3.77082407e-01 5.48300147e-01 -2.31076792e-01 -5.82051992e-01 -4.06757444e-01 1.50808126e-01]	[9.429883003234863, 8.4938383102417]
a32e9198-529f-4ed3-8398-f5c86e72c2f6	saliency-scale-and-information-towards-a	null	null	http://papers.nips.cc/paper/5946-saliency-scale-and-information-towards-a-unifying-theory	http://papers.nips.cc/paper/5946-saliency-scale-and-information-towards-a-unifying-theory.pdf	Saliency, Scale and Information: Towards a Unifying Theory	In this paper we present a definition for visual saliency grounded in information theory. This proposal is shown to relate to a variety of classic research contributions in scale-space theory, interest point detection, bilateral filtering, and to existing models of visual saliency. Based on the proposed definition of visual saliency, we demonstrate results competitive with the state-of-the art for both prediction of human fixations, and segmentation of salient objects. We also characterize different properties of this model including robustness to image transformations, and extension to a wide range of other data types with 3D mesh models serving as an example. Finally, we relate this proposal more generally to the role of saliency computation in visual information processing and draw connections to putative mechanisms for saliency computation in human vision.	['Shafin Rahman', 'Neil Bruce']	2015-12-01	null	null	null	neurips-2015-12	['interest-point-detection']	['computer-vision']	[ 4.66765106e-01 1.75555900e-01 -1.32306591e-01 -2.59726405e-01 -1.68453991e-01 -4.89974380e-01 5.99981070e-01 5.37414551e-01 -3.92650485e-01 4.92950648e-01 1.61262900e-01 -1.63411513e-01 -2.92699546e-01 -4.77959484e-01 -6.56116009e-01 -3.52097899e-01 -1.13659717e-01 2.14757281e-03 1.02650034e+00 -4.30889755e-01 1.27426910e+00 9.27851617e-01 -2.05576825e+00 1.25176996e-01 6.77659392e-01 1.01346838e+00 6.12552345e-01 5.02698898e-01 3.13835964e-02 3.66782188e-01 -3.43161821e-01 -2.23472521e-01 1.98914856e-01 -2.61493146e-01 -1.03285801e+00 1.68327734e-01 7.45841324e-01 1.52592078e-01 1.71447266e-02 1.19640398e+00 3.62743765e-01 3.19994509e-01 7.77550876e-01 -1.34277725e+00 -8.20996642e-01 1.17549645e-02 -6.77282274e-01 1.06632924e+00 3.44994009e-01 -1.87335059e-01 1.04081249e+00 -1.27095020e+00 9.54344094e-01 1.28377783e+00 5.15184045e-01 3.77830088e-01 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e8baf0e3-7e8e-4360-b303-cda36b7e97cc	continual-treatment-effect-estimation	2301.01026	null	https://arxiv.org/abs/2301.01026v4	https://arxiv.org/pdf/2301.01026v4.pdf	Continual Causal Effect Estimation: Challenges and Opportunities	A further understanding of cause and effect within observational data is critical across many domains, such as economics, health care, public policy, web mining, online advertising, and marketing campaigns. Although significant advances have been made to overcome the challenges in causal effect estimation with observational data, such as missing counterfactual outcomes and selection bias between treatment and control groups, the existing methods mainly focus on source-specific and stationary observational data. Such learning strategies assume that all observational data are already available during the training phase and from only one source. This practical concern of accessibility is ubiquitous in various academic and industrial applications. That's what it boiled down to: in the era of big data, we face new challenges in causal inference with observational data, i.e., the extensibility for incrementally available observational data, the adaptability for extra domain adaptation problem except for the imbalance between treatment and control groups, and the accessibility for an enormous amount of data. In this position paper, we formally define the problem of continual treatment effect estimation, describe its research challenges, and then present possible solutions to this problem. Moreover, we will discuss future research directions on this topic.	['Sheng Li', 'Zhixuan Chu']	2023-01-03	null	null	null	null	['marketing', 'selection-bias']	['miscellaneous', 'natural-language-processing']	[ 4.14251983e-01 1.38869183e-02 -1.24310148e+00 -4.11338925e-01 -6.71223164e-01 -2.95312792e-01 5.36407888e-01 3.26970607e-01 -4.71960276e-01 1.25535202e+00 7.58814454e-01 -7.27145970e-01 -6.48868918e-01 -7.92319775e-01 -8.49148333e-01 -5.78238606e-01 -3.26669186e-01 4.38191205e-01 -1.83991924e-01 9.75028276e-02 6.19126260e-02 2.22035181e-02 -1.30643368e+00 -5.31023070e-02 1.15046012e+00 4.25221771e-01 -1.35182098e-01 6.50813431e-02 1.18924990e-01 6.01117074e-01 -3.87217283e-01 -3.81811559e-01 2.88381856e-02 -2.20252797e-01 -5.68319023e-01 -1.86254293e-01 3.38947386e-01 -4.64826465e-01 -2.78954178e-01 7.90573657e-01 7.58823574e-01 -2.27979887e-02 7.21738219e-01 -1.49052048e+00 -7.79036283e-01 8.36266100e-01 -8.48925889e-01 1.83598042e-01 2.02091902e-01 1.45162210e-01 9.14806664e-01 -3.02733541e-01 6.55908167e-01 1.43261909e+00 4.96102870e-01 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3168af8a-bd4e-4be3-b5eb-ab3c7d963111	hyperspectral-demosaicing-of-snapshot-camera	2211.15435	null	https://arxiv.org/abs/2211.15435v1	https://arxiv.org/pdf/2211.15435v1.pdf	Hyperspectral Demosaicing of Snapshot Camera Images Using Deep Learning	Spectral imaging technologies have rapidly evolved during the past decades. The recent development of single-camera-one-shot techniques for hyperspectral imaging allows multiple spectral bands to be captured simultaneously (3x3, 4x4 or 5x5 mosaic), opening up a wide range of applications. Examples include intraoperative imaging, agricultural field inspection and food quality assessment. To capture images across a wide spectrum range, i.e. to achieve high spectral resolution, the sensor design sacrifices spatial resolution. With increasing mosaic size, this effect becomes increasingly detrimental. Furthermore, demosaicing is challenging. Without incorporating edge, shape, and object information during interpolation, chromatic artifacts are likely to appear in the obtained images. Recent approaches use neural networks for demosaicing, enabling direct information extraction from image data. However, obtaining training data for these approaches poses a challenge as well. This work proposes a parallel neural network based demosaicing procedure trained on a new ground truth dataset captured in a controlled environment by a hyperspectral snapshot camera with a 4x4 mosaic pattern. The dataset is a combination of real captured scenes with images from publicly available data adapted to the 4x4 mosaic pattern. To obtain real world ground-truth data, we performed multiple camera captures with 1-pixel shifts in order to compose the entire data cube. Experiments show that the proposed network outperforms state-of-art networks.	['Peter Eisert', 'Anna Hilsmann', 'Charul Daudkhane', 'Eric L. Wisotzky']	2022-11-21	null	null	null	null	['demosaicking']	['computer-vision']	[ 9.31656122e-01 -5.27088046e-01 1.94691420e-01 -2.95757383e-01 -5.38442254e-01 -5.79193652e-01 1.31460354e-01 -9.43613648e-02 -3.18175763e-01 7.78456450e-01 -1.98900372e-01 -5.82640618e-02 -3.98619384e-01 -8.81167948e-01 -6.94585025e-01 -1.04070020e+00 1.08710624e-01 -5.06217107e-02 -1.56964481e-01 -6.54451698e-02 -3.56568694e-02 6.54389858e-01 -1.69084513e+00 2.00026780e-01 1.13038027e+00 1.05840802e+00 7.13230073e-01 4.18055087e-01 1.37614518e-01 1.00064456e-01 -4.84717280e-01 7.45801330e-02 8.46951365e-01 -2.19802633e-01 -1.17008194e-01 7.42838562e-01 7.72446990e-01 -6.39566302e-01 -1.39016211e-01 1.42471838e+00 3.45066547e-01 1.43446341e-01 2.97834814e-01 -8.30762684e-01 -8.85708392e-01 3.22162271e-01 -8.95053864e-01 -3.40406418e-01 -8.99173990e-02 4.01386946e-01 6.09110057e-01 -7.12491810e-01 3.29145312e-01 5.99528611e-01 6.77169442e-01 7.97813386e-03 -1.21105671e+00 -4.28008467e-01 -2.28868946e-01 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bebb5706-3665-4f8e-930e-bd62c16717fc	pscnet-pyramidal-scale-and-global-context	2012.03597	null	https://arxiv.org/abs/2012.03597v3	https://arxiv.org/pdf/2012.03597v3.pdf	PSGCNet: A Pyramidal Scale and Global Context Guided Network for Dense Object Counting in Remote Sensing Images	Object counting, which aims to count the accurate number of object instances in images, has been attracting more and more attention. However, challenges such as large scale variation, complex background interference, and non-uniform density distribution greatly limit the counting accuracy, particularly striking in remote sensing imagery. To mitigate the above issues, this paper proposes a novel framework for dense object counting in remote sensing images, which incorporates a pyramidal scale module (PSM) and a global context module (GCM), dubbed PSGCNet, where PSM is used to adaptively capture multi-scale information and GCM is to guide the model to select suitable scales generated from PSM. Moreover, a reliable supervision manner improved from Bayesian and Counting loss (BCL) is utilized to learn the density probability and then compute the count expectation at each annotation. It can relieve non-uniform density distribution to a certain extent. Extensive experiments on four remote sensing counting datasets demonstrate the effectiveness of the proposed method and the superiority of it compared with state-of-the-arts. Additionally, experiments extended on four commonly used crowd counting datasets further validate the generalization ability of the model. Code is available at https://github.com/gaoguangshuai/PSGCNet.	['Yunhong Wang', 'Lu Li', 'Zhenghui Hu', 'Qi Wen', 'Qingjie Liu', 'Guangshuai Gao']	2020-12-07	null	null	null	null	['object-counting']	['computer-vision']	[ 8.88602883e-02 -6.27372801e-01 2.34760456e-02 -3.36402565e-01 -3.75197023e-01 -1.96372852e-01 4.72167492e-01 4.92446311e-02 -6.90187454e-01 8.92155409e-01 -6.24541789e-02 3.80124338e-02 -2.52343174e-02 -1.11683702e+00 -3.44332784e-01 -9.00385022e-01 1.60169691e-01 3.57520074e-01 4.60687131e-01 1.74268991e-01 3.50099653e-01 3.95974606e-01 -1.53819346e+00 -3.59816074e-01 1.24100649e+00 8.19858670e-01 7.54532278e-01 4.73923296e-01 -2.91459858e-01 7.78640807e-01 -6.27704918e-01 -1.87662631e-01 1.74967483e-01 -9.09227282e-02 -3.57003003e-01 7.41327703e-02 3.84380311e-01 -6.82120919e-01 -1.76842570e-01 1.39476395e+00 6.54914260e-01 1.89139843e-01 5.69568455e-01 -1.01876676e+00 -6.15471125e-01 2.20890790e-01 -1.17131281e+00 8.45511258e-01 -1.35843679e-01 2.03982487e-01 6.11652911e-01 -8.35959375e-01 3.05921622e-02 1.23473001e+00 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dee7d306-e83e-47b5-9dc0-69a958bc41ba	topic-aware-contextualized-embeddings-for	2201.10982	null	https://arxiv.org/abs/2201.10982v1	https://arxiv.org/pdf/2201.10982v1.pdf	Topic Aware Contextualized Embeddings for High Quality Phrase Extraction	Keyphrase extraction from a given document is the task of automatically extracting salient phrases that best describe the document. This paper proposes a novel unsupervised graph-based ranking method to extract high-quality phrases from a given document. We obtain the contextualized embeddings from pre-trained language models enriched with topic vectors from Latent Dirichlet Allocation (LDA) to represent the candidate phrases and the document. We introduce a scoring mechanism for the phrases using the information obtained from contextualized embeddings and the topic vectors. The salient phrases are extracted using a ranking algorithm on an undirected graph constructed for the given document. In the undirected graph, the nodes represent the phrases, and the edges between the phrases represent the semantic relatedness between them, weighted by a score obtained from the scoring mechanism. To demonstrate the efficacy of our proposed method, we perform several experiments on open source datasets in the science domain and observe that our novel method outperforms existing unsupervised embedding based keyphrase extraction methods. For instance, on the SemEval2017 dataset, our method advances the F1 score from 0.2195 (EmbedRank) to 0.2819 at the top 10 extracted keyphrases. Several variants of the proposed algorithm are investigated to determine their effect on the quality of keyphrases. We further demonstrate the ability of our proposed method to collect additional high-quality keyphrases that are not present in the document from external knowledge bases like Wikipedia for enriching the document with newly discovered keyphrases. We evaluate this step on a collection of annotated documents. The F1-score at the top 10 expanded keyphrases is 0.60, indicating that our algorithm can also be used for 'concept' expansion using external knowledge.	['Vikram Goyal', 'Mukesh Mohania', 'Venktesh V']	2022-01-17	null	null	null	null	['keyphrase-extraction']	['natural-language-processing']	[-6.19559251e-02 3.99201572e-01 -3.51539910e-01 2.88274974e-01 -7.08768785e-01 -7.49123573e-01 8.90714824e-01 1.00380731e+00 -6.83093607e-01 6.85006559e-01 8.78155589e-01 1.12752721e-01 -4.07740623e-01 -9.51721251e-01 -6.09834909e-01 -7.06758499e-01 -1.60424232e-01 2.93726355e-01 3.52043688e-01 -2.08293684e-02 6.88472867e-01 1.99416369e-01 -1.43113792e+00 2.52965018e-02 9.59825516e-01 4.16501105e-01 4.74251509e-01 4.81711209e-01 -5.92712104e-01 1.28695145e-01 -4.57274377e-01 -2.75654793e-01 -4.70866263e-02 1.46444747e-02 -6.88766122e-01 -1.62745323e-02 2.38399506e-01 -8.38402659e-02 -2.33047962e-01 1.10518157e+00 2.49216810e-01 2.17960730e-01 8.33630204e-01 -9.50572908e-01 -7.36936569e-01 8.69310558e-01 -5.42280674e-01 1.95803449e-01 4.22959685e-01 -6.03705943e-01 1.80984128e+00 -1.38745546e+00 9.99361217e-01 9.23938870e-01 7.85369873e-02 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214bb4af-7ace-42de-a86c-2e778ba7b4a2	adaptive-multi-view-rule-discovery-for-weakly	2206.13749	null	https://arxiv.org/abs/2206.13749v1	https://arxiv.org/pdf/2206.13749v1.pdf	Adaptive Multi-view Rule Discovery for Weakly-Supervised Compatible Products Prediction	On e-commerce platforms, predicting if two products are compatible with each other is an important functionality to achieve trustworthy product recommendation and search experience for consumers. However, accurately predicting product compatibility is difficult due to the heterogeneous product data and the lack of manually curated training data. We study the problem of discovering effective labeling rules that can enable weakly-supervised product compatibility prediction. We develop AMRule, a multi-view rule discovery framework that can (1) adaptively and iteratively discover novel rulers that can complement the current weakly-supervised model to improve compatibility prediction; (2) discover interpretable rules from both structured attribute tables and unstructured product descriptions. AMRule adaptively discovers labeling rules from large-error instances via a boosting-style strategy, the high-quality rules can remedy the current model's weak spots and refine the model iteratively. For rule discovery from structured product attributes, we generate composable high-order rules from decision trees; and for rule discovery from unstructured product descriptions, we generate prompt-based rules from a pre-trained language model. Experiments on 4 real-world datasets show that AMRule outperforms the baselines by 5.98% on average and improves rule quality and rule proposal efficiency.	['Chao Zhang', 'Xiquan Cui', 'Rebecca West', 'Rongzhi Zhang']	2022-06-28	null	null	null	null	['product-recommendation']	['miscellaneous']	[ 7.19103515e-02 1.44141570e-01 -1.15231860e+00 -8.94212961e-01 -4.85008180e-01 -9.24733281e-01 1.53246522e-01 3.31076026e-01 3.58385623e-01 2.67367929e-01 -1.71097473e-03 -6.01605654e-01 -3.50547731e-01 -9.13716257e-01 -6.12452865e-01 -2.14816704e-01 -6.62692934e-02 9.74170625e-01 3.35289799e-02 -3.52207541e-01 -9.71529335e-02 -1.58614680e-01 -1.71047723e+00 9.69047546e-01 1.12141824e+00 1.63378513e+00 -1.68114454e-01 7.45308921e-02 -2.24216685e-01 9.27487135e-01 2.40233809e-01 -1.16735542e+00 7.37439156e-01 -9.22608003e-03 -6.82972789e-01 2.09312528e-01 1.96971178e-01 -1.68266192e-01 5.94774127e-01 1.18587852e+00 -3.10890138e-01 -2.42800355e-01 2.96876997e-01 -1.27491033e+00 -8.65724266e-01 1.07591546e+00 -4.76528525e-01 -3.25703442e-01 4.62046534e-01 -2.29283035e-01 1.75357890e+00 -9.83779311e-01 7.17893124e-01 1.02436984e+00 7.19444394e-01 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4851ac22-c764-4ea5-b4d7-6a26650081c0	exploring-the-potential-of-large-language-1	2307.03393	null	https://arxiv.org/abs/2307.03393v2	https://arxiv.org/pdf/2307.03393v2.pdf	Exploring the Potential of Large Language Models (LLMs) in Learning on Graphs	Learning on Graphs has attracted immense attention due to its wide real-world applications. The most popular pipeline for learning on graphs with textual node attributes primarily relies on Graph Neural Networks (GNNs), and utilizes shallow text embedding as initial node representations, which has limitations in general knowledge and profound semantic understanding. In recent years, Large Language Models (LLMs) have been proven to possess extensive common knowledge and powerful semantic comprehension abilities that have revolutionized existing workflows to handle text data. In this paper, we aim to explore the potential of LLMs in graph machine learning, especially the node classification task, and investigate two possible pipelines: LLMs-as-Enhancers and LLMs-as-Predictors. The former leverages LLMs to enhance nodes' text attributes with their massive knowledge and then generate predictions through GNNs. The latter attempts to directly employ LLMs as standalone predictors. We conduct comprehensive and systematical studies on these two pipelines under various settings. From comprehensive empirical results, we make original observations and find new insights that open new possibilities and suggest promising directions to leverage LLMs for learning on graphs.	['Jiliang Tang', 'Hui Liu', 'Wenqi Fan', 'Dawei Yin', 'Shuaiqiang Wang', 'Xiaochi Wei', 'Hongzhi Wen', 'Wei Jin', 'Hang Li', 'Haitao Mao', 'Zhikai Chen']	2023-07-07	null	null	null	null	['node-classification', 'general-knowledge']	['graphs', 'miscellaneous']	[ 2.47464702e-01 5.62627852e-01 -4.55713451e-01 -2.84558088e-01 -1.28310487e-01 -5.06117404e-01 8.25464904e-01 6.95933163e-01 -2.61245012e-01 3.73774558e-01 1.63426369e-01 -7.37024724e-01 -1.57614604e-01 -1.26836860e+00 -5.14621675e-01 -3.56442779e-01 -3.38084519e-01 3.98114383e-01 1.98601007e-01 -3.04687768e-01 -2.53412891e-02 4.21947002e-01 -1.09077787e+00 1.94258064e-01 8.79615307e-01 5.87079227e-01 2.05501556e-01 7.08137512e-01 -7.20411479e-01 1.09270847e+00 -3.12061638e-01 -1.01132727e+00 -2.85235401e-02 3.23062986e-02 -1.00223255e+00 -3.74334693e-01 1.21097155e-01 7.64166564e-02 -7.83833086e-01 1.06541300e+00 2.93969780e-01 -1.91562906e-01 5.63023508e-01 -1.64817262e+00 -1.07828808e+00 1.13830972e+00 -5.82302570e-01 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5a7b1c5d-c732-4047-a7f6-1ffc57792560	vicreg-variance-invariance-covariance	2105.04906	null	https://arxiv.org/abs/2105.04906v3	https://arxiv.org/pdf/2105.04906v3.pdf	VICReg: Variance-Invariance-Covariance Regularization for Self-Supervised Learning	Recent self-supervised methods for image representation learning are based on maximizing the agreement between embedding vectors from different views of the same image. A trivial solution is obtained when the encoder outputs constant vectors. This collapse problem is often avoided through implicit biases in the learning architecture, that often lack a clear justification or interpretation. In this paper, we introduce VICReg (Variance-Invariance-Covariance Regularization), a method that explicitly avoids the collapse problem with a simple regularization term on the variance of the embeddings along each dimension individually. VICReg combines the variance term with a decorrelation mechanism based on redundancy reduction and covariance regularization, and achieves results on par with the state of the art on several downstream tasks. In addition, we show that incorporating our new variance term into other methods helps stabilize the training and leads to performance improvements.	['Yann Lecun', 'Jean Ponce', 'Adrien Bardes']	2021-05-11	vicreg-variance-invariance-covariance-1	https://openreview.net/forum?id=IXexLXymbZ9	https://openreview.net/pdf?id=IXexLXymbZ9	neurips-2021-12	['self-supervised-image-classification']	['computer-vision']	[ 1.69756457e-01 3.87962796e-02 -1.48900762e-01 -4.83244717e-01 -4.77295965e-01 -5.77911079e-01 8.67653191e-01 7.23621249e-02 -4.46668088e-01 3.73732358e-01 5.57305813e-01 -1.09082766e-01 -1.60467178e-01 -3.86727512e-01 -6.53118849e-01 -8.18543255e-01 2.87256151e-01 -1.01439646e-02 7.65488893e-02 -1.47545174e-01 2.57458091e-01 3.14673781e-01 -1.36641932e+00 2.77607769e-01 4.17812556e-01 8.55676472e-01 1.32467031e-01 3.88828218e-01 2.92233489e-02 8.13223839e-01 -1.91794455e-01 -3.50133985e-01 3.79838675e-01 -4.94759023e-01 -6.47191823e-01 1.78100988e-01 7.80875683e-01 -2.06603914e-01 -3.74292701e-01 1.00680137e+00 3.31258774e-01 -1.54647930e-02 9.47870612e-01 -1.10833502e+00 -9.79956985e-01 2.70272315e-01 -8.13554525e-01 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996b0688-b609-494a-8c18-69b26eb213fd	gamifying-video-object-segmentation	1601.00825	null	http://arxiv.org/abs/1601.00825v1	http://arxiv.org/pdf/1601.00825v1.pdf	Gamifying Video Object Segmentation	Video object segmentation can be considered as one of the most challenging computer vision problems. Indeed, so far, no existing solution is able to effectively deal with the peculiarities of real-world videos, especially in cases of articulated motion and object occlusions; limitations that appear more evident when we compare their performance with the human one. However, manually segmenting objects in videos is largely impractical as it requires a lot of human time and concentration. To address this problem, in this paper we propose an interactive video object segmentation method, which exploits, on one hand, the capability of humans to identify correctly objects in visual scenes, and on the other hand, the collective human brainpower to solve challenging tasks. In particular, our method relies on a web game to collect human inputs on object locations, followed by an accurate segmentation phase achieved by optimizing an energy function encoding spatial and temporal constraints between object regions as well as human-provided input. Performance analysis carried out on challenging video datasets with some users playing the game demonstrated that our method shows a better trade-off between annotation times and segmentation accuracy than interactive video annotation and automated video object segmentation approaches.	['Concetto Spampinato', 'Simone Palazzo', 'Daniela Giordano']	2016-01-05	null	null	null	null	['interactive-video-object-segmentation']	['computer-vision']	[ 1.85626701e-01 1.17766216e-01 2.11543307e-01 -1.41068369e-01 -4.12667662e-01 -8.13423634e-01 3.44602823e-01 2.81261057e-01 -8.45409274e-01 5.52751362e-01 -3.87963116e-01 -4.80333529e-02 -5.86843006e-02 -5.35440683e-01 -5.68914533e-01 -6.08906150e-01 2.72861309e-02 6.82004452e-01 7.75767148e-01 -1.54276133e-01 3.15685481e-01 5.70547402e-01 -1.74242008e+00 -5.49904071e-02 9.45182979e-01 9.55848575e-01 5.68793595e-01 6.69920146e-01 -1.66763604e-01 7.02055275e-01 -5.24976552e-01 -3.40554953e-01 2.87582338e-01 -3.74561757e-01 -9.43572223e-01 5.93564868e-01 2.75192827e-01 -2.77119756e-01 1.22865513e-01 1.12224555e+00 2.20096394e-01 4.30920511e-01 5.66553950e-01 -1.12147129e+00 6.83757197e-03 3.04801077e-01 -4.10902888e-01 4.51168060e-01 4.56143469e-01 4.22536194e-01 8.41531038e-01 -4.90932822e-01 8.37021887e-01 7.87002623e-01 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03edaefc-b200-4801-af07-a9d186ddefa2	dual-curriculum-teacher-for-domain	2210.08748	null	https://arxiv.org/abs/2210.08748v1	https://arxiv.org/pdf/2210.08748v1.pdf	Dual-Curriculum Teacher for Domain-Inconsistent Object Detection in Autonomous Driving	Object detection for autonomous vehicles has received increasing attention in recent years, where labeled data are often expensive while unlabeled data can be collected readily, calling for research on semi-supervised learning for this area. Existing semi-supervised object detection (SSOD) methods usually assume that the labeled and unlabeled data come from the same data distribution. In autonomous driving, however, data are usually collected from different scenarios, such as different weather conditions or different times in a day. Motivated by this, we study a novel but challenging domain inconsistent SSOD problem. It involves two kinds of distribution shifts among different domains, including (1) data distribution discrepancy, and (2) class distribution shifts, making existing SSOD methods suffer from inaccurate pseudo-labels and hurting model performance. To address this problem, we propose a novel method, namely Dual-Curriculum Teacher (DucTeacher). Specifically, DucTeacher consists of two curriculums, i.e., (1) domain evolving curriculum seeks to learn from the data progressively to handle data distribution discrepancy by estimating the similarity between domains, and (2) distribution matching curriculum seeks to estimate the class distribution for each unlabeled domain to handle class distribution shifts. In this way, DucTeacher can calibrate biased pseudo-labels and handle the domain-inconsistent SSOD problem effectively. DucTeacher shows its advantages on SODA10M, the largest public semi-supervised autonomous driving dataset, and COCO, a widely used SSOD benchmark. Experiments show that DucTeacher achieves new state-of-the-art performance on SODA10M with 2.2 mAP improvement and on COCO with 0.8 mAP improvement.	['Zhenguo Li', 'Fei Chen', 'Lanqing Hong', 'Yifan Zhang', 'Longhui Yu']	2022-10-17	null	null	null	null	['semi-supervised-object-detection']	['computer-vision']	[-3.44912745e-02 -3.66787687e-02 -4.75116700e-01 -7.88372099e-01 -7.56967843e-01 -5.76486349e-01 4.82800484e-01 4.03949134e-02 -5.46306670e-01 6.89594030e-01 -2.66929686e-01 -2.54516304e-01 1.53145090e-01 -5.39372981e-01 -9.57622170e-01 -7.08138764e-01 4.34082806e-01 9.06679273e-01 7.68081188e-01 -2.14188308e-01 -6.82593957e-02 2.11635470e-01 -1.77909684e+00 -8.09398219e-02 1.14162064e+00 1.06112862e+00 4.14139390e-01 1.65234581e-01 -3.22625965e-01 5.57155490e-01 -6.33646369e-01 -1.46815628e-02 3.22544396e-01 -3.11480999e-01 -6.69517696e-01 3.01882476e-01 5.61618447e-01 -4.27099705e-01 -3.80166888e-01 1.23559821e+00 3.24718148e-01 -1.12881951e-01 7.71618128e-01 -1.80956817e+00 -4.17850912e-01 4.69309658e-01 -8.07671964e-01 3.00363660e-01 -4.32412773e-01 2.21330687e-01 5.96064329e-01 -8.75996590e-01 4.78405386e-01 1.32243812e+00 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c2990eb0-90ae-4f8c-9597-f0a4e90fb6b8	restoreformer-high-quality-blind-face	2201.06374	null	https://arxiv.org/abs/2201.06374v3	https://arxiv.org/pdf/2201.06374v3.pdf	RestoreFormer: High-Quality Blind Face Restoration from Undegraded Key-Value Pairs	Blind face restoration is to recover a high-quality face image from unknown degradations. As face image contains abundant contextual information, we propose a method, RestoreFormer, which explores fully-spatial attentions to model contextual information and surpasses existing works that use local operators. RestoreFormer has several benefits compared to prior arts. First, unlike the conventional multi-head self-attention in previous Vision Transformers (ViTs), RestoreFormer incorporates a multi-head cross-attention layer to learn fully-spatial interactions between corrupted queries and high-quality key-value pairs. Second, the key-value pairs in ResotreFormer are sampled from a reconstruction-oriented high-quality dictionary, whose elements are rich in high-quality facial features specifically aimed for face reconstruction, leading to superior restoration results. Third, RestoreFormer outperforms advanced state-of-the-art methods on one synthetic dataset and three real-world datasets, as well as produces images with better visual quality.	['Ping Luo', 'Wenping Wang', 'Runjian Chen', 'Jiawei Zhang', 'Zhouxia Wang']	2022-01-17	null	http://openaccess.thecvf.com//content/CVPR2022/html/Wang_RestoreFormer_High-Quality_Blind_Face_Restoration_From_Undegraded_Key-Value_Pairs_CVPR_2022_paper.html	http://openaccess.thecvf.com//content/CVPR2022/papers/Wang_RestoreFormer_High-Quality_Blind_Face_Restoration_From_Undegraded_Key-Value_Pairs_CVPR_2022_paper.pdf	cvpr-2022-1	['blind-face-restoration', 'face-reconstruction']	['computer-vision', 'computer-vision']	[ 1.08161755e-01 -3.98407996e-01 7.80062452e-02 -4.36762065e-01 -1.03679121e+00 -9.80038103e-03 4.30698812e-01 -7.15184867e-01 1.03808753e-01 6.69818461e-01 9.33722436e-01 3.05251926e-01 -1.79591626e-01 -5.23121357e-01 -9.07692671e-01 -7.58270741e-01 2.68491805e-01 6.98911175e-02 -4.91647422e-01 -3.02703589e-01 1.14287831e-01 5.80747128e-01 -1.91297555e+00 6.00278735e-01 8.34243953e-01 1.14669049e+00 2.33672962e-01 3.88262808e-01 1.77632570e-01 8.21348846e-01 -4.98263568e-01 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2ceee8ef-6716-46d1-862f-a2a0dbe06f29	do-transformers-dream-of-inference-or-can	null	null	https://aclanthology.org/2020.insights-1.12	https://aclanthology.org/2020.insights-1.12.pdf	Do Transformers Dream of Inference, or Can Pretrained Generative Models Learn Implicit Inferential Rules?	Large pretrained language models (LM) have been used successfully for multi-hop question answering. However, most of these directions are not interpretable, as they do not make the inference hops necessary to explain a candidate answer explicitly. In this work, we investigate the capability of a state-of-the-art transformer LM to generate explicit inference hops, i.e., to infer a new statement necessary to answer a question given some premise input statements. Our analysis shows that such LMs can generate new statements for some simple inference types, but performance remains poor for complex, real-world inference types such as those that require monotonicity, composition, and commonsense knowledge.	['Mihai Surdeanu', 'Zhengzhong Liang']	null	null	null	null	emnlp-insights-2020-11	['multi-hop-question-answering']	['knowledge-base']	[ 4.14776951e-01 8.62511575e-01 -2.75406569e-01 -5.08376718e-01 -7.30791867e-01 -7.35573053e-01 5.06460965e-01 4.07144487e-01 -1.27342284e-01 1.10777652e+00 2.11222529e-01 -1.25303602e+00 -6.16453588e-02 -1.20120907e+00 -9.77089584e-01 3.13968778e-01 3.67888212e-01 6.58782423e-01 5.66866040e-01 -4.88659024e-01 1.77345917e-01 1.55026004e-01 -1.42043746e+00 7.93127894e-01 1.08708906e+00 5.71356297e-01 -1.78159371e-01 7.05626428e-01 -7.10254908e-01 1.56838679e+00 -7.47474194e-01 -8.22237074e-01 -4.08207893e-01 -6.89395726e-01 -1.59357429e+00 -5.63583434e-01 5.76566339e-01 -3.08547080e-01 1.20555259e-01 9.92619038e-01 5.32410853e-02 -7.73852766e-02 5.64894140e-01 -1.24744225e+00 -1.03556812e+00 1.32512379e+00 3.36202115e-01 4.77690309e-01 9.44072843e-01 5.09707108e-02 1.60061133e+00 -7.74587989e-01 5.48303545e-01 1.51776123e+00 6.11979663e-01 7.98771679e-01 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8f7d9dd8-9a46-489e-8292-ad2862097b76	optimizing-answer-set-computation-via	1812.09718	null	http://arxiv.org/abs/1812.09718v2	http://arxiv.org/pdf/1812.09718v2.pdf	Optimizing Answer Set Computation via Heuristic-Based Decomposition	Answer Set Programming (ASP) is a purely declarative formalism developed in the field of logic programming and nonmonotonic reasoning: computational problems are encoded by logic programs whose answer sets, corresponding to solutions, are computed by an ASP system. Different, semantically equivalent, programs can be defined for the same problem; however, performance of systems evaluating them might significantly vary. We propose an approach for automatically transforming an input logic program into an equivalent one that can be evaluated more efficiently. One can make use of existing tree-decomposition techniques for rewriting selected rules into a set of multiple ones; the idea is to guide and adaptively apply them on the basis of proper new heuristics, to obtain a smart rewriting algorithm to be integrated into an ASP system. The method is rather general: it can be adapted to any system and implement different preference policies. Furthermore, we define a set of new heuristics tailored at optimizing grounding, one of the main phases of the ASP computation; we use them in order to implement the approach into the ASP system DLV, in particular into its grounding subsystem I-DLV, and carry out an extensive experimental activity for assessing the impact of the proposal. Under consideration in Theory and Practice of Logic Programming (TPLP).	['Jessica Zangari', 'Simona Perri', 'Francesco Calimeri']	2018-12-23	null	null	null	null	['tree-decomposition']	['graphs']	[ 7.22829774e-02 7.78906286e-01 -1.52000606e-01 -7.16254830e-01 -2.90506929e-01 -9.25462484e-01 4.14928645e-01 5.24411321e-01 -8.10401049e-03 5.49845874e-01 8.43189191e-03 -4.14350957e-01 -5.76931059e-01 -1.45957851e+00 -6.55305088e-01 -2.56016344e-01 2.75867850e-01 1.06150889e+00 8.53157461e-01 -6.47920132e-01 1.00625306e-01 6.29956782e-01 -1.99916887e+00 6.27200961e-01 8.36088121e-01 7.88758457e-01 2.22779196e-02 2.28037983e-01 -3.35638404e-01 9.77905631e-01 -8.58970657e-02 -5.73603213e-01 3.76136959e-01 -2.63862014e-01 -1.29924989e+00 -1.51117817e-01 -2.55541354e-02 1.61520168e-01 2.89562374e-01 1.26991796e+00 -1.31023780e-01 7.10751712e-02 2.22136989e-01 -1.23159158e+00 -1.51235670e-01 9.66133714e-01 5.00528872e-01 -2.72426605e-01 8.53551805e-01 1.79955825e-01 1.18913352e+00 -2.92323112e-01 1.00938809e+00 1.23310268e+00 2.45606586e-01 4.88256574e-01 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-2.62820959e-01 2.46719941e-01 -4.09442216e-01 -4.42879021e-01 3.69105563e-02 2.25838378e-01]	[8.61484432220459, 6.697964668273926]
93309706-854b-4b9a-b851-ed8abecbffd4	fast-image2point-towards-real-time-point	2209.10029	null	https://arxiv.org/abs/2209.10029v1	https://arxiv.org/pdf/2209.10029v1.pdf	Fast-Image2Point: Towards Real-Time Point Cloud Reconstruction of a Single Image using 3D Supervision	A key question in the problem of 3D reconstruction is how to train a machine or a robot to model 3D objects. Many tasks like navigation in real-time systems such as autonomous vehicles directly depend on this problem. These systems usually have limited computational power. Despite considerable progress in 3D reconstruction systems in recent years, applying them to real-time systems such as navigation systems in autonomous vehicles is still challenging due to the high complexity and computational demand of the existing methods. This study addresses current problems in reconstructing objects displayed in a single-view image in a faster (real-time) fashion. To this end, a simple yet powerful deep neural framework is developed. The proposed framework consists of two components: the feature extractor module and the 3D generator module. We use point cloud representation for the output of our reconstruction module. The ShapeNet dataset is utilized to compare the method with the existing results in terms of computation time and accuracy. Simulations demonstrate the superior performance of the proposed method. Index Terms-Real-time 3D reconstruction, single-view reconstruction, supervised learning, deep neural network	['Kamran Ghaffari T', 'Amir G. Aghdam', 'AmirHossein Zamani']	2022-09-20	null	null	null	null	['point-cloud-reconstruction']	['computer-vision']	[ 4.89663482e-02 -1.42643183e-01 3.54454786e-01 -5.63114047e-01 -3.75795096e-01 -4.46938485e-01 7.16612935e-01 -3.80451709e-01 -2.80351490e-01 1.93649858e-01 -5.85564911e-01 -5.30345380e-01 9.65220630e-02 -1.14763021e+00 -8.85281265e-01 -3.91430706e-01 2.87955433e-01 9.01720881e-01 4.50994134e-01 -3.57354730e-01 3.30461800e-01 1.20554399e+00 -1.98630285e+00 -1.06224909e-01 7.45003462e-01 1.18414450e+00 7.19148636e-01 4.73679841e-01 -2.03695834e-01 2.72091061e-01 2.55068839e-02 -3.26676830e-03 6.47843122e-01 -7.52601996e-02 -2.87095726e-01 2.04620615e-01 2.61264384e-01 -6.20084167e-01 -3.64802867e-01 1.04309475e+00 6.15517020e-01 8.52569863e-02 7.29038596e-01 -1.20949328e+00 -2.41207350e-02 -2.33933911e-01 -4.23322946e-01 -1.95415050e-01 1.71125010e-01 -4.67663594e-02 2.78055936e-01 -1.18151689e+00 7.84462869e-01 1.34347320e+00 6.60841405e-01 4.37332600e-01 -8.72257292e-01 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2.34104946e-01 2.93088585e-01 -9.36074495e-01 1.12402700e-01 -3.37937288e-02]	[8.222725868225098, -2.978959798812866]
03298930-c07c-4baa-81d3-3c6d1616f850	vitpose-simple-vision-transformer-baselines	2204.12484	null	https://arxiv.org/abs/2204.12484v3	https://arxiv.org/pdf/2204.12484v3.pdf	ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation	Although no specific domain knowledge is considered in the design, plain vision transformers have shown excellent performance in visual recognition tasks. However, little effort has been made to reveal the potential of such simple structures for pose estimation tasks. In this paper, we show the surprisingly good capabilities of plain vision transformers for pose estimation from various aspects, namely simplicity in model structure, scalability in model size, flexibility in training paradigm, and transferability of knowledge between models, through a simple baseline model called ViTPose. Specifically, ViTPose employs plain and non-hierarchical vision transformers as backbones to extract features for a given person instance and a lightweight decoder for pose estimation. It can be scaled up from 100M to 1B parameters by taking the advantages of the scalable model capacity and high parallelism of transformers, setting a new Pareto front between throughput and performance. Besides, ViTPose is very flexible regarding the attention type, input resolution, pre-training and finetuning strategy, as well as dealing with multiple pose tasks. We also empirically demonstrate that the knowledge of large ViTPose models can be easily transferred to small ones via a simple knowledge token. Experimental results show that our basic ViTPose model outperforms representative methods on the challenging MS COCO Keypoint Detection benchmark, while the largest model sets a new state-of-the-art. The code and models are available at https://github.com/ViTAE-Transformer/ViTPose.	['DaCheng Tao', 'Qiming Zhang', 'Jing Zhang', 'Yufei Xu']	2022-04-26	null	null	null	null	['2d-human-pose-estimation']	['computer-vision']	[-6.80032894e-02 1.33971900e-01 -1.72114894e-02 -2.29412422e-01 -8.36108148e-01 -6.36316419e-01 5.65753698e-01 -2.10900381e-01 -4.78491575e-01 2.65529484e-01 2.00815573e-01 4.63757776e-02 5.64090684e-02 -3.79847378e-01 -9.63284910e-01 -4.62642819e-01 1.11634903e-01 8.31884682e-01 5.05543053e-01 -1.48007005e-01 -1.04496926e-01 3.58551562e-01 -1.46953702e+00 1.96421802e-01 4.08072472e-01 1.08962607e+00 4.43871230e-01 8.68984640e-01 3.44340444e-01 4.04101163e-01 -5.34655154e-01 -9.02122796e-01 4.17466402e-01 5.08234315e-02 -7.46167898e-01 4.10450483e-03 9.67218637e-01 -5.95559001e-01 -6.12006843e-01 6.21886671e-01 1.03919280e+00 -3.56181890e-01 4.15998876e-01 -1.15125763e+00 -4.22870219e-01 5.14878452e-01 -5.89195549e-01 2.74950176e-01 3.31034511e-01 5.31534553e-01 9.83171880e-01 -1.02138579e+00 3.67031932e-01 1.26319587e+00 9.47863042e-01 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7a38e362-8bb3-4c39-8a0b-a8ba80ee6c53	the-as-nu-system-for-the-m2voc-challenge	2104.03009	null	https://arxiv.org/abs/2104.03009v1	https://arxiv.org/pdf/2104.03009v1.pdf	The AS-NU System for the M2VoC Challenge	This paper describes the AS-NU systems for two tracks in MultiSpeaker Multi-Style Voice Cloning Challenge (M2VoC). The first track focuses on using a small number of 100 target utterances for voice cloning, while the second track focuses on using only 5 target utterances for voice cloning. Due to the serious lack of data in the second track, we selected the speaker most similar to the target speaker from the training data of the TTS system, and used the speaker's utterances and the given 5 target utterances to fine-tune our model. The evaluation results show that our systems on the two tracks perform similarly in terms of quality, but there is still a clear gap between the similarity score of the second track and the similarity score of the first track.	['Hsin-Min Wang', 'Yu Tsao', 'Tomoki Toda', 'Pin-Jui Ku', 'Yu-Wen Chen', 'Yu-Huai Peng', 'Wen-Chin Huang', 'Yi-Chiao Wu', 'Cheng-Hung Hu']	2021-04-07	null	null	null	null	['voice-cloning']	['speech']	[ 1.27138272e-01 -2.32465076e-03 2.14311451e-01 -2.16596305e-01 -1.36370456e+00 -6.31004333e-01 3.24148655e-01 -4.18467641e-01 -3.16523641e-01 4.35779184e-01 6.13200247e-01 -4.02225584e-01 2.84796238e-01 1.62710905e-01 -2.50918418e-01 -6.13189697e-01 4.28480387e-01 5.92563212e-01 4.09243703e-01 -3.75374496e-01 -5.20935729e-02 2.92547017e-01 -1.23478401e+00 3.41125667e-01 4.40477997e-01 6.86066866e-01 3.40498298e-01 1.23817980e+00 -1.85729682e-01 5.66307902e-01 -9.76218998e-01 -4.11263138e-01 4.45733964e-02 -1.04681182e+00 -9.56833482e-01 -1.37301404e-02 6.74981833e-01 1.43095898e-02 -2.92315483e-01 9.25942361e-01 1.21629333e+00 2.59427875e-01 2.07664654e-01 -1.10632646e+00 -1.13513350e-01 9.23314989e-01 -7.64007419e-02 5.83259106e-01 4.96855706e-01 -2.04124212e-01 1.11311662e+00 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7841d024-3b69-4a52-9cbb-b9f9ae6082fd	shift-robust-molecular-relational-learning	2305.18451	null	https://arxiv.org/abs/2305.18451v2	https://arxiv.org/pdf/2305.18451v2.pdf	Shift-Robust Molecular Relational Learning with Causal Substructure	Recently, molecular relational learning, whose goal is to predict the interaction behavior between molecular pairs, got a surge of interest in molecular sciences due to its wide range of applications. In this work, we propose CMRL that is robust to the distributional shift in molecular relational learning by detecting the core substructure that is causally related to chemical reactions. To do so, we first assume a causal relationship based on the domain knowledge of molecular sciences and construct a structural causal model (SCM) that reveals the relationship between variables. Based on the SCM, we introduce a novel conditional intervention framework whose intervention is conditioned on the paired molecule. With the conditional intervention framework, our model successfully learns from the causal substructure and alleviates the confounding effect of shortcut substructures that are spuriously correlated to chemical reactions. Extensive experiments on various tasks with real-world and synthetic datasets demonstrate the superiority of CMRL over state-of-the-art baseline models. Our code is available at https://github.com/Namkyeong/CMRL.	['Chanyoung Park', 'Sein Kim', 'Gyoung S. Na', 'Kanghoon Yoon', 'Namkyeong Lee']	2023-05-29	null	null	null	null	['relational-reasoning']	['natural-language-processing']	[ 4.99173075e-01 -1.22021377e-01 -5.70141613e-01 -4.05125618e-01 -4.34803337e-01 -5.21107078e-01 6.74577534e-01 5.35018623e-01 4.70103882e-02 1.01880062e+00 3.20881397e-01 -6.13919377e-01 -3.84962589e-01 -9.20351982e-01 -1.21735942e+00 -8.87342155e-01 -7.31229708e-02 3.38711254e-02 1.29861981e-01 6.27554059e-02 2.74249613e-01 3.45170259e-01 -9.01000142e-01 4.08381969e-01 1.12025094e+00 4.14236248e-01 2.56869197e-01 1.49337590e-01 -4.68411669e-02 9.91323173e-01 -1.41933247e-01 -3.56944978e-01 -1.15578920e-01 -7.09434211e-01 -7.64242351e-01 -5.48900723e-01 2.72996396e-01 3.19957018e-01 -6.58282220e-01 1.01747549e+00 4.31441069e-01 7.40396902e-02 8.23417068e-01 -1.22682917e+00 -7.47782469e-01 7.57326126e-01 -6.02152407e-01 2.01837361e-01 3.32664460e-01 2.67485440e-01 1.44568598e+00 -9.83903050e-01 6.82630241e-01 1.59313941e+00 3.67691875e-01 3.69127393e-01 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-3.03657383e-01 3.70242566e-01 2.69029051e-01 -8.64382207e-01 -1.04855463e-01 1.79398015e-01]	[5.195934772491455, 5.9106764793396]
4bdc6370-11cd-43e0-9053-5709ee6d387c	divide-and-conquer-text-semantic-matching	null	null	https://openreview.net/forum?id=TVsyDo9hbX	https://openreview.net/pdf?id=TVsyDo9hbX	Divide and Conquer: Text Semantic Matching with Disentangled Keywords and Intents	Text semantic matching is a fundamental task that has been widely used in various scenarios, such as community question answering, information retrieval, and recommendation. Most state-of-the-art matching models, e.g., BERT, directly perform text comparison by processing each word uniformly. However, a query sentence generally comprises content that calls for different levels of matching granularity. Specifically, keywords represent factual information such as action, entity, and event that should be strictly matched, while intents convey abstract concepts and ideas that can be paraphrased into various expressions. In this work, we propose a simple yet effective training strategy for text semantic matching in a divide-and-conquer manner by disentangling keywords from intents. Our approach can be easily combined with pre-trained language models (PLM) without influencing their inference efficiency, achieving stable performance improvements against a wide range of PLMs on three benchmarks.	['Anonymous']	2022-01-16	null	null	null	acl-arr-january-2022-1	['community-question-answering', 'community-question-answering']	['miscellaneous', 'natural-language-processing']	[ 4.78837222e-01 -1.46578044e-01 -5.27747273e-01 -5.76864898e-01 -6.99337363e-01 -4.28766608e-01 9.54972565e-01 9.70954835e-01 -5.94974041e-01 3.66011381e-01 5.34107566e-01 -4.43781793e-01 -4.08330001e-03 -1.05551004e+00 -4.86987203e-01 -1.12689503e-01 5.65580249e-01 4.67979997e-01 6.38939261e-01 -2.86574453e-01 4.65233266e-01 -2.50260308e-02 -1.62036693e+00 5.46322227e-01 8.72084320e-01 1.01738560e+00 9.26548764e-02 5.33171557e-02 -8.61284554e-01 7.68495023e-01 -5.46997130e-01 -8.01985383e-01 -2.18033984e-01 -3.80532801e-01 -9.94560003e-01 -3.49151045e-01 5.11710227e-01 -1.42320961e-01 -4.24012244e-01 1.15649319e+00 2.40101144e-01 3.93251568e-01 6.26332581e-01 -1.13452089e+00 -4.84385103e-01 7.77534425e-01 -3.28774780e-01 1.31923571e-01 8.62520039e-01 -2.67649293e-01 1.49529648e+00 -6.83389664e-01 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3.42026442e-01 5.48686050e-02 -7.22301841e-01 -7.70735383e-01 -6.85043558e-02 8.99029493e-01 -1.00118764e-01 -4.51415807e-01 -2.93626696e-01 -2.30731443e-02]	[11.067634582519531, 8.095356941223145]
7021cb60-f29c-4b0c-9809-7c2a7371f8ce	bi-noising-diffusion-towards-conditional	2212.07352	null	https://arxiv.org/abs/2212.07352v1	https://arxiv.org/pdf/2212.07352v1.pdf	Bi-Noising Diffusion: Towards Conditional Diffusion Models with Generative Restoration Priors	Conditional diffusion probabilistic models can model the distribution of natural images and can generate diverse and realistic samples based on given conditions. However, oftentimes their results can be unrealistic with observable color shifts and textures. We believe that this issue results from the divergence between the probabilistic distribution learned by the model and the distribution of natural images. The delicate conditions gradually enlarge the divergence during each sampling timestep. To address this issue, we introduce a new method that brings the predicted samples to the training data manifold using a pretrained unconditional diffusion model. The unconditional model acts as a regularizer and reduces the divergence introduced by the conditional model at each sampling step. We perform comprehensive experiments to demonstrate the effectiveness of our approach on super-resolution, colorization, turbulence removal, and image-deraining tasks. The improvements obtained by our method suggest that the priors can be incorporated as a general plugin for improving conditional diffusion models.	['Vishal M. Patel', 'Nithin Gopalakrishnan Nair', 'Kangfu Mei']	2022-12-14	null	null	null	null	['colorization']	['computer-vision']	[ 1.06846273e-01 -1.14634521e-01 2.69921333e-01 -2.63207406e-01 -5.85889995e-01 -2.96305597e-01 7.84517705e-01 -4.69862401e-01 -1.81304559e-01 7.65581250e-01 2.60314345e-01 2.31928378e-01 1.63586095e-01 -7.83497989e-01 -5.47759950e-01 -1.04151917e+00 3.21115911e-01 2.72624463e-01 5.85608006e-01 1.45164385e-01 3.23518127e-01 5.28463662e-01 -1.49321997e+00 3.31145823e-01 1.21266663e+00 5.53657830e-01 5.97207725e-01 7.40989506e-01 -2.67210484e-01 9.27327871e-01 -5.40560603e-01 -5.45723923e-02 2.42118523e-01 -5.17774403e-01 -5.22179723e-01 4.25487697e-01 1.86551705e-01 -4.82903779e-01 -2.24215403e-01 1.33945501e+00 3.13229531e-01 3.68339241e-01 9.14574146e-01 -7.32042074e-01 -8.86871159e-01 3.64510030e-01 -1.08256876e+00 1.23462260e-01 6.97228238e-02 2.80030102e-01 4.12732512e-01 -8.38305712e-01 6.81258559e-01 1.40393221e+00 5.57157040e-01 5.74415922e-01 -1.63895893e+00 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-8.14967930e-01 4.99368638e-01 4.30858105e-01 -5.28193891e-01 -6.91805661e-01 4.97475386e-01]	[11.539217948913574, -2.115844488143921]
4bdc812f-c272-4c67-ad09-de9bec82dfaa	understanding-breast-cancer-survival-using	2305.18410	null	https://arxiv.org/abs/2305.18410v1	https://arxiv.org/pdf/2305.18410v1.pdf	Understanding Breast Cancer Survival: Using Causality and Language Models on Multi-omics Data	The need for more usable and explainable machine learning models in healthcare increases the importance of developing and utilizing causal discovery algorithms, which aim to discover causal relations by analyzing observational data. Explainable approaches aid clinicians and biologists in predicting the prognosis of diseases and suggesting proper treatments. However, very little research has been conducted at the crossroads between causal discovery, genomics, and breast cancer, and we aim to bridge this gap. Moreover, evaluation of causal discovery methods on real data is in general notoriously difficult because ground-truth causal relations are usually unknown, and accordingly, in this paper, we also propose to address the evaluation problem with large language models. In particular, we exploit suitable causal discovery algorithms to investigate how various perturbations in the genome can affect the survival of patients diagnosed with breast cancer. We used three main causal discovery algorithms: PC, Greedy Equivalence Search (GES), and a Generalized Precision Matrix-based one. We experiment with a subset of The Cancer Genome Atlas, which contains information about mutations, copy number variations, protein levels, and gene expressions for 705 breast cancer patients. Our findings reveal important factors related to the vital status of patients using causal discovery algorithms. However, the reliability of these results remains a concern in the medical domain. Accordingly, as another contribution of the work, the results are validated through language models trained on biomedical literature, such as BlueBERT and other large language models trained on medical corpora. Our results profess proper utilization of causal discovery algorithms and language models for revealing reliable causal relations for clinical applications.	['Kun Zhang', 'Preslav Nakov', 'Yujia Zheng', 'Aigerim Zhumbhayeva', 'Shahad Hardan', 'Mugariya Farooq']	2023-05-28	null	null	null	null	['causal-discovery']	['knowledge-base']	[ 3.80992025e-01 3.19290459e-01 -6.51588380e-01 -2.73920894e-01 -5.97780824e-01 -2.00398743e-01 5.74405968e-01 7.34216511e-01 -1.21484324e-01 1.21831965e+00 6.31545126e-01 -7.80507743e-01 -8.91231239e-01 -8.65206122e-01 -8.13694239e-01 -7.81234324e-01 -4.42640841e-01 4.70223904e-01 -2.74485618e-01 -5.41868396e-02 1.10578857e-01 3.27722877e-01 -1.12762892e+00 4.28246975e-01 9.72711861e-01 3.69362861e-01 -1.01592034e-01 4.99206245e-01 -5.97337000e-02 7.60158598e-01 -8.66450816e-02 -5.35309255e-01 -4.67052937e-01 -6.64196730e-01 -7.94322908e-01 -6.62308931e-01 -2.89279997e-01 2.03084975e-01 -1.39809847e-01 8.19855034e-01 3.46588731e-01 -5.57241023e-01 5.34989417e-01 -1.29497206e+00 -4.92755771e-01 1.16090918e+00 -3.92950773e-01 1.14886276e-01 3.86473686e-01 -4.17100079e-03 1.12813365e+00 -5.06115615e-01 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89d8c3eb-0f5a-4bfa-903e-e687cc89d054	khanq-a-dataset-for-generating-deep-questions	null	null	https://aclanthology.org/2022.coling-1.518	https://aclanthology.org/2022.coling-1.518.pdf	KHANQ: A Dataset for Generating Deep Questions in Education	Designing in-depth educational questions is a time-consuming and cognitively demanding task. Therefore, it is intriguing to study how to build Question Generation (QG) models to automate the question creation process. However, existing QG datasets are not suitable for educational question generation because the questions are not real questions asked by humans during learning and can be solved by simply searching for information. To bridge this gap, we present KHANQ, a challenging dataset for educational question generation, containing 1,034 high-quality learner-generated questions seeking an in-depth understanding of the taught online courses in Khan Academy. Each data sample is carefully paraphrased and annotated as a triple of 1) Context: an independent paragraph on which the question is based; 2) Prompt: a text prompt for the question (e.g., the learner’s background knowledge); 3) Question: a deep question based on Context and coherent with Prompt. By conducting a human evaluation on the aspects of appropriateness, coverage, coherence, and complexity, we show that state-of-the-art QG models which perform well on shallow question generation datasets have difficulty in generating useful educational questions. This makes KHANQ a challenging testbed for educational question generation.	['Hengchang Hu', 'Liangming Pan', 'Huanli Gong']	null	null	null	null	coling-2022-10	['question-generation']	['natural-language-processing']	[ 8.14898983e-02 4.44735914e-01 3.11621219e-01 -3.70702505e-01 -9.89131391e-01 -1.00154209e+00 5.60059369e-01 6.65804565e-01 -2.20630929e-01 7.36030281e-01 4.28616971e-01 -9.13610578e-01 -4.64410871e-01 -1.11245334e+00 -7.14312613e-01 1.51938677e-01 5.31597733e-01 3.42767000e-01 5.70553601e-01 -7.85020292e-01 5.15243590e-01 2.07456462e-02 -1.87439966e+00 5.36681294e-01 1.59269631e+00 6.82546973e-01 3.80748928e-01 8.74923587e-01 -8.39147568e-01 1.20504129e+00 -1.19823444e+00 -4.68275189e-01 -2.48156399e-01 -1.21863723e+00 -1.48449719e+00 -3.21659707e-02 9.53615010e-01 -3.16285759e-01 -1.27942994e-01 7.78035045e-01 4.63127375e-01 3.79457951e-01 3.69021982e-01 -1.20036542e+00 -1.01597440e+00 5.78249395e-01 5.18201768e-01 3.31020802e-01 1.20502412e+00 1.35404021e-01 9.65214252e-01 -7.99909174e-01 6.52179420e-01 8.48929465e-01 3.43522787e-01 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784f6f7b-8bce-4e7f-bbf6-0f85bfe8a97c	stabilized-in-context-learning-with-pre	2302.05932	null	https://arxiv.org/abs/2302.05932v1	https://arxiv.org/pdf/2302.05932v1.pdf	Stabilized In-Context Learning with Pre-trained Language Models for Few Shot Dialogue State Tracking	Prompt-based methods with large pre-trained language models (PLMs) have shown impressive unaided performance across many NLP tasks. These models improve even further with the addition of a few labeled in-context exemplars to guide output generation. However, for more complex tasks such as dialogue state tracking (DST), designing prompts that reliably convey the desired intent is nontrivial, leading to unstable results. Furthermore, building in-context exemplars for dialogue tasks is difficult because conversational contexts are long while model input lengths are relatively short. To overcome these issues we first adapt a meta-learning scheme to the dialogue domain which stabilizes the ability of the model to perform well under various prompts. We additionally design a novel training method to improve upon vanilla retrieval mechanisms to find ideal in-context examples. Finally, we introduce a saliency model to limit dialogue text length, allowing us to include more exemplars per query. In effect, we are able to achieve highly competitive results for few-shot DST on MultiWOZ.	['Zhou Yu', 'Kun Qian', 'Derek Chen']	2023-02-12	null	null	null	null	['dialogue-state-tracking']	['natural-language-processing']	[ 3.78182650e-01 3.61820579e-01 -1.36605054e-01 -4.22997713e-01 -1.11515450e+00 -5.53910196e-01 1.11649966e+00 2.73803651e-01 -4.94278818e-01 8.17248821e-01 7.28894413e-01 -2.14254424e-01 2.00305462e-01 -4.40117955e-01 -2.83169448e-01 -1.69139564e-01 4.65229265e-02 7.05185890e-01 3.40806574e-01 -7.75698960e-01 5.22093832e-01 -3.42405401e-02 -1.33311021e+00 6.00878477e-01 9.37179625e-01 2.55375683e-01 5.28737366e-01 9.83763695e-01 -5.76422691e-01 6.82309091e-01 -1.03161180e+00 -2.54432023e-01 -8.52672011e-02 -7.68512487e-01 -1.05153990e+00 9.82016511e-03 5.28448880e-01 -4.27210480e-01 -3.52135539e-01 5.05015135e-01 6.98232532e-01 6.42662525e-01 6.88677073e-01 -9.77861524e-01 -3.95028502e-01 7.79235542e-01 -1.84157908e-01 1.09233871e-01 7.92730510e-01 3.08090210e-01 1.13902509e+00 -9.02446091e-01 9.52211678e-01 1.59593451e+00 2.86300868e-01 1.01949716e+00 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c4e2d665-b02d-4bd2-9ab5-8f125df2370b	retrieval-augmented-multi-label-text	2305.13058	null	https://arxiv.org/abs/2305.13058v1	https://arxiv.org/pdf/2305.13058v1.pdf	Retrieval-augmented Multi-label Text Classification	Multi-label text classification (MLC) is a challenging task in settings of large label sets, where label support follows a Zipfian distribution. In this paper, we address this problem through retrieval augmentation, aiming to improve the sample efficiency of classification models. Our approach closely follows the standard MLC architecture of a Transformer-based encoder paired with a set of classification heads. In our case, however, the input document representation is augmented through cross-attention to similar documents retrieved from the training set and represented in a task-specific manner. We evaluate this approach on four datasets from the legal and biomedical domains, all of which feature highly skewed label distributions. Our experiments show that retrieval augmentation substantially improves model performance on the long tail of infrequent labels especially so for lower-resource training scenarios and more challenging long-document data scenarios.	['Yova Kementchedjhieva', 'Ilias Chalkidis']	2023-05-22	null	null	null	null	['multi-label-text-classification', 'multi-label-text-classification']	['methodology', 'natural-language-processing']	[ 7.15922296e-01 1.17477626e-02 -5.44881880e-01 -5.96797347e-01 -1.43376136e+00 -6.46155953e-01 7.11588621e-01 4.86432552e-01 -6.91998005e-01 8.22723031e-01 2.90311247e-01 -4.51688558e-01 -2.00761393e-01 -4.51058209e-01 -5.54971635e-01 -6.23586893e-01 2.68193692e-01 9.58597302e-01 -4.97866534e-02 -2.66045295e-02 1.65733889e-01 2.11393639e-01 -1.33615756e+00 6.88201606e-01 5.64275086e-01 9.68851447e-01 -9.50713977e-02 5.04319429e-01 -1.42128676e-01 9.31955040e-01 -4.32019800e-01 -5.14183521e-01 -2.66068224e-02 -1.74000055e-01 -1.11873019e+00 1.61381233e-02 6.16972625e-01 -7.87751526e-02 2.91582500e-03 6.50938034e-01 5.74892759e-01 6.24426827e-02 1.24863982e+00 -1.16824973e+00 -4.31868225e-01 6.27925754e-01 -7.60871053e-01 2.35279258e-02 4.76204380e-02 -4.48270708e-01 1.27815795e+00 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2618a44c-2437-457c-a8e9-054fc5509b55	shoerinsics-shoeprint-prediction-for	2205.02361	null	https://arxiv.org/abs/2205.02361v2	https://arxiv.org/pdf/2205.02361v2.pdf	Creating a Forensic Database of Shoeprints from Online Shoe Tread Photos	Shoe tread impressions are one of the most common types of evidence left at crime scenes. However, the utility of such evidence is limited by the lack of databases of footwear prints that cover the large and growing number of distinct shoe models. Moreover, the database is preferred to contain the 3D shape, or depth, of shoe-tread photos so as to allow for extracting shoeprints to match a query (crime-scene) print. We propose to address this gap by leveraging shoe-tread photos collected by online retailers. The core challenge is to predict depth maps for these photos. As they do not have ground-truth 3D shapes allowing for training depth predictors, we exploit synthetic data that does. We develop a method termed ShoeRinsics that learns to predict depth by leveraging a mix of fully supervised synthetic data and unsupervised retail image data. In particular, we find domain adaptation and intrinsic image decomposition techniques effectively mitigate the synthetic-real domain gap and yield significantly better depth prediction. To validate our method, we introduce 2 validation sets consisting of shoe-tread image and print pairs and define a benchmarking protocol to quantify the quality of predicted depth. On this benchmark, ShoeRinsics outperforms existing methods of depth prediction and synthetic-to-real domain adaptation.	['Charless C. Fowlkes', 'Shu Kong', 'Bailey Kong', 'Samia Shafique']	2022-05-04	null	null	null	null	['intrinsic-image-decomposition']	['computer-vision']	[ 4.37458038e-01 -3.20907123e-03 -2.48172089e-01 -5.55728197e-01 -9.82489347e-01 -6.80977941e-01 4.33245093e-01 -1.48116916e-01 -2.02413559e-01 4.35244679e-01 1.87496349e-01 3.20069529e-02 -2.23013073e-01 -9.76890147e-01 -7.72508323e-01 -2.23942548e-01 3.68753523e-01 7.36199379e-01 4.53016818e-01 -3.30244184e-01 4.39054251e-01 6.31667435e-01 -1.53270781e+00 4.34905261e-01 7.98187971e-01 1.25078797e+00 1.64310619e-01 5.22544801e-01 7.65313581e-02 3.03933293e-01 -1.42789274e-01 -8.20939422e-01 8.91324103e-01 -1.01229765e-01 -2.91359633e-01 4.98108596e-01 1.12425053e+00 -8.04188550e-01 -3.33663225e-01 5.27921498e-01 4.05504793e-01 -2.79483497e-01 9.03885424e-01 -1.04604340e+00 -5.66199958e-01 -2.75833309e-01 -6.35000288e-01 -2.07338989e-01 2.32408896e-01 2.62375176e-01 9.58676636e-01 -8.14331412e-01 1.06175900e+00 9.76904631e-01 9.16560650e-01 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ededa625-a715-4ab2-b637-85dbd6610ac1	practical-digital-disguises-leveraging-face	2204.03559	null	https://arxiv.org/abs/2204.03559v2	https://arxiv.org/pdf/2204.03559v2.pdf	Practical Digital Disguises: Leveraging Face Swaps to Protect Patient Privacy	With rapid advancements in image generation technology, face swapping for privacy protection has emerged as an active area of research. The ultimate benefit is improved access to video datasets, e.g. in healthcare settings. Recent literature has proposed deep network-based architectures to perform facial swaps and reported the associated reduction in facial recognition accuracy. However, there is not much reporting on how well these methods preserve the types of semantic information needed for the privatized videos to remain useful for their intended application. Our main contribution is a novel end-to-end face swapping pipeline for recorded videos of standardized assessments of autism symptoms in children. Through this design, we are the first to provide a methodology for assessing the privacy-utility trade-offs for the face swapping approach to patient privacy protection. Our methodology can show, for example, that current deep network based face swapping is bottle-necked by face detection in real world videos, and the extent to which gaze and expression information is preserved by face swaps relative to baseline privatization methods such as blurring.	['Eakta Jain', 'Jenny Skytta', 'Frederick Shic', 'Ethan Wilson']	2022-04-07	null	null	null	null	['face-detection']	['computer-vision']	[ 4.47674423e-01 6.00240350e-01 3.11527342e-01 -7.48296618e-01 -4.06220555e-01 -7.41823256e-01 2.26512000e-01 -2.21756160e-01 -4.39166814e-01 4.36082959e-01 4.50445145e-01 -1.30407112e-02 -4.80665080e-02 -2.85699815e-01 -7.38710880e-01 -4.11496669e-01 -1.70979828e-01 -7.14033246e-02 -2.34281272e-01 4.78363745e-02 3.50341983e-02 6.13660574e-01 -1.73904502e+00 5.70779145e-01 3.35538566e-01 1.11114740e+00 -2.87985682e-01 3.96347821e-01 3.65208954e-01 6.36657774e-01 -5.02199352e-01 -8.12627256e-01 6.21025324e-01 -2.99770832e-01 -7.54700541e-01 -1.65244564e-01 1.09904075e+00 -1.34679830e+00 -6.44361913e-01 9.90829706e-01 7.33698070e-01 -1.13655880e-01 -8.68726522e-03 -1.41506290e+00 -5.52657247e-01 2.72954851e-01 -4.57386613e-01 1.73736125e-01 4.26479936e-01 3.97772044e-01 5.35727024e-01 -4.49154317e-01 8.46507847e-01 1.27196145e+00 6.66925788e-01 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-2.74884403e-01 -5.97291470e-01 -5.34105957e-01 3.92484099e-01 8.02809238e-01 -4.76401120e-01 -3.09210986e-01 2.86306255e-02]	[12.848980903625488, 0.6741330027580261]
c670c674-7c0b-4566-bac2-3f7ea7a633d9	mimetic-neural-networks-a-unified-framework	2102.03881	null	https://arxiv.org/abs/2102.03881v1	https://arxiv.org/pdf/2102.03881v1.pdf	Mimetic Neural Networks: A unified framework for Protein Design and Folding	Recent advancements in machine learning techniques for protein folding motivate better results in its inverse problem -- protein design. In this work we introduce a new graph mimetic neural network, MimNet, and show that it is possible to build a reversible architecture that solves the structure and design problems in tandem, allowing to improve protein design when the structure is better estimated. We use the ProteinNet data set and show that the state of the art results in protein design can be improved, given recent architectures for protein folding.	['Eran Treister', 'Chen Keasar', 'Eldad Haber', 'Tue Boesen', 'Moshe Eliasof']	2021-02-07	null	null	null	null	['protein-design']	['medical']	[ 4.16146815e-01 4.97880131e-01 -3.32725823e-01 -1.68168321e-01 1.70381069e-01 -5.96646428e-01 -1.29007334e-02 2.79734544e-02 -2.61185914e-01 1.22152197e+00 2.28223339e-01 -8.70336533e-01 2.14318752e-01 -8.14591050e-01 -1.44996691e+00 -7.71151423e-01 -1.93184808e-01 7.08517790e-01 -7.44684786e-02 -7.89092660e-01 3.22468132e-01 7.97019422e-01 -1.07334483e+00 5.24275422e-01 7.38493323e-01 1.37694836e-01 3.36921394e-01 7.87042558e-01 -1.15975887e-01 8.35142434e-01 -2.35903844e-01 -2.76300218e-02 2.03514948e-01 -8.31761181e-01 -1.39350021e+00 -4.54846948e-01 3.44572335e-01 2.50709921e-01 -4.24101204e-01 5.92854023e-01 7.36288667e-01 1.24470681e-01 4.76387382e-01 -3.15785497e-01 -1.34247494e+00 6.88206196e-01 -8.33654478e-02 1.12049147e-01 3.01046878e-01 3.65902245e-01 1.12488544e+00 -6.49730861e-01 1.10883415e+00 1.41943920e+00 9.60879743e-01 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7a88b991-84c2-4ac1-ad5e-f9ccdd5a301c	robust-semi-direct-monocular-visual-odometry	1909.11362	null	https://arxiv.org/abs/1909.11362v2	https://arxiv.org/pdf/1909.11362v2.pdf	Robust Monocular Edge Visual Odometry through Coarse-to-Fine Data Association	In this work, we propose a monocular visual odometry framework, which allows exploiting the best attributes of edge feature for illumination-robust camera tracking, while at the same time ameliorating the performance degradation of edge mapping. In the front-end, an ICP-based edge registration can provide robust motion estimation and coarse data association under lighting changes. In the back-end, a novel edge-guided data association pipeline searches for the best photometrically matched points along geometrically possible edges through template matching, so that the matches can be further refined in later bundle adjustment. The core of our proposed data association strategy lies in a point-to-edge geometric uncertainty analysis, which analytically derives (1) the probabilistic search length formula that significantly reduces the search space for system speed-up and (2) the geometrical confidence metric for mapping degradation detection based on the predicted depth uncertainty. Moreover, match confidence based patch size adaption strategy is integrated into our pipeline, connecting with other components, to reduce the matching ambiguity. We present extensive analysis and evaluation of our proposed system on synthetic and real-world benchmark datasets under the influence of illumination changes and large camera motions, where our proposed system outperforms current state-of-art algorithms.	['Xiaolong Wu', 'Patricio Vela', 'Cedric Pradalier']	2019-09-25	null	null	null	null	['monocular-visual-odometry']	['robots']	[-2.84522753e-02 -1.88181534e-01 2.04233482e-01 -2.90148128e-02 -6.54985189e-01 -6.41425490e-01 3.81357044e-01 1.79241776e-01 -5.29680312e-01 4.66131061e-01 4.81626624e-03 1.04907483e-01 -2.07082838e-01 -8.09447169e-01 -7.26054192e-01 -7.66948104e-01 2.22104222e-01 4.54906642e-01 6.96582913e-01 1.73260137e-01 5.33721387e-01 6.46502435e-01 -1.64594257e+00 -6.33733153e-01 1.01429057e+00 1.33394063e+00 4.03512686e-01 3.10370207e-01 1.22428373e-01 9.37002618e-03 2.67609525e-02 -3.38771760e-01 4.38617945e-01 8.53576511e-02 -1.45976841e-01 2.47907758e-01 5.99066854e-01 -3.15605193e-01 -1.31390020e-01 1.18082500e+00 7.23935127e-01 8.80411044e-02 4.23542887e-01 -1.00372422e+00 -8.93734302e-03 -2.10486934e-01 -6.63429558e-01 -2.12170944e-01 5.22507727e-01 3.39059681e-01 6.35223866e-01 -1.10759950e+00 8.39363098e-01 8.57681572e-01 8.58260095e-01 -4.02517244e-02 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15b273f4-0fd0-4e23-badc-3a31b71fefbb	comparing-nars-and-reinforcement-learning-an	2304.03291	null	https://arxiv.org/abs/2304.03291v2	https://arxiv.org/pdf/2304.03291v2.pdf	Comparing NARS and Reinforcement Learning: An Analysis of ONA and $Q$-Learning Algorithms	In recent years, reinforcement learning (RL) has emerged as a popular approach for solving sequence-based tasks in machine learning. However, finding suitable alternatives to RL remains an exciting and innovative research area. One such alternative that has garnered attention is the Non-Axiomatic Reasoning System (NARS), which is a general-purpose cognitive reasoning framework. In this paper, we delve into the potential of NARS as a substitute for RL in solving sequence-based tasks. To investigate this, we conduct a comparative analysis of the performance of ONA as an implementation of NARS and $Q$-Learning in various environments that were created using the Open AI gym. The environments have different difficulty levels, ranging from simple to complex. Our results demonstrate that NARS is a promising alternative to RL, with competitive performance in diverse environments, particularly in non-deterministic ones.	['Sindri Magnússon', 'Ali Beikmohammadi']	2023-03-17	null	null	null	null	['q-learning']	['methodology']	[-1.58576682e-01 -1.04291737e-01 2.26425380e-01 -2.36159060e-02 -3.20265859e-01 -6.19821548e-01 3.67802560e-01 -1.04628667e-01 -6.88062072e-01 9.42052603e-01 -1.41739666e-01 -5.47641814e-01 -5.67776144e-01 -9.18746412e-01 -4.68765169e-01 -4.70667899e-01 -5.06235175e-02 4.31781739e-01 3.35269541e-01 -8.63656819e-01 5.92149258e-01 5.28442025e-01 -1.92988026e+00 2.11678714e-01 1.11937702e+00 5.76318800e-01 4.97630954e-01 6.54083848e-01 -2.76173562e-01 1.19493830e+00 -7.99288273e-01 -2.88775057e-01 4.24184084e-01 -5.64002037e-01 -1.07723653e+00 -3.81666809e-01 -1.29790932e-01 -5.40114641e-02 -3.63150507e-01 7.12284267e-01 7.07792163e-01 5.42833924e-01 4.22729403e-01 -1.36337721e+00 -3.36910993e-01 6.26109719e-01 -2.06091721e-02 2.51872838e-01 8.31928194e-01 6.28689885e-01 7.82120347e-01 -2.02125937e-01 3.72620493e-01 1.36536205e+00 5.00343561e-01 6.44498825e-01 -9.88467693e-01 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2.78299540e-01 -4.69763607e-01 -8.68406773e-01 -2.41370145e-02 -3.75876397e-01]	[3.887775182723999, 1.480825662612915]
d6517d33-db86-498d-a5cd-d8e3fcb85e13	it5-large-scale-text-to-text-pretraining-for	2203.03759	null	https://arxiv.org/abs/2203.03759v1	https://arxiv.org/pdf/2203.03759v1.pdf	IT5: Large-scale Text-to-text Pretraining for Italian Language Understanding and Generation	The T5 model and its unified text-to-text paradigm contributed in advancing the state-of-the-art for many natural language processing tasks. While some multilingual variants of the T5 model have recently been introduced, their performances were found to provide suboptimal performances for languages other than English if compared to monolingual variants. We are motivated by these findings to introduce IT5, the first family of encoder-decoder transformer models pretrained specifically on Italian. We perform a thorough cleaning of a web-crawled Italian corpus including more than 40 billion words and use it to pretrain three IT5 models of different sizes. The performance of IT5 models and their multilingual counterparts is then evaluated on a broad range of natural language understanding and generation benchmarks for Italian. We find the monolingual IT5 models to provide the best scale-to-performance ratio across tested models, consistently outperforming their multilingual counterparts and setting a new state-of-the-art for most Italian conditional language generation tasks.	['Malvina Nissim', 'Gabriele Sarti']	2022-03-07	null	null	null	null	['text-style-transfoer', 'headline-generation']	['natural-language-processing', 'natural-language-processing']	[ 1.61911383e-01 4.27049607e-01 -1.17076576e-01 -2.69881517e-01 -1.54865062e+00 -7.71784842e-01 1.15224743e+00 -1.93875134e-02 -4.61011052e-01 1.03381479e+00 4.38851386e-01 -5.67320883e-01 3.13253134e-01 -5.38825929e-01 -7.14175045e-01 -2.33630359e-01 1.92560092e-01 1.01465368e+00 8.51773769e-02 -5.43875873e-01 -1.37539417e-01 -1.51375026e-01 -9.34409738e-01 5.64291775e-01 1.06531048e+00 5.21075308e-01 2.07611874e-01 6.16289794e-01 -1.82700083e-02 8.06246281e-01 -7.08668530e-01 -9.23217773e-01 -3.04102451e-02 -3.03028435e-01 -1.10192299e+00 -4.19085503e-01 2.24215910e-01 3.77464220e-02 -7.23562092e-02 6.29334688e-01 5.96037865e-01 -3.82853359e-01 5.94157755e-01 -8.35209727e-01 -9.19287741e-01 1.43111026e+00 -2.74866492e-01 2.10044190e-01 6.22061074e-01 1.55694008e-01 1.08263206e+00 -8.57807636e-01 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93ef3f42-8360-4380-bd74-b479a5ae3f1f	channel-attention-based-iterative-residual	2006.01469	null	https://arxiv.org/abs/2006.01469v1	https://arxiv.org/pdf/2006.01469v1.pdf	Channel Attention based Iterative Residual Learning for Depth Map Super-Resolution	Despite the remarkable progresses made in deep-learning based depth map super-resolution (DSR), how to tackle real-world degradation in low-resolution (LR) depth maps remains a major challenge. Existing DSR model is generally trained and tested on synthetic dataset, which is very different from what would get from a real depth sensor. In this paper, we argue that DSR models trained under this setting are restrictive and not effective in dealing with real-world DSR tasks. We make two contributions in tackling real-world degradation of different depth sensors. First, we propose to classify the generation of LR depth maps into two types: non-linear downsampling with noise and interval downsampling, for which DSR models are learned correspondingly. Second, we propose a new framework for real-world DSR, which consists of four modules : 1) An iterative residual learning module with deep supervision to learn effective high-frequency components of depth maps in a coarse-to-fine manner; 2) A channel attention strategy to enhance channels with abundant high-frequency components; 3) A multi-stage fusion module to effectively re-exploit the results in the coarse-to-fine process; and 4) A depth refinement module to improve the depth map by TGV regularization and input loss. Extensive experiments on benchmarking datasets demonstrate the superiority of our method over current state-of-the-art DSR methods.	['Ruigang Yang', 'Hongdng Li', 'Wei Li', 'Yuchao Dai', 'Xibin Song', 'Liu Liu', 'Dingfu Zhou']	2020-06-02	channel-attention-based-iterative-residual-1	http://openaccess.thecvf.com/content_CVPR_2020/html/Song_Channel_Attention_Based_Iterative_Residual_Learning_for_Depth_Map_Super-Resolution_CVPR_2020_paper.html	http://openaccess.thecvf.com/content_CVPR_2020/papers/Song_Channel_Attention_Based_Iterative_Residual_Learning_for_Depth_Map_Super-Resolution_CVPR_2020_paper.pdf	cvpr-2020-6	['depth-map-super-resolution']	['computer-vision']	[ 6.50934458e-01 1.51349500e-01 8.34820494e-02 -2.30107114e-01 -1.26852262e+00 4.95077297e-02 4.67344254e-01 -3.18188190e-01 -2.62855858e-01 9.79219556e-01 5.17237961e-01 2.95111537e-01 -9.25831571e-02 -1.03336513e+00 -8.61986756e-01 -7.98413754e-01 1.77363202e-01 2.26370156e-01 5.63265920e-01 -4.86166537e-01 2.73775429e-01 5.72796524e-01 -1.86741555e+00 4.81508225e-01 9.65227067e-01 1.16920269e+00 4.41070050e-01 7.09501922e-01 1.10344462e-01 1.07874286e+00 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4c33653e-b836-4fc9-9734-2d527e91ae92	deep-convolutional-sparse-coding-networks-for	2005.08448	null	https://arxiv.org/abs/2005.08448v1	https://arxiv.org/pdf/2005.08448v1.pdf	Deep Convolutional Sparse Coding Networks for Image Fusion	Image fusion is a significant problem in many fields including digital photography, computational imaging and remote sensing, to name but a few. Recently, deep learning has emerged as an important tool for image fusion. This paper presents three deep convolutional sparse coding (CSC) networks for three kinds of image fusion tasks (i.e., infrared and visible image fusion, multi-exposure image fusion, and multi-modal image fusion). The CSC model and the iterative shrinkage and thresholding algorithm are generalized into dictionary convolution units. As a result, all hyper-parameters are learned from data. Our extensive experiments and comprehensive comparisons reveal the superiority of the proposed networks with regard to quantitative evaluation and visual inspection.	['Chun-Xia Zhang', 'Junmin Liu', 'Yicheng Wang', 'Jiangshe Zhang', 'Zixiang Zhao', 'Shuang Xu']	2020-05-18	null	null	null	null	['infrared-and-visible-image-fusion', 'multi-exposure-image-fusion']	['computer-vision', 'computer-vision']	[ 3.18754524e-01 -6.93300784e-01 7.41998032e-02 -3.83382529e-01 -5.19090414e-01 -1.89354017e-01 4.87482190e-01 -3.73002626e-02 -3.73725176e-01 4.69456673e-01 1.87439770e-01 -1.86016515e-01 -2.79538393e-01 -6.10545874e-01 -3.31329584e-01 -1.02024174e+00 2.77248949e-01 -3.50028455e-01 -1.73536032e-01 -2.75621206e-01 -6.04556873e-02 6.60663068e-01 -1.55586624e+00 -1.36892691e-01 1.14746428e+00 1.33171749e+00 3.31406236e-01 4.13492113e-01 8.85660723e-02 5.75056911e-01 -2.41235659e-01 -3.71862084e-01 4.40696090e-01 -2.76584715e-01 -3.48599494e-01 4.01406884e-01 4.81486142e-01 -3.19320351e-01 -6.74263418e-01 1.51460612e+00 6.83109403e-01 3.52267236e-01 3.24556708e-01 -1.02464104e+00 -1.03943551e+00 3.86597514e-01 -9.93341029e-01 4.28463340e-01 -2.54917964e-02 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830013ab-48a6-44ed-9ed0-67e5aee3a1ea	fine-grained-scene-graph-generation-with-data	2203.11654	null	https://arxiv.org/abs/2203.11654v2	https://arxiv.org/pdf/2203.11654v2.pdf	Fine-Grained Scene Graph Generation with Data Transfer	Scene graph generation (SGG) is designed to extract (subject, predicate, object) triplets in images. Recent works have made a steady progress on SGG, and provide useful tools for high-level vision and language understanding. However, due to the data distribution problems including long-tail distribution and semantic ambiguity, the predictions of current SGG models tend to collapse to several frequent but uninformative predicates (e.g., on, at), which limits practical application of these models in downstream tasks. To deal with the problems above, we propose a novel Internal and External Data Transfer (IETrans) method, which can be applied in a plug-and-play fashion and expanded to large SGG with 1,807 predicate classes. Our IETrans tries to relieve the data distribution problem by automatically creating an enhanced dataset that provides more sufficient and coherent annotations for all predicates. By training on the enhanced dataset, a Neural Motif model doubles the macro performance while maintaining competitive micro performance. The code and data are publicly available at https://github.com/waxnkw/IETrans-SGG.pytorch.	['Tat-Seng Chua', 'Maosong Sun', 'Zhiyuan Liu', 'Wei Ji', 'Qianyu Chen', 'Yuan YAO', 'Ao Zhang']	2022-03-22	null	null	null	null	['scene-graph-generation', 'unbiased-scene-graph-generation']	['computer-vision', 'computer-vision']	[ 2.83936679e-01 9.33324397e-02 -1.28457472e-01 -5.08263230e-01 -6.29596531e-01 -5.00226557e-01 4.91560638e-01 -8.79773274e-02 -8.08739588e-02 5.19376516e-01 2.70376831e-01 -3.53267461e-01 1.57900363e-01 -8.72236073e-01 -8.20445955e-01 -6.67154729e-01 1.82512775e-01 4.54359472e-01 4.88516986e-01 3.67459655e-02 1.19489849e-01 2.77749859e-02 -1.80527365e+00 4.97128248e-01 8.21107507e-01 9.40872431e-01 5.56254089e-01 4.08207238e-01 -1.90283760e-01 7.06335664e-01 -3.73676300e-01 -5.55253148e-01 2.18782365e-01 -4.05433178e-01 -7.53582716e-01 1.22798197e-02 4.24291998e-01 -1.15670681e-01 -3.04048121e-01 1.15669751e+00 5.30538678e-01 2.50698719e-02 5.18838167e-01 -1.42559624e+00 -8.12524140e-01 4.67620313e-01 -6.92649901e-01 -9.06357467e-02 2.65044898e-01 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d06eb9ee-3656-44c8-9a4f-5c9861b4ea51	seggpt-meets-co-saliency-scene	2305.04396	null	https://arxiv.org/abs/2305.04396v1	https://arxiv.org/pdf/2305.04396v1.pdf	SegGPT Meets Co-Saliency Scene	Co-salient object detection targets at detecting co-existed salient objects among a group of images. Recently, a generalist model for segmenting everything in context, called SegGPT, is gaining public attention. In view of its breakthrough for segmentation, we can hardly wait to probe into its contribution to the task of co-salient object detection. In this report, we first design a framework to enable SegGPT for the problem of co-salient object detection. Proceed to the next step, we evaluate the performance of SegGPT on the problem of co-salient object detection on three available datasets. We achieve a finding that co-saliency scenes challenges SegGPT due to context discrepancy within a group of co-saliency images.	['Jungong Han', 'Dingwen Zhang', 'Shoukun Xu', 'Yi Liu']	2023-05-08	null	null	null	null	['co-saliency-detection', 'salient-object-detection-1']	['computer-vision', 'computer-vision']	[ 5.49348354e-01 1.29002541e-01 -2.05241874e-01 -1.78427950e-01 -7.95695662e-01 -2.39963889e-01 4.57931608e-01 5.00558436e-01 -1.82018667e-01 2.88736373e-01 3.34430456e-01 -2.42129788e-02 4.57893610e-02 -4.20618951e-01 -5.91362715e-01 -4.95836288e-01 -3.00881807e-02 -5.40374517e-02 9.30626571e-01 -2.72776872e-01 7.51083791e-01 2.66932815e-01 -1.94219255e+00 5.71155429e-01 8.38150561e-01 7.85646260e-01 8.86389077e-01 6.01203561e-01 -1.39800027e-01 6.12154067e-01 -4.64313239e-01 -8.27388540e-02 2.04787210e-01 -4.24200416e-01 -1.20054471e+00 2.14032575e-01 8.39421809e-01 1.33821368e-01 2.09837377e-01 1.39963901e+00 4.03834105e-01 1.18276685e-01 4.11316991e-01 -1.37616003e+00 -5.01508892e-01 8.03182423e-01 -8.45168471e-01 1.12267816e+00 2.47303799e-01 9.31212306e-03 1.48199105e+00 -1.24064589e+00 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e8c311da-544e-4f67-b4f3-6afcc805e459	deepfl-iqa-weak-supervision-for-deep-iqa	2001.08113	null	https://arxiv.org/abs/2001.08113v1	https://arxiv.org/pdf/2001.08113v1.pdf	DeepFL-IQA: Weak Supervision for Deep IQA Feature Learning	Multi-level deep-features have been driving state-of-the-art methods for aesthetics and image quality assessment (IQA). However, most IQA benchmarks are comprised of artificially distorted images, for which features derived from ImageNet under-perform. We propose a new IQA dataset and a weakly supervised feature learning approach to train features more suitable for IQA of artificially distorted images. The dataset, KADIS-700k, is far more extensive than similar works, consisting of 140,000 pristine images, 25 distortions types, totaling 700k distorted versions. Our weakly supervised feature learning is designed as a multi-task learning type training, using eleven existing full-reference IQA metrics as proxies for differential mean opinion scores. We also introduce a benchmark database, KADID-10k, of artificially degraded images, each subjectively annotated by 30 crowd workers. We make use of our derived image feature vectors for (no-reference) image quality assessment by training and testing a shallow regression network on this database and five other benchmark IQA databases. Our method, termed DeepFL-IQA, performs better than other feature-based no-reference IQA methods and also better than all tested full-reference IQA methods on KADID-10k. For the other five benchmark IQA databases, DeepFL-IQA matches the performance of the best existing end-to-end deep learning-based methods on average.	['Vlad Hosu', 'Hanhe Lin', 'Dietmar Saupe']	2020-01-20	null	null	null	null	['no-reference-image-quality-assessment']	['computer-vision']	[ 7.16873258e-02 -1.19905263e-01 3.49319756e-01 -4.58891034e-01 -1.26221573e+00 -4.46217269e-01 6.67693853e-01 -8.59377384e-02 -2.87610352e-01 5.18754125e-01 5.35561800e-01 1.29351348e-01 -3.00562531e-01 -8.78358722e-01 -7.58337438e-01 -6.12882197e-01 -1.10325642e-01 1.93122283e-01 -1.52356312e-01 -4.22910780e-01 3.25326800e-01 3.38526458e-01 -1.71308506e+00 6.55017853e-01 8.25761497e-01 1.50788033e+00 -2.35566989e-01 8.26146364e-01 3.05315286e-01 6.45203888e-01 -1.01179850e+00 -1.12585211e+00 5.28480232e-01 -4.48558927e-01 -1.10642242e+00 1.19110450e-01 1.02561700e+00 -4.47021276e-01 -5.35033762e-01 8.50356579e-01 1.05390310e+00 1.04873881e-01 8.63251269e-01 -1.42387903e+00 -1.39552939e+00 1.22604668e-01 -4.30218607e-01 3.16979408e-01 6.15208268e-01 7.54403651e-01 1.07532990e+00 -1.12417412e+00 4.91435111e-01 1.55450428e+00 7.73576736e-01 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fa875e51-81df-469e-8855-d94c0975ca42	rst-parsing-from-scratch	2105.10861	null	https://arxiv.org/abs/2105.10861v1	https://arxiv.org/pdf/2105.10861v1.pdf	RST Parsing from Scratch	We introduce a novel top-down end-to-end formulation of document-level discourse parsing in the Rhetorical Structure Theory (RST) framework. In this formulation, we consider discourse parsing as a sequence of splitting decisions at token boundaries and use a seq2seq network to model the splitting decisions. Our framework facilitates discourse parsing from scratch without requiring discourse segmentation as a prerequisite; rather, it yields segmentation as part of the parsing process. Our unified parsing model adopts a beam search to decode the best tree structure by searching through a space of high-scoring trees. With extensive experiments on the standard English RST discourse treebank, we demonstrate that our parser outperforms existing methods by a good margin in both end-to-end parsing and parsing with gold segmentation. More importantly, it does so without using any handcrafted features, making it faster and easily adaptable to new languages and domains.	['XiaoLi Li', 'Shafiq Joty', 'Xuan-Phi Nguyen', 'Thanh-Tung Nguyen']	2021-05-23	null	https://aclanthology.org/2021.naacl-main.128	https://aclanthology.org/2021.naacl-main.128.pdf	naacl-2021-4	['discourse-segmentation', 'discourse-parsing']	['natural-language-processing', 'natural-language-processing']	[ 5.33056915e-01 8.79328907e-01 -3.60718876e-01 -4.38573092e-01 -1.20643723e+00 -1.03012669e+00 5.74759364e-01 3.65691125e-01 -4.86746520e-01 7.26484835e-01 7.79504180e-01 -9.46140409e-01 3.07171404e-01 -8.72293293e-01 -4.61710513e-01 -2.62676090e-01 5.34068681e-02 4.78980333e-01 4.69874322e-01 -4.28645462e-01 2.68614739e-01 -9.55490246e-02 -8.42353284e-01 6.99802101e-01 8.25690925e-01 4.70923752e-01 2.66779929e-01 1.06026244e+00 -4.59750116e-01 1.27188027e+00 -8.18435490e-01 -5.48611522e-01 -1.28837302e-01 -7.74489403e-01 -1.59886634e+00 1.27392247e-01 -9.52192023e-02 -2.91881442e-01 2.69277729e-02 9.22162950e-01 1.01596065e-01 5.12581095e-02 2.79663503e-01 -3.58923942e-01 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62e819e4-3600-44cd-b3d3-8f21f16cd68a	ktn-knowledge-transfer-network-for-learning	2206.10090	null	https://arxiv.org/abs/2206.10090v1	https://arxiv.org/pdf/2206.10090v1.pdf	KTN: Knowledge Transfer Network for Learning Multi-person 2D-3D Correspondences	Human densepose estimation, aiming at establishing dense correspondences between 2D pixels of human body and 3D human body template, is a key technique in enabling machines to have an understanding of people in images. It still poses several challenges due to practical scenarios where real-world scenes are complex and only partial annotations are available, leading to incompelete or false estimations. In this work, we present a novel framework to detect the densepose of multiple people in an image. The proposed method, which we refer to Knowledge Transfer Network (KTN), tackles two main problems: 1) how to refine image representation for alleviating incomplete estimations, and 2) how to reduce false estimation caused by the low-quality training labels (i.e., limited annotations and class-imbalance labels). Unlike existing works directly propagating the pyramidal features of regions for densepose estimation, the KTN uses a refinement of pyramidal representation, where it simultaneously maintains feature resolution and suppresses background pixels, and this strategy results in a substantial increase in accuracy. Moreover, the KTN enhances the ability of 3D based body parsing with external knowledges, where it casts 2D based body parsers trained from sufficient annotations as a 3D based body parser through a structural body knowledge graph. In this way, it significantly reduces the adverse effects caused by the low-quality annotations. The effectiveness of KTN is demonstrated by its superior performance to the state-of-the-art methods on DensePose-COCO dataset. Extensive ablation studies and experimental results on representative tasks (e.g., human body segmentation, human part segmentation and keypoints detection) and two popular densepose estimation pipelines (i.e., RCNN and fully-convolutional frameworks), further indicate the generalizability of the proposed method.	['Meng Wang', 'Jingkuan Song', 'Yixuan Zhou', 'Lianli Gao', 'Xuanhan Wang']	2022-06-21	null	null	null	null	['human-part-segmentation']	['computer-vision']	[ 3.53737295e-01 4.69597667e-01 -2.04803631e-01 -1.17799476e-01 -6.55993342e-01 -2.30117336e-01 1.73729345e-01 -2.59415001e-01 -4.90728140e-01 4.98998731e-01 1.08567700e-01 3.44436884e-01 3.03099245e-01 -8.28064144e-01 -9.27763224e-01 -5.43559611e-01 2.93261141e-01 6.05188906e-01 5.75916409e-01 -7.28910491e-02 -3.51310462e-01 1.06504954e-01 -1.56835985e+00 3.67631093e-02 9.27644432e-01 1.08020008e+00 6.19096272e-02 2.51361638e-01 -2.81942170e-02 4.42585647e-01 -4.13565695e-01 -9.92666841e-01 4.05178845e-01 -4.09347713e-01 -5.64165413e-01 2.21665904e-01 7.66279459e-01 -5.68636239e-01 -1.77464589e-01 1.04502666e+00 5.27278125e-01 -2.62972683e-01 5.05318940e-01 -1.10449481e+00 -1.70493379e-01 3.67243469e-01 -9.38368559e-01 -1.87577203e-01 2.74868131e-01 2.38456219e-01 5.48876405e-01 -7.80871987e-01 5.85291803e-01 1.28440917e+00 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167eb032-8a14-4b7b-abbd-49567f4a058e	grid-forming-control-based-on-emulated	2303.00391	null	https://arxiv.org/abs/2303.00391v1	https://arxiv.org/pdf/2303.00391v1.pdf	Grid-Forming Control Based On Emulated Synchronous Condenser Strategy Compliant With Challenging Grid Code Requirements	Future power systems will include high shares of inverter-based generation. There is a general consensus that for allowing this transition, the Grid-Forming (GFo) control approach would be of great value. This article presents a GFo control strategy which is based on the concept of an Emulated Synchronous Condenser in parallel with a controlled current source with an explicit representation of the swing equation. The advantage of this control is that it can cope with challenging grid code requirements such as severe phase jumps, balanced and unbalanced Fault Ride-Through (FRT), main grid disconnection and black start. All these scenarios can be surpassed with a single control structure with no further logic involved (e.g. fault detection to turn on or off different control parts, freezes, etc.). The proposed strategy is evaluated via time-domain simulations of a 2-MW Battery Energy Storage System (BESS).	['Grégoire Prime', 'Valentin Costan', 'Antoine Rossé', 'Julian Freytes']	2023-03-01	null	null	null	null	['fault-detection']	['miscellaneous']	[-2.09744141e-01 1.32752340e-02 1.32902056e-01 1.42858744e-01 2.15980485e-01 -1.08507681e+00 8.93873692e-01 4.47366059e-01 2.81641543e-01 1.39545453e+00 -5.21369040e-01 -4.30718571e-01 -4.69431847e-01 -7.14485109e-01 -2.89366990e-01 -9.90380883e-01 -2.06064299e-01 5.14838159e-01 2.49275267e-01 -5.19486129e-01 2.36094519e-01 8.34556401e-01 -1.47128308e+00 -5.21334350e-01 1.24113643e+00 9.87083256e-01 1.78706363e-01 2.02654719e-01 3.86894107e-01 5.09923279e-01 -1.07682347e+00 4.34827030e-01 -1.33948624e-02 -6.25962377e-01 -4.96941477e-01 1.74881801e-01 -6.44972324e-01 -2.99723059e-01 1.38862148e-01 1.00519991e+00 5.93712211e-01 1.37289360e-01 7.90122390e-01 -1.79588771e+00 2.38613561e-01 3.41166347e-01 -2.28363141e-01 4.01251644e-01 4.64608282e-01 2.89544493e-01 5.32339215e-01 -5.37233472e-01 3.69081020e-01 5.26400983e-01 5.77057898e-02 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531d2c3b-dca4-40e0-b097-f36d9d8a5c08	towards-performance-improvement-in-indian	null	null	https://aclanthology.org/2020.icon-main.47	https://aclanthology.org/2020.icon-main.47.pdf	Towards Performance Improvement in Indian Sign Language Recognition	Sign language is a complete natural language used by deaf and dumb people. It has its own grammar and it differs with spoken language to a great extent. Since people without hearing and speech impairment lack the knowledge of the sign language, the deaf and dumb people find it difficult to communicate with them. The conception of system that would be able to translate the sign language into text would facilitate understanding of sign language without human interpreter. This paper describes a systematic approach that takes Indian Sign Language (ISL) video as input and converts it into text using frame sequence generator and image augmentation techniques. By incorporating these two concepts, we have increased dataset size and reduced overfitting. It is demonstrated that using simple image manipulation techniques and batch of shifted frames of videos, performance of sign language recognition can be significantly improved. Approach described in this paper achieves 99.57% accuracy on the dynamic gesture dataset of ISL.	['Brijesh Bhatt', 'Devendra Thakor', 'Kinjal Mistree']	null	null	null	null	icon-2020-12	['sign-language-recognition', 'image-augmentation']	['computer-vision', 'computer-vision']	[ 1.84605360e-01 -5.02007902e-02 -1.04832323e-02 -4.55341518e-01 -4.05170411e-01 -7.83995926e-01 6.50971413e-01 -9.02010500e-01 -6.61488950e-01 7.99866915e-01 5.53938568e-01 -4.73668844e-01 1.40443027e-01 -4.87440199e-01 -1.90739185e-01 -6.03158414e-01 1.61782816e-01 2.75797457e-01 3.88194352e-01 -3.35023582e-01 4.42764461e-01 4.99709487e-01 -1.58993018e+00 2.48191535e-01 3.68974894e-01 2.02835828e-01 3.50825667e-01 1.19413400e+00 -4.78190780e-01 1.08275414e+00 -5.06033838e-01 1.66960165e-01 5.75825930e-01 -7.52816617e-01 -8.86684656e-01 1.98652729e-01 5.18979013e-01 -8.75079095e-01 -6.22713506e-01 6.93465590e-01 8.41978788e-01 -4.54106368e-02 6.97913527e-01 -1.33701611e+00 -6.01278782e-01 3.92905861e-01 -1.83248654e-01 -4.31975946e-02 7.37717330e-01 4.17811036e-01 3.65395695e-01 -5.65920830e-01 9.65412557e-01 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aa0317eb-d271-41cb-bb72-fb8e2fe437e8	graphonomy-universal-human-parsing-via-graph	1904.04536	null	http://arxiv.org/abs/1904.04536v1	http://arxiv.org/pdf/1904.04536v1.pdf	Graphonomy: Universal Human Parsing via Graph Transfer Learning	Prior highly-tuned human parsing models tend to fit towards each dataset in a specific domain or with discrepant label granularity, and can hardly be adapted to other human parsing tasks without extensive re-training. In this paper, we aim to learn a single universal human parsing model that can tackle all kinds of human parsing needs by unifying label annotations from different domains or at various levels of granularity. This poses many fundamental learning challenges, e.g. discovering underlying semantic structures among different label granularity, performing proper transfer learning across different image domains, and identifying and utilizing label redundancies across related tasks. To address these challenges, we propose a new universal human parsing agent, named "Graphonomy", which incorporates hierarchical graph transfer learning upon the conventional parsing network to encode the underlying label semantic structures and propagate relevant semantic information. In particular, Graphonomy first learns and propagates compact high-level graph representation among the labels within one dataset via Intra-Graph Reasoning, and then transfers semantic information across multiple datasets via Inter-Graph Transfer. Various graph transfer dependencies (\eg, similarity, linguistic knowledge) between different datasets are analyzed and encoded to enhance graph transfer capability. By distilling universal semantic graph representation to each specific task, Graphonomy is able to predict all levels of parsing labels in one system without piling up the complexity. Experimental results show Graphonomy effectively achieves the state-of-the-art results on three human parsing benchmarks as well as advantageous universal human parsing performance.	['Liang Lin', 'Ke Gong', 'Yiming Gao', 'Xiaohui Shen', 'Xiaodan Liang', 'Meng Wang']	2019-04-09	graphonomy-universal-human-parsing-via-graph-1	http://openaccess.thecvf.com/content_CVPR_2019/html/Gong_Graphonomy_Universal_Human_Parsing_via_Graph_Transfer_Learning_CVPR_2019_paper.html	http://openaccess.thecvf.com/content_CVPR_2019/papers/Gong_Graphonomy_Universal_Human_Parsing_via_Graph_Transfer_Learning_CVPR_2019_paper.pdf	cvpr-2019-6	['human-parsing']	['computer-vision']	[ 4.97034520e-01 5.86620450e-01 -3.19748759e-01 -6.48100138e-01 -8.59275818e-01 -7.36314893e-01 1.77070439e-01 3.38293254e-01 -2.31891900e-01 5.42219996e-01 9.86013934e-02 -1.81297094e-01 8.36361349e-02 -1.01314950e+00 -5.81624448e-01 -4.16975230e-01 2.92466491e-01 6.96181357e-01 5.96806526e-01 -2.53067687e-02 -1.31894529e-01 -4.82206754e-02 -1.06367040e+00 5.73511779e-01 8.33629489e-01 9.33537066e-01 4.94129807e-01 4.59031433e-01 -6.68833256e-01 1.10159993e+00 -4.61517245e-01 -7.32291579e-01 -6.26773238e-02 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d10ddf55-d4aa-4a84-8d59-ee0650e9e1db	detecting-dominant-vanishing-points-in	1608.04267	null	http://arxiv.org/abs/1608.04267v2	http://arxiv.org/pdf/1608.04267v2.pdf	Detecting Dominant Vanishing Points in Natural Scenes with Application to Composition-Sensitive Image Retrieval	Linear perspective is widely used in landscape photography to create the impression of depth on a 2D photo. Automated understanding of linear perspective in landscape photography has several real-world applications, including aesthetics assessment, image retrieval, and on-site feedback for photo composition, yet adequate automated understanding has been elusive. We address this problem by detecting the dominant vanishing point and the associated line structures in a photo. However, natural landscape scenes pose great technical challenges because often the inadequate number of strong edges converging to the dominant vanishing point is inadequate. To overcome this difficulty, we propose a novel vanishing point detection method that exploits global structures in the scene via contour detection. We show that our method significantly outperforms state-of-the-art methods on a public ground truth landscape image dataset that we have created. Based on the detection results, we further demonstrate how our approach to linear perspective understanding provides on-site guidance to amateur photographers on their work through a novel viewpoint-specific image retrieval system.	['Zihan Zhou', 'James Z. Wang', 'Farshid Farhat']	2016-08-15	null	null	null	null	['contour-detection']	['computer-vision']	[ 6.57639086e-01 -1.02847211e-01 -4.49034907e-02 -3.57632905e-01 -7.07095146e-01 -8.52863193e-01 3.33927274e-01 1.64979547e-01 1.09305000e-02 1.21998727e-01 3.00889015e-01 -2.15946138e-01 -2.11045276e-02 -7.37389684e-01 -5.04608452e-01 -2.65082717e-01 3.30482751e-01 -8.11413154e-02 2.90310830e-01 -3.88010353e-01 8.32087755e-01 7.32984245e-01 -1.42974401e+00 3.34309250e-01 5.97696722e-01 7.58111596e-01 8.20629224e-02 5.64828277e-01 4.29511443e-02 2.92464823e-01 -3.43701899e-01 -6.07617557e-01 6.78219259e-01 -3.34628195e-01 -3.80182207e-01 5.03171861e-01 1.18231297e+00 -7.43989110e-01 -4.63117409e-04 1.08855164e+00 4.35033321e-01 -9.92904976e-02 4.88675475e-01 -9.37009275e-01 -5.77601552e-01 -1.25716701e-01 -1.16409481e+00 5.64809255e-02 7.19983757e-01 2.31446415e-01 1.11865544e+00 -8.81561756e-01 9.12894487e-01 1.03079820e+00 8.83415699e-01 9.98476967e-02 -1.23453808e+00 -3.52206707e-01 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0396fe92-5e0d-47a6-ab12-f6958c26955a	memories-are-one-to-many-mapping-alleviators	2212.05005	null	https://arxiv.org/abs/2212.05005v2	https://arxiv.org/pdf/2212.05005v2.pdf	Memories are One-to-Many Mapping Alleviators in Talking Face Generation	Talking face generation aims at generating photo-realistic video portraits of a target person driven by input audio. Due to its nature of one-to-many mapping from the input audio to the output video (e.g., one speech content may have multiple feasible visual appearances), learning a deterministic mapping like previous works brings ambiguity during training, and thus causes inferior visual results. Although this one-to-many mapping could be alleviated in part by a two-stage framework (i.e., an audio-to-expression model followed by a neural-rendering model), it is still insufficient since the prediction is produced without enough information (e.g., emotions, wrinkles, etc.). In this paper, we propose MemFace to complement the missing information with an implicit memory and an explicit memory that follow the sense of the two stages respectively. More specifically, the implicit memory is employed in the audio-to-expression model to capture high-level semantics in the audio-expression shared space, while the explicit memory is employed in the neural-rendering model to help synthesize pixel-level details. Our experimental results show that our proposed MemFace surpasses all the state-of-the-art results across multiple scenarios consistently and significantly.	['Jiang Bian', 'Li Song', 'Sheng Zhao', 'Runnan Li', 'Jun Ling', 'Xu Tan', 'Tianyu He', 'Anni Tang']	2022-12-09	null	null	null	null	['talking-face-generation', 'face-generation']	['computer-vision', 'computer-vision']	[ 4.95859236e-01 2.57862151e-01 7.85804316e-02 -4.31395501e-01 -6.76547050e-01 -1.37529120e-01 5.82920730e-01 -4.33812410e-01 1.35774568e-01 6.95492029e-01 2.45296687e-01 9.22202170e-02 3.12799007e-01 -8.47242713e-01 -8.76717210e-01 -8.18088830e-01 3.76604348e-01 6.73105121e-02 4.12474312e-02 -2.51975507e-01 -8.33691880e-02 1.11437909e-01 -1.98662019e+00 8.96125257e-01 7.06021607e-01 1.18176830e+00 3.54705364e-01 5.05899310e-01 -4.20221001e-01 9.91961181e-01 -6.82151318e-01 -4.97985721e-01 -2.05454938e-02 -6.40594006e-01 -4.02769059e-01 3.63574624e-01 5.45323670e-01 -5.05373478e-01 -1.61629558e-01 9.95225430e-01 4.72028047e-01 4.04219665e-02 6.01170778e-01 -1.40311742e+00 -7.28321552e-01 2.46874467e-01 -7.39564061e-01 -3.74057710e-01 5.73384941e-01 2.66488731e-01 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835066bc-4ff7-45a4-9f9d-8d0b43cebf7d	a-structural-transformer-with-relative	null	null	https://openreview.net/forum?id=RxjDi-W69iW	https://openreview.net/pdf?id=RxjDi-W69iW	A Structural Transformer with Relative Positions in Trees for Code-to-Sequence Tasks	We suggest two approaches to incorporate syntactic information into transformer models encoding trees (e.g. abstract syntax trees) and generating sequences. First, we use self-attention with relative position representations to consider structural relationships between nodes using a representation that encodes movements between any pair of nodes in the tree, and demonstrate how those movements can be computed efficiently on the fly. Second, we suggest an auxiliary loss enforcing the network to predict the lowest common ancestor of node pairs. We apply both methods to source code summarization tasks, where we outperform the state-of-the-art by up to 6% F1. On natural language machine translation, our models yield competitive results, while substantially faster than other. We also consistently outperform sequence-based transformers, and demonstrate that our method yields representations that are more closely aligned with the tree's structure.	['Anonymous']	2020-06-04	null	null	null	null	['code-summarization']	['computer-code']	[ 7.29004681e-01 6.83037817e-01 -3.64584744e-01 -2.63046950e-01 -1.25739336e+00 -8.01930666e-01 5.43557703e-01 4.78517413e-01 1.58238530e-01 6.65108383e-01 8.50195110e-01 -8.10431242e-01 3.21583688e-01 -9.64687586e-01 -1.14943969e+00 -1.48520857e-01 -5.99422455e-02 3.90040398e-01 2.11422458e-01 -4.18343127e-01 3.78642499e-01 1.87722325e-01 -1.29071975e+00 6.81772232e-01 9.94516790e-01 4.70847547e-01 4.83957455e-02 6.95464015e-01 -4.78800327e-01 8.98536563e-01 -5.13028622e-01 -7.65795350e-01 3.94667313e-02 -7.21041262e-01 -1.30742121e+00 -1.25665411e-01 6.20359778e-01 -1.51796088e-01 -2.68895626e-01 8.48394036e-01 1.01515412e-01 -2.26163119e-01 5.96513927e-01 -9.67798948e-01 -9.59789574e-01 1.28672087e+00 -7.27393806e-01 3.32523197e-01 6.23090446e-01 1.22075841e-01 1.58934307e+00 -5.77300191e-01 8.68026078e-01 1.34353924e+00 7.79448569e-01 4.72098053e-01 -1.69939017e+00 -2.76948363e-01 3.51055980e-01 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b6cfefd2-6f79-4c26-83d2-3f0f5057c3f2	gnn-xml-graph-neural-networks-for-extreme	2012.05860	null	https://arxiv.org/abs/2012.05860v1	https://arxiv.org/pdf/2012.05860v1.pdf	GNN-XML: Graph Neural Networks for Extreme Multi-label Text Classification	Extreme multi-label text classification (XMTC) aims to tag a text instance with the most relevant subset of labels from an extremely large label set. XMTC has attracted much recent attention due to massive label sets yielded by modern applications, such as news annotation and product recommendation. The main challenges of XMTC are the data scalability and sparsity, thereby leading to two issues: i) the intractability to scale to the extreme label setting, ii) the presence of long-tailed label distribution, implying that a large fraction of labels have few positive training instances. To overcome these problems, we propose GNN-XML, a scalable graph neural network framework tailored for XMTC problems. Specifically, we exploit label correlations via mining their co-occurrence patterns and build a label graph based on the correlation matrix. We then conduct the attributed graph clustering by performing graph convolution with a low-pass graph filter to jointly model label dependencies and label features, which induces semantic label clusters. We further propose a bilateral-branch graph isomorphism network to decouple representation learning and classifier learning for better modeling tail labels. Experimental results on multiple benchmark datasets show that GNN-XML significantly outperforms state-of-the-art methods while maintaining comparable prediction efficiency and model size.	['Shiliang Sun', 'Daoming Zong']	2020-12-10	null	null	null	null	['product-recommendation', 'news-annotation']	['miscellaneous', 'natural-language-processing']	[ 3.79360616e-01 4.96382937e-02 -5.18677890e-01 -5.15577316e-01 -3.23566824e-01 -5.20349681e-01 2.77082294e-01 4.93252337e-01 -9.78514850e-02 1.59976944e-01 -6.78898320e-02 -2.60792881e-01 -3.50437999e-01 -7.49901652e-01 -2.66919553e-01 -8.29026103e-01 -9.48527902e-02 7.85737693e-01 -5.01550138e-02 9.99710113e-02 -7.00753033e-02 -8.16034339e-03 -1.49012613e+00 4.02385473e-01 7.28257716e-01 1.27914929e+00 5.04610278e-02 1.12739414e-01 -4.41738307e-01 1.26546836e+00 1.00414746e-01 -4.56528693e-01 1.61083326e-01 -3.08412254e-01 -8.08417976e-01 3.07430804e-01 5.41846812e-01 2.49491572e-01 -1.13509528e-01 1.33029580e+00 3.18468422e-01 1.56020150e-01 7.95497954e-01 -1.59247458e+00 -6.82347536e-01 1.06327546e+00 -1.31857729e+00 -3.07783484e-01 1.82113931e-01 -3.51164132e-01 1.66875792e+00 -6.34141505e-01 5.44350863e-01 1.21644866e+00 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a8289d18-feb1-471c-91a8-346b98e3c16f	sv000gg-at-semeval-2016-task-11-heavy-gauge	null	null	https://aclanthology.org/S16-1149	https://aclanthology.org/S16-1149.pdf	SV000gg at SemEval-2016 Task 11: Heavy Gauge Complex Word Identification with System Voting	null	['Lucia Specia', 'Gustavo Paetzold']	2016-06-01	null	null	null	semeval-2016-6	['complex-word-identification']	['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 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ab4e367f-b702-4f47-acdc-11c00e8df8f2	anomaly-detection-with-domain-adaptation	2006.03689	null	https://arxiv.org/abs/2006.03689v1	https://arxiv.org/pdf/2006.03689v1.pdf	Anomaly Detection with Domain Adaptation	We study the problem of semi-supervised anomaly detection with domain adaptation. Given a set of normal data from a source domain and a limited amount of normal examples from a target domain, the goal is to have a well-performing anomaly detector in the target domain. We propose the Invariant Representation Anomaly Detection (IRAD) to solve this problem where we first learn to extract a domain-invariant representation. The extraction is achieved by an across-domain encoder trained together with source-specific encoders and generators by adversarial learning. An anomaly detector is then trained using the learnt representations. We evaluate IRAD extensively on digits images datasets (MNIST, USPS and SVHN) and object recognition datasets (Office-Home). Experimental results show that IRAD outperforms baseline models by a wide margin across different datasets. We derive a theoretical lower bound for the joint error that explains the performance decay from overtraining and also an upper bound for the generalization error.	['Eric Darve', 'Ziyi Yang', 'Iman Soltani Bozchalooi']	2020-06-05	null	null	null	null	['supervised-anomaly-detection', 'semi-supervised-anomaly-detection']	['computer-vision', 'computer-vision']	[ 4.04532313e-01 1.81409001e-01 3.30944583e-02 -5.84008157e-01 -8.44469130e-01 -5.35463154e-01 7.55092740e-01 2.87039541e-02 -3.33780438e-01 4.85263675e-01 -1.04594454e-01 -1.21870413e-01 2.03041136e-01 -5.63386858e-01 -9.54129219e-01 -5.18798172e-01 -3.71687114e-01 6.49363995e-01 1.66121587e-01 -1.88522145e-01 1.15836896e-01 5.18688202e-01 -1.21771193e+00 1.67992786e-01 1.01314723e+00 1.36948395e+00 -3.71894717e-01 5.31095803e-01 -1.14666499e-01 8.44492555e-01 -7.77006626e-01 -2.05353767e-01 7.55215287e-01 -6.58678114e-01 -6.73400939e-01 3.27198774e-01 4.44487661e-01 -4.42628622e-01 -4.73349154e-01 1.35876906e+00 1.79448575e-01 2.09650025e-01 1.32163799e+00 -1.54831207e+00 -9.44206178e-01 1.82548866e-01 -5.25875211e-01 5.87596416e-01 2.35434338e-01 1.89224482e-01 8.76480877e-01 -9.01328206e-01 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90c2bfb5-2aa9-4e85-ba45-e91fb2247f40	know-your-boundaries-the-necessity-of	2206.00695	null	https://arxiv.org/abs/2206.00695v1	https://arxiv.org/pdf/2206.00695v1.pdf	Know Your Boundaries: The Necessity of Explicit Behavioral Cloning in Offline RL	We introduce an offline reinforcement learning (RL) algorithm that explicitly clones a behavior policy to constrain value learning. In offline RL, it is often important to prevent a policy from selecting unobserved actions, since the consequence of these actions cannot be presumed without additional information about the environment. One straightforward way to implement such a constraint is to explicitly model a given data distribution via behavior cloning and directly force a policy not to select uncertain actions. However, many offline RL methods instantiate the constraint indirectly -- for example, pessimistic value estimation -- due to a concern about errors when modeling a potentially complex behavior policy. In this work, we argue that it is not only viable but beneficial to explicitly model the behavior policy for offline RL because the constraint can be realized in a stable way with the trained model. We first suggest a theoretical framework that allows us to incorporate behavior-cloned models into value-based offline RL methods, enjoying the strength of both explicit behavior cloning and value learning. Then, we propose a practical method utilizing a score-based generative model for behavior cloning. With the proposed method, we show state-of-the-art performance on several datasets within the D4RL and Robomimic benchmarks and achieve competitive performance across all datasets tested.	['Scott Niekum', 'Wonjoon Goo']	2022-06-01	null	null	null	null	['d4rl']	['robots']	[ 1.22983903e-02 3.92632842e-01 -7.58276463e-01 -3.92732471e-01 -7.22264528e-01 -6.66628182e-01 5.30359328e-01 5.58139235e-02 -6.06945038e-01 1.03017998e+00 4.66015749e-02 -3.72083932e-01 -1.10527888e-01 -8.88617277e-01 -9.87334847e-01 -7.42928863e-01 -5.48961246e-03 6.04911327e-01 1.27872482e-01 -1.11098364e-01 1.39490306e-01 5.13386965e-01 -1.55963778e+00 4.00048420e-02 6.58188701e-01 9.74292099e-01 2.31025174e-01 6.52247906e-01 1.45843681e-02 1.24003100e+00 -6.58614159e-01 -2.20310524e-01 2.55766183e-01 -6.76668942e-01 -6.62034273e-01 1.64088652e-01 -3.62289637e-01 -9.13350523e-01 -1.36926755e-01 8.86714041e-01 1.86966211e-01 2.37119049e-01 4.68271524e-01 -1.34593439e+00 -2.17029452e-01 9.58419740e-01 -1.81041598e-01 -3.34967464e-01 2.75854617e-01 4.38112617e-01 1.06873894e+00 1.05318548e-02 6.19590580e-01 1.17085779e+00 1.35459956e-02 7.09978878e-01 -1.36629665e+00 -4.31484520e-01 7.15916634e-01 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8da6564c-ed3f-4c27-aa86-d35d51f0ea64	perturbation-based-qe-an-explainable	2305.07457	null	https://arxiv.org/abs/2305.07457v1	https://arxiv.org/pdf/2305.07457v1.pdf	Perturbation-based QE: An Explainable, Unsupervised Word-level Quality Estimation Method for Blackbox Machine Translation	Quality Estimation (QE) is the task of predicting the quality of Machine Translation (MT) system output, without using any gold-standard translation references. State-of-the-art QE models are supervised: they require human-labeled quality of some MT system output on some datasets for training, making them domain-dependent and MT-system-dependent. There has been research on unsupervised QE, which requires glass-box access to the MT systems, or parallel MT data to generate synthetic errors for training QE models. In this paper, we present Perturbation-based QE - a word-level Quality Estimation approach that works simply by analyzing MT system output on perturbed input source sentences. Our approach is unsupervised, explainable, and can evaluate any type of blackbox MT systems, including the currently prominent large language models (LLMs) with opaque internal processes. For language directions with no labeled QE data, our approach has similar or better performance than the zero-shot supervised approach on the WMT21 shared task. Our approach is better at detecting gender bias and word-sense-disambiguation errors in translation than supervised QE, indicating its robustness to out-of-domain usage. The performance gap is larger when detecting errors on a nontraditional translation-prompting LLM, indicating that our approach is more generalizable to different MT systems. We give examples demonstrating our approach's explainability power, where it shows which input source words have influence on a certain MT output word.	['Jan Niehues', 'Tu Anh Dinh']	2023-05-12	null	null	null	null	['word-sense-disambiguation']	['natural-language-processing']	[ 1.70816362e-01 3.53408664e-01 -3.68601114e-01 -2.51047224e-01 -1.37001681e+00 -7.60533154e-01 8.76071215e-01 1.21104181e-01 -3.99629414e-01 9.29619074e-01 2.86971241e-01 -9.38621223e-01 1.15145750e-01 -3.89846504e-01 -9.66080487e-01 -2.61951953e-01 5.40423572e-01 9.64186728e-01 -1.84779689e-01 -7.06345618e-01 5.18329501e-01 -8.87134895e-02 -1.07048905e+00 3.02800953e-01 1.44597614e+00 3.22860271e-01 2.43291989e-01 9.28755760e-01 -1.45006180e-01 5.96137285e-01 -1.11261475e+00 -1.03994167e+00 1.76141392e-02 -6.72803164e-01 -1.07376611e+00 -1.80191472e-01 3.00051719e-01 1.85243964e-01 6.00057542e-02 1.01184618e+00 8.44223738e-01 -5.04554570e-01 8.36787105e-01 -1.31086540e+00 -9.34410214e-01 9.26607490e-01 -7.68808350e-02 3.87294024e-01 4.36370432e-01 5.06145775e-01 1.19816661e+00 -1.18820071e+00 9.56836224e-01 1.45796466e+00 4.76062715e-01 6.09506547e-01 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