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Tracking by natural language specification is a new rising research topic
that aims at locating the target object in the video sequence based on its
language description. Compared with traditional bounding box (BBox) based
tracking, this setting guides object tracking with high-level semantic
information, addresses the ambiguity of BBox, and links local and global search
organically together. Those benefits may bring more flexible, robust and
accurate tracking performance in practical scenarios. However, existing natural
language initialized trackers are developed and compared on benchmark datasets
proposed for tracking-by-BBox, which can't reflect the true power of
tracking-by-language. In this work, we propose a new benchmark specifically
dedicated to the tracking-by-language, including a large scale dataset, strong
and diverse baseline methods. Specifically, we collect 2k video sequences
(contains a total of 1,244,340 frames, 663 words) and split 1300/700 for the
train/testing respectively. We densely annotate one sentence in English and
corresponding bounding boxes of the target object for each video. We also
introduce two new challenges into TNL2K for the object tracking task, i.e.,
adversarial samples and modality switch. A strong baseline method based on an
adaptive local-global-search scheme is proposed for future works to compare. We
believe this benchmark will greatly boost related researches on natural
language guided tracking. | [
"cs.CV",
"cs.AI"
] |
Attention based Transformer architecture has enabled significant advances in
the field of natural language processing. In addition to new pre-training
techniques, recent improvements crucially rely on working with a relatively
larger embedding dimension for tokens. Unfortunately, this leads to models that
are prohibitively large to be employed in the downstream tasks. In this paper
we identify one of the important factors contributing to the large embedding
size requirement. In particular, our analysis highlights that the scaling
between the number of heads and the size of each head in the current
architecture gives rise to a low-rank bottleneck in attention heads, causing
this limitation. We further validate this in our experiments. As a solution we
propose to set the head size of an attention unit to input sequence length, and
independent of the number of heads, resulting in multi-head attention layers
with provably more expressive power. We empirically show that this allows us to
train models with a relatively smaller embedding dimension and with better
performance scaling. | [
"cs.LG",
"stat.ML"
] |
Deep learning based models, generally, require a large number of samples for
appropriate training, a requirement that is difficult to satisfy in the medical
field. This issue can usually be avoided with a proper initialization of the
weights. On the task of medical image segmentation in general, two techniques
are oftentimes employed to tackle the training of a deep network $f_T$. The
first one consists in reusing some weights of a network $f_S$ pre-trained on a
large scale database ($e.g.$ ImageNet). This procedure, also known as
$transfer$ $learning$, happens to reduce the flexibility when it comes to new
network design since $f_T$ is constrained to match some parts of $f_S$. The
second commonly used technique consists in working on image patches to benefit
from the large number of available patches. This paper brings together these
two techniques and propose to train $arbitrarily$ $designed$ $networks$ that
segment an image in one forward pass, with a focus on relatively small
databases. An experimental work have been carried out on the tasks of retinal
blood vessel segmentation and the optic disc one, using four publicly available
databases. Furthermore, three types of network are considered, going from a
very light weighted network to a densely connected one. The final results show
the efficiency of the proposed framework along with state of the art results on
all the databases. | [
"cs.CV",
"cs.LG"
] |
Squamous Cell Carcinoma (SCC) is the most common cancer type of the
epithelium and is often detected at a late stage. Besides invasive diagnosis of
SCC by means of biopsy and histo-pathologic assessment, Confocal Laser
Endomicroscopy (CLE) has emerged as noninvasive method that was successfully
used to diagnose SCC in vivo. For interpretation of CLE images, however,
extensive training is required, which limits its applicability and use in
clinical practice of the method. To aid diagnosis of SCC in a broader scope,
automatic detection methods have been proposed. This work compares two methods
with regard to their applicability in a transfer learning sense, i.e. training
on one tissue type (from one clinical team) and applying the learnt
classification system to another entity (different anatomy, different clinical
team). Besides a previously proposed, patch-based method based on convolutional
neural networks, a novel classification method on image level (based on a
pre-trained Inception V.3 network with dedicated preprocessing and
interpretation of class activation maps) is proposed and evaluated. The newly
presented approach improves recognition performance, yielding accuracies of
91.63% on the first data set (oral cavity) and 92.63% on a joint data set. The
generalization from oral cavity to the second data set (vocal folds) lead to
similar area-under-the-ROC curve values than a direct training on the vocal
folds data set, indicating good generalization. | [
"cs.CV"
] |
While Physics-Based Simulation (PBS) can accurately drape a 3D garment on a
3D body, it remains too costly for real-time applications, such as virtual
try-on. By contrast, inference in a deep network, requiring a single forward
pass, is much faster. Taking advantage of this, we propose a novel architecture
to fit a 3D garment template to a 3D body. Specifically, we build upon the
recent progress in 3D point cloud processing with deep networks to extract
garment features at varying levels of detail, including point-wise, patch-wise
and global features. We fuse these features with those extracted in parallel
from the 3D body, so as to model the cloth-body interactions. The resulting
two-stream architecture, which we call as GarNet, is trained using a loss
function inspired by physics-based modeling, and delivers visually plausible
garment shapes whose 3D points are, on average, less than 1 cm away from those
of a PBS method, while running 100 times faster. Moreover, the proposed method
can model various garment types with different cutting patterns when parameters
of those patterns are given as input to the network. | [
"cs.CV"
] |
It is a long-standing question to discover causal relations among a set of
variables in many empirical sciences. Recently, Reinforcement Learning (RL) has
achieved promising results in causal discovery from observational data.
However, searching the space of directed graphs and enforcing acyclicity by
implicit penalties tend to be inefficient and restrict the existing RL-based
method to small scale problems. In this work, we propose a novel RL-based
approach for causal discovery, by incorporating RL into the ordering-based
paradigm. Specifically, we formulate the ordering search problem as a
multi-step Markov decision process, implement the ordering generating process
with an encoder-decoder architecture, and finally use RL to optimize the
proposed model based on the reward mechanisms designed for~each ordering. A
generated ordering would then be processed using variable selection to obtain
the final causal graph. We analyze the consistency and computational complexity
of the proposed method, and empirically show that a pretrained model can be
exploited to accelerate training. Experimental results on both synthetic and
real data sets shows that the proposed method achieves a much improved
performance over existing RL-based method. | [
"cs.LG"
] |
Attention-based neural networks have achieved state-of-the-art results on a
wide range of tasks. Most such models use deterministic attention while
stochastic attention is less explored due to the optimization difficulties or
complicated model design. This paper introduces Bayesian attention belief
networks, which construct a decoder network by modeling unnormalized attention
weights with a hierarchy of gamma distributions, and an encoder network by
stacking Weibull distributions with a deterministic-upward-stochastic-downward
structure to approximate the posterior. The resulting auto-encoding networks
can be optimized in a differentiable way with a variational lower bound. It is
simple to convert any models with deterministic attention, including pretrained
ones, to the proposed Bayesian attention belief networks. On a variety of
language understanding tasks, we show that our method outperforms deterministic
attention and state-of-the-art stochastic attention in accuracy, uncertainty
estimation, generalization across domains, and robustness to adversarial
attacks. We further demonstrate the general applicability of our method on
neural machine translation and visual question answering, showing great
potential of incorporating our method into various attention-related tasks. | [
"cs.LG",
"cs.CL",
"stat.ML"
] |
Self-supervised learning provides an opportunity to explore unlabeled chest
X-rays and their associated free-text reports accumulated in clinical routine
without manual supervision. This paper proposes a Joint Image Text
Representation Learning Network (JoImTeRNet) for pre-training on chest X-ray
images and their radiology reports. The model was pre-trained on both the
global image-sentence level and the local image region-word level for
visual-textual matching. Both are bidirectionally constrained on Cross-Entropy
based and ranking-based Triplet Matching Losses. The region-word matching is
calculated using the attention mechanism without direct supervision about their
mapping. The pre-trained multi-modal representation learning paves the way for
downstream tasks concerning image and/or text encoding. We demonstrate the
representation learning quality by cross-modality retrievals and multi-label
classifications on two datasets: OpenI-IU and MIMIC-CXR | [
"cs.LG",
"cs.AI",
"cs.CL",
"cs.CV",
"eess.IV"
] |
We present a new learning-based method for multi-frame depth estimation from
a color video, which is a fundamental problem in scene understanding, robot
navigation or handheld 3D reconstruction. While recent learning-based methods
estimate depth at high accuracy, 3D point clouds exported from their depth maps
often fail to preserve important geometric feature (e.g., corners, edges,
planes) of man-made scenes. Widely-used pixel-wise depth errors do not
specifically penalize inconsistency on these features. These inaccuracies are
particularly severe when subsequent depth reconstructions are accumulated in an
attempt to scan a full environment with man-made objects with this kind of
features. Our depth estimation algorithm therefore introduces a Combined Normal
Map (CNM) constraint, which is designed to better preserve high-curvature
features and global planar regions. In order to further improve the depth
estimation accuracy, we introduce a new occlusion-aware strategy that
aggregates initial depth predictions from multiple adjacent views into one
final depth map and one occlusion probability map for the current reference
view. Our method outperforms the state-of-the-art in terms of depth estimation
accuracy, and preserves essential geometric features of man-made indoor scenes
much better than other algorithms. | [
"cs.CV"
] |
In this paper, we present a neat yet effective transformer-based framework
for visual grounding, namely TransVG, to address the task of grounding a
language query to the corresponding region onto an image. The state-of-the-art
methods, including two-stage or one-stage ones, rely on a complex module with
manually-designed mechanisms to perform the query reasoning and multi-modal
fusion. However, the involvement of certain mechanisms in fusion module design,
such as query decomposition and image scene graph, makes the models easily
overfit to datasets with specific scenarios, and limits the plenitudinous
interaction between the visual-linguistic context. To avoid this caveat, we
propose to establish the multi-modal correspondence by leveraging transformers,
and empirically show that the complex fusion modules (\eg, modular attention
network, dynamic graph, and multi-modal tree) can be replaced by a simple stack
of transformer encoder layers with higher performance. Moreover, we
re-formulate the visual grounding as a direct coordinates regression problem
and avoid making predictions out of a set of candidates (\emph{i.e.}, region
proposals or anchor boxes). Extensive experiments are conducted on five widely
used datasets, and a series of state-of-the-art records are set by our TransVG.
We build the benchmark of transformer-based visual grounding framework and make
the code available at \url{https://github.com/djiajunustc/TransVG}. | [
"cs.CV"
] |
Detection and segmentation of Brain tumor is very important because it
provides anatomical information of normal and abnormal tissues which helps in
treatment planning and patient follow-up. There are number of techniques for
image segmentation. Proposed research work uses ANFIS (Artificial Neural
Network Fuzzy Inference System) for image classification and then compares the
results with FCM (Fuzzy C means) and K-NN (K-nearest neighbor). ANFIS includes
benefits of both ANN and the fuzzy logic systems. A comprehensive feature set
and fuzzy rules are selected to classify an abnormal image to the corresponding
tumor type. Experimental results illustrate promising results in terms of
classification accuracy. A comparative analysis is performed with the FCM and
K-NN to show the superior nature of ANFIS systems. | [
"cs.CV",
"cs.AI"
] |
The robot market has been growing significantly and is expected to become 1.5
times larger in 2024 than what it was in 2019. Robots have attracted attention
of security companies thanks to their mobility. These days, for security
robots, unmanned aerial vehicles (UAVs) have quickly emerged by highlighting
their advantage: they can even go to any hazardous place that humans cannot
access. For UAVs, Drone has been a representative model and has several merits
to consist of various sensors such as high-resolution cameras. Therefore, Drone
is the most suitable as a mobile surveillance robot. These attractive
advantages such as high-resolution cameras and mobility can be a double-edged
sword, i.e., privacy infringement. Surveillance drones take videos with
high-resolution to fulfill their role, however, those contain a lot of privacy
sensitive information. The indiscriminate shooting is a critical issue for
those who are very reluctant to be exposed. To tackle the privacy infringement,
this work proposes face-anonymizing drone patrol system. In this system, one
person's face in a video is transformed into a different face with facial
components maintained. To construct our privacy-preserving system, we have
adopted the latest generative adversarial networks frameworks and have some
modifications on losses of those frameworks. Our face-anonymzing approach is
evaluated with various public face-image and video dataset. Moreover, our
system is evaluated with a customized drone consisting of a high-resolution
camera, a companion computer, and a drone control computer. Finally, we confirm
that our system can protect privacy sensitive information with our
face-anonymzing algorithm while preserving the performance of robot perception,
i.e., simultaneous localization and mapping. | [
"cs.CV",
"cs.CR",
"eess.IV"
] |
We propose a simple but effective modification of the discriminators, namely
measure-conditional discriminators, as a plug-and-play module for different
GANs. By taking the generated distributions as part of input so that the target
optimum for the discriminator is stationary, the proposed discriminator is more
robust than the vanilla one. A variant of the measure-conditional discriminator
can also handle multiple target distributions, or act as a surrogate model of
statistical distances such as KL divergence with applications to transfer
learning. | [
"cs.LG"
] |
Gesture recognition opens up new ways for humans to intuitively interact with
machines. Especially for service robots, gestures can be a valuable addition to
the means of communication to, for example, draw the robot's attention to
someone or something. Extracting a gesture from video data and classifying it
is a challenging task and a variety of approaches have been proposed throughout
the years. This paper presents a method for gesture recognition in RGB videos
using OpenPose to extract the pose of a person and Dynamic Time Warping (DTW)
in conjunction with One-Nearest-Neighbor (1NN) for time-series classification.
The main features of this approach are the independence of any specific
hardware and high flexibility, because new gestures can be added to the
classifier by adding only a few examples of it. We utilize the robustness of
the Deep Learning-based OpenPose framework while avoiding the data-intensive
task of training a neural network ourselves. We demonstrate the classification
performance of our method using a public dataset. | [
"cs.CV",
"cs.LG",
"cs.RO"
] |
Deep learning (DL) models for disease classification or segmentation from
medical images are increasingly trained using transfer learning (TL) from
unrelated natural world images. However, shortcomings and utility of TL for
specialized tasks in the medical imaging domain remain unknown and are based on
assumptions that increasing training data will improve performance. We report
detailed comparisons, rigorous statistical analysis and comparisons of widely
used DL architecture for binary segmentation after TL with ImageNet
initialization (TII-models) with supervised learning with only medical
images(LMI-models) of macroscopic optical skin cancer, microscopic prostate
core biopsy and Computed Tomography (CT) DICOM images. Through visual
inspection of TII and LMI model outputs and their Grad-CAM counterparts, our
results identify several counter intuitive scenarios where automated
segmentation of one tumor by both models or the use of individual segmentation
output masks in various combinations from individual models leads to 10%
increase in performance. We also report sophisticated ensemble DL strategies
for achieving clinical grade medical image segmentation and model explanations
under low data regimes. For example; estimating performance, explanations and
replicability of LMI and TII models described by us can be used for situations
in which sparsity promotes better learning. A free GitHub repository of TII and
LMI models, code and more than 10,000 medical images and their Grad-CAM output
from this study can be used as starting points for advanced computational
medicine and DL research for biomedical discovery and applications. | [
"stat.ML",
"cs.LG"
] |
The optimal predictor for a linear dynamical system (with hidden state and
Gaussian noise) takes the form of an autoregressive linear filter, namely the
Kalman filter. However, a fundamental problem in reinforcement learning and
control theory is to make optimal predictions in an unknown dynamical system.
To this end, we take the approach of directly learning an autoregressive filter
for time-series prediction under unknown dynamics. Our analysis differs from
previous statistical analyses in that we regress not only on the inputs to the
dynamical system, but also the outputs, which is essential to dealing with
process noise. The main challenge is to estimate the filter under worst case
input (in $\mathcal H_\infty$ norm), for which we use an $L^\infty$-based
objective rather than ordinary least-squares. For learning an autoregressive
model, our algorithm has optimal sample complexity in terms of the rollout
length, which does not seem to be attained by naive least-squares. | [
"cs.LG",
"math.OC",
"stat.ML"
] |
The fully convolutional network (FCN) has dominated salient object detection
for a long period. However, the locality of CNN requires the model deep enough
to have a global receptive field and such a deep model always leads to the loss
of local details. In this paper, we introduce a new attention-based encoder,
vision transformer, into salient object detection to ensure the globalization
of the representations from shallow to deep layers. With the global view in
very shallow layers, the transformer encoder preserves more local
representations to recover the spatial details in final saliency maps. Besides,
as each layer can capture a global view of its previous layer, adjacent layers
can implicitly maximize the representation differences and minimize the
redundant features, making that every output feature of transformer layers
contributes uniquely for final prediction. To decode features from the
transformer, we propose a simple yet effective deeply-transformed decoder. The
decoder densely decodes and upsamples the transformer features, generating the
final saliency map with less noise injection. Experimental results demonstrate
that our method significantly outperforms other FCN-based and transformer-based
methods in five benchmarks by a large margin, with an average of 12.17%
improvement in terms of Mean Absolute Error (MAE). Code will be available at
https://github.com/OliverRensu/GLSTR. | [
"cs.CV"
] |
The proposed work aims at proposing a alternative kernel decomposition in the
context of kernel machines with indefinite kernels. The original paper of KSVM
(SVM in Kre\v{i}n spaces) uses the eigen-decomposition, our proposition avoids
this decompostion. We explain how it can help in designing an algorithm that
won't require to compute the full kernel matrix. Finally we illustrate the good
behavior of the proposed method compared to KSVM. | [
"cs.LG"
] |
Reading text in the wild is a very challenging task due to the diversity of
text instances and the complexity of natural scenes. Recently, the community
has paid increasing attention to the problem of recognizing text instances with
irregular shapes. One intuitive and effective way to handle this problem is to
rectify irregular text to a canonical form before recognition. However, these
methods might struggle when dealing with highly curved or distorted text
instances. To tackle this issue, we propose in this paper a
Symmetry-constrained Rectification Network (ScRN) based on local attributes of
text instances, such as center line, scale and orientation. Such constraints
with an accurate description of text shape enable ScRN to generate better
rectification results than existing methods and thus lead to higher recognition
accuracy. Our method achieves state-of-the-art performance on text with both
regular and irregular shapes. Specifically, the system outperforms existing
algorithms by a large margin on datasets that contain quite a proportion of
irregular text instances, e.g., ICDAR 2015, SVT-Perspective and CUTE80. | [
"cs.CV"
] |
Many deep learning architectures for semantic segmentation involve a Fully
Convolutional Neural Network (FCN) followed by a Conditional Random Field (CRF)
to carry out inference over an image. These models typically involve unary
potentials based on local appearance features computed by FCNs, and binary
potentials based on the displacement between pixels. We show that while current
methods succeed in segmenting whole objects, they perform poorly in situations
involving a large number of object parts. We therefore suggest incorporating
into the inference algorithm additional higher-order potentials inspired by the
way humans identify and localize parts. We incorporate two relations that were
shown to be useful to human object identification - containment and attachment
- into the energy term of the CRF and evaluate their performance on the Pascal
VOC Parts dataset. Our experimental results show that the segmentation of fine
parts is positively affected by the addition of these two relations, and that
the segmentation of fine parts can be further influenced by complex structural
features. | [
"cs.CV"
] |
Reinforcement learning (RL) methods have been shown to be capable of learning
intelligent behavior in rich domains. However, this has largely been done in
simulated domains without adequate focus on the process of building the
simulator. In this paper, we consider a setting where we have access to an
ensemble of pre-trained and possibly inaccurate simulators (models). We
approximate the real environment using a state-dependent linear combination of
the ensemble, where the coefficients are determined by the given state features
and some unknown parameters. Our proposed algorithm provably learns a
near-optimal policy with a sample complexity polynomial in the number of
unknown parameters, and incurs no dependence on the size of the state (or
action) space. As an extension, we also consider the more challenging problem
of model selection, where the state features are unknown and can be chosen from
a large candidate set. We provide exponential lower bounds that illustrate the
fundamental hardness of this problem, and develop a provably efficient
algorithm under additional natural assumptions. | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
We propose a novel method called deep convolutional decision jungle (CDJ) and
its learning algorithm for image classification. The CDJ maintains the
structure of standard convolutional neural networks (CNNs), i.e. multiple
layers of multiple response maps fully connected. Each response map-or node-in
both the convolutional and fully-connected layers selectively respond to class
labels s.t. each data sample travels via a specific soft route of those
activated nodes. The proposed method CDJ automatically learns features, whereas
decision forests and jungles require pre-defined feature sets. Compared to
CNNs, the method embeds the benefits of using data-dependent discriminative
functions, which better handles multi-modal/heterogeneous data; further,the
method offers more diverse sparse network responses, which in turn can be used
for cost-effective learning/classification. The network is learnt by combining
conventional softmax and proposed entropy losses in each layer. The entropy
loss,as used in decision tree growing, measures the purity of data activation
according to the class label distribution. The back-propagation rule for the
proposed loss function is derived from stochastic gradient descent (SGD)
optimization of CNNs. We show that our proposed method outperforms
state-of-the-art methods on three public image classification benchmarks and
one face verification dataset. We also demonstrate the use of auxiliary data
labels, when available, which helps our method to learn more discriminative
routing and representations and leads to improved classification. | [
"cs.CV"
] |
Time-series forecasting is one of the most active research topics in
artificial intelligence. Applications in real-world time series should consider
two factors for achieving reliable predictions: modeling dynamic dependencies
among multiple variables and adjusting the model's intrinsic hyperparameters. A
still open gap in that literature is that statistical and ensemble learning
approaches systematically present lower predictive performance than deep
learning methods. They generally disregard the data sequence aspect entangled
with multivariate data represented in more than one time series. Conversely,
this work presents a novel neural network architecture for time-series
forecasting that combines the power of graph evolution with deep recurrent
learning on distinct data distributions; we named our method Recurrent Graph
Evolution Neural Network (ReGENN). The idea is to infer multiple multivariate
relationships between co-occurring time-series by assuming that the temporal
data depends not only on inner variables and intra-temporal relationships
(i.e., observations from itself) but also on outer variables and inter-temporal
relationships (i.e., observations from other-selves). An extensive set of
experiments was conducted comparing ReGENN with dozens of ensemble methods and
classical statistical ones, showing sound improvement of up to 64.87% over the
competing algorithms. Furthermore, we present an analysis of the intermediate
weights arising from ReGENN, showing that by looking at inter and
intra-temporal relationships simultaneously, time-series forecasting is majorly
improved if paying attention to how multiple multivariate data synchronously
evolve. | [
"cs.LG",
"cs.AI",
"cs.NE",
"stat.ML",
"37M10, 68T07, 68T05, 68T37, 82C32",
"I.2; I.5; I.2.4; I.2.6; I.5.1"
] |
We propose a new defense mechanism against adversarial attacks inspired by an
optical co-processor, providing robustness without compromising natural
accuracy in both white-box and black-box settings. This hardware co-processor
performs a nonlinear fixed random transformation, where the parameters are
unknown and impossible to retrieve with sufficient precision for large enough
dimensions. In the white-box setting, our defense works by obfuscating the
parameters of the random projection. Unlike other defenses relying on
obfuscated gradients, we find we are unable to build a reliable backward
differentiable approximation for obfuscated parameters. Moreover, while our
model reaches a good natural accuracy with a hybrid backpropagation - synthetic
gradient method, the same approach is suboptimal if employed to generate
adversarial examples. We find the combination of a random projection and
binarization in the optical system also improves robustness against various
types of black-box attacks. Finally, our hybrid training method builds robust
features against transfer attacks. We demonstrate our approach on a VGG-like
architecture, placing the defense on top of the convolutional features, on
CIFAR-10 and CIFAR-100. Code is available at
https://github.com/lightonai/adversarial-robustness-by-design. | [
"cs.CV",
"cs.LG"
] |
We consider statistical learning problems, when the distribution $P'$ of the
training observations $Z'_1,\; \ldots,\; Z'_n$ differs from the distribution
$P$ involved in the risk one seeks to minimize (referred to as the test
distribution) but is still defined on the same measurable space as $P$ and
dominates it. In the unrealistic case where the likelihood ratio
$\Phi(z)=dP/dP'(z)$ is known, one may straightforwardly extends the Empirical
Risk Minimization (ERM) approach to this specific transfer learning setup using
the same idea as that behind Importance Sampling, by minimizing a weighted
version of the empirical risk functional computed from the 'biased' training
data $Z'_i$ with weights $\Phi(Z'_i)$. Although the importance function
$\Phi(z)$ is generally unknown in practice, we show that, in various situations
frequently encountered in practice, it takes a simple form and can be directly
estimated from the $Z'_i$'s and some auxiliary information on the statistical
population $P$. By means of linearization techniques, we then prove that the
generalization capacity of the approach aforementioned is preserved when
plugging the resulting estimates of the $\Phi(Z'_i)$'s into the weighted
empirical risk. Beyond these theoretical guarantees, numerical results provide
strong empirical evidence of the relevance of the approach promoted in this
article. | [
"stat.ML",
"cs.LG"
] |
The composition of elementary behaviors to solve challenging transfer
learning problems is one of the key elements in building intelligent machines.
To date, there has been plenty of work on learning task-specific policies or
skills but almost no focus on composing necessary, task-agnostic skills to find
a solution to new problems. In this paper, we propose a novel deep
reinforcement learning-based skill transfer and composition method that takes
the agent's primitive policies to solve unseen tasks. We evaluate our method in
difficult cases where training policy through standard reinforcement learning
(RL) or even hierarchical RL is either not feasible or exhibits high sample
complexity. We show that our method not only transfers skills to new problem
settings but also solves the challenging environments requiring both task
planning and motion control with high data efficiency. | [
"cs.LG",
"cs.AI",
"cs.RO",
"stat.ML"
] |
Background: Accurate diagnosis of skull base tumors is essential for
providing personalized surgical treatment strategies. Intraoperative diagnosis
can be challenging due to tumor diversity and lack of intraoperative pathology
resources.
Objective: To develop an independent and parallel intraoperative pathology
workflow that can provide rapid and accurate skull base tumor diagnoses using
label-free optical imaging and artificial intelligence (AI).
Method: We used a fiber laser-based, label-free, non-consumptive,
high-resolution microscopy method ($<$ 60 sec per 1 $\times$ 1 mm$^\text{2}$),
called stimulated Raman histology (SRH), to image a consecutive, multicenter
cohort of skull base tumor patients. SRH images were then used to train a
convolutional neural network (CNN) model using three representation learning
strategies: cross-entropy, self-supervised contrastive learning, and supervised
contrastive learning. Our trained CNN models were tested on a held-out,
multicenter SRH dataset.
Results: SRH was able to image the diagnostic features of both benign and
malignant skull base tumors. Of the three representation learning strategies,
supervised contrastive learning most effectively learned the distinctive and
diagnostic SRH image features for each of the skull base tumor types. In our
multicenter testing set, cross-entropy achieved an overall diagnostic accuracy
of 91.5%, self-supervised contrastive learning 83.9%, and supervised
contrastive learning 96.6%. Our trained model was able to identify tumor-normal
margins and detect regions of microscopic tumor infiltration in whole-slide SRH
images.
Conclusion: SRH with AI models trained using contrastive representation
learning can provide rapid and accurate intraoperative diagnosis of skull base
tumors. | [
"cs.CV",
"cs.AI",
"cs.LG"
] |
In video object tracking, there exist rich temporal contexts among successive
frames, which have been largely overlooked in existing trackers. In this work,
we bridge the individual video frames and explore the temporal contexts across
them via a transformer architecture for robust object tracking. Different from
classic usage of the transformer in natural language processing tasks, we
separate its encoder and decoder into two parallel branches and carefully
design them within the Siamese-like tracking pipelines. The transformer encoder
promotes the target templates via attention-based feature reinforcement, which
benefits the high-quality tracking model generation. The transformer decoder
propagates the tracking cues from previous templates to the current frame,
which facilitates the object searching process. Our transformer-assisted
tracking framework is neat and trained in an end-to-end manner. With the
proposed transformer, a simple Siamese matching approach is able to outperform
the current top-performing trackers. By combining our transformer with the
recent discriminative tracking pipeline, our method sets several new
state-of-the-art records on prevalent tracking benchmarks. | [
"cs.CV"
] |
It is of significance for an agent to learn a widely applicable and
general-purpose policy that can achieve diverse goals including images and text
descriptions. Considering such perceptually-specific goals, the frontier of
deep reinforcement learning research is to learn a goal-conditioned policy
without hand-crafted rewards. To learn this kind of policy, recent works
usually take as the reward the non-parametric distance to a given goal in an
explicit embedding space. From a different viewpoint, we propose a novel
unsupervised learning approach named goal-conditioned policy with intrinsic
motivation (GPIM), which jointly learns both an abstract-level policy and a
goal-conditioned policy. The abstract-level policy is conditioned on a latent
variable to optimize a discriminator and discovers diverse states that are
further rendered into perceptually-specific goals for the goal-conditioned
policy. The learned discriminator serves as an intrinsic reward function for
the goal-conditioned policy to imitate the trajectory induced by the
abstract-level policy. Experiments on various robotic tasks demonstrate the
effectiveness and efficiency of our proposed GPIM method which substantially
outperforms prior techniques. | [
"cs.LG",
"cs.RO"
] |
Recent terrorist attacks in major cities around the world have brought many
casualties among innocent citizens. One potential threat is represented by
abandoned luggage items (that could contain bombs or biological warfare) in
public areas. In this paper, we describe an approach for real-time automatic
detection of abandoned luggage in video captured by surveillance cameras. The
approach is comprised of two stages: (i) static object detection based on
background subtraction and motion estimation and (ii) abandoned luggage
recognition based on a cascade of convolutional neural networks (CNN). To train
our neural networks we provide two types of examples: images collected from the
Internet and realistic examples generated by imposing various suitcases and
bags over the scene's background. We present empirical results demonstrating
that our approach yields better performance than a strong CNN baseline method. | [
"cs.CV"
] |
This paper augments the reward received by a reinforcement learning agent
with potential functions in order to help the agent learn (possibly stochastic)
optimal policies. We show that a potential-based reward shaping scheme is able
to preserve optimality of stochastic policies, and demonstrate that the ability
of an agent to learn an optimal policy is not affected when this scheme is
augmented to soft Q-learning. We propose a method to impart potential based
advice schemes to policy gradient algorithms. An algorithm that considers an
advantage actor-critic architecture augmented with this scheme is proposed, and
we give guarantees on its convergence. Finally, we evaluate our approach on a
puddle-jump grid world with indistinguishable states, and the continuous state
and action mountain car environment from classical control. Our results
indicate that these schemes allow the agent to learn a stochastic optimal
policy faster and obtain a higher average reward. | [
"cs.LG",
"cs.AI",
"cs.SY",
"eess.SY",
"stat.ML"
] |
Before deploying machine learning models it is critical to assess their
robustness. In the context of deep neural networks for image understanding,
changing the object location, rotation and size may affect the predictions in
non-trivial ways. In this work we perform a fine-grained analysis of robustness
with respect to these factors of variation using SI-Score, a synthetic dataset.
In particular, we investigate ResNets, Vision Transformers and CLIP, and
identify interesting qualitative differences between these. | [
"cs.CV",
"cs.AI",
"cs.LG"
] |
Transforming a thermal infrared image into a robust perceptual colour Visible
image is an ill-posed problem due to the differences in their spectral domains
and in the objects' representations. Objects appear in one spectrum but not
necessarily in the other, and the thermal signature of a single object may have
different colours in its Visible representation. This makes a direct mapping
from thermal to Visible images impossible and necessitates a solution that
preserves texture captured in the thermal spectrum while predicting the
possible colour for certain objects. In this work, a deep learning method to
map the thermal signature from the thermal image's spectrum to a Visible
representation in their low-frequency space is proposed. A pan-sharpening
method is then used to merge the predicted low-frequency representation with
the high-frequency representation extracted from the thermal image. The
proposed model generates colour values consistent with the Visible ground truth
when the object does not vary much in its appearance and generates averaged
grey values in other cases. The proposed method shows robust perceptual night
vision images in preserving the object's appearance and image context compared
with the existing state-of-the-art. | [
"cs.CV",
"cs.LG"
] |
Deep neural networks are the default choice of learning models for computer
vision tasks. Extensive work has been carried out in recent years on explaining
deep models for vision tasks such as classification. However, recent work has
shown that it is possible for these models to produce substantially different
attribution maps even when two very similar images are given to the network,
raising serious questions about trustworthiness. To address this issue, we
propose a robust attribution training strategy to improve attributional
robustness of deep neural networks. Our method carefully analyzes the
requirements for attributional robustness and introduces two new regularizers
that preserve a model's attribution map during attacks. Our method surpasses
state-of-the-art attributional robustness methods by a margin of approximately
3% to 9% in terms of attribution robustness measures on several datasets
including MNIST, FMNIST, Flower and GTSRB. | [
"cs.CV",
"cs.AI"
] |
We consider the problem of recovering an expert's reward function with
inverse reinforcement learning (IRL) when there are missing/incomplete
state-action pairs or observations in the demonstrated trajectories. This issue
of missing trajectory data or information occurs in many situations, e.g., GPS
signals from vehicles moving on a road network are intermittent. In this paper,
we propose a tractable approach to directly compute the log-likelihood of
demonstrated trajectories with incomplete/missing data. Our algorithm is
efficient in handling a large number of missing segments in the demonstrated
trajectories, as it performs the training with incomplete data by solving a
sequence of systems of linear equations, and the number of such systems to be
solved does not depend on the number of missing segments. Empirical evaluation
on a real-world dataset shows that our training algorithm outperforms other
conventional techniques. | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
Generative Adversarial Networks (GANs) have shown great promise in modeling
high dimensional data. The learning objective of GANs usually minimizes some
measure discrepancy, \textit{e.g.}, $f$-divergence~($f$-GANs) or Integral
Probability Metric~(Wasserstein GANs). With $f$-divergence as the objective
function, the discriminator essentially estimates the density ratio, and the
estimated ratio proves useful in further improving the sample quality of the
generator. However, how to leverage the information contained in the
discriminator of Wasserstein GANs (WGAN) is less explored. In this paper, we
introduce the Discriminator Contrastive Divergence, which is well motivated by
the property of WGAN's discriminator and the relationship between WGAN and
energy-based model. Compared to standard GANs, where the generator is directly
utilized to obtain new samples, our method proposes a semi-amortized generation
procedure where the samples are produced with the generator's output as an
initial state. Then several steps of Langevin dynamics are conducted using the
gradient of the discriminator. We demonstrate the benefits of significant
improved generation on both synthetic data and several real-world image
generation benchmarks. | [
"cs.LG",
"cs.CV",
"stat.ML"
] |
Determining the spread of GTV$_{LN}$ is essential in defining the respective
resection or irradiating regions for the downstream workflows of surgical
resection and radiotherapy for many cancers. Different from the more common
enlarged lymph node (LN), GTV$_{LN}$ also includes smaller ones if associated
with high positron emission tomography signals and/or any metastasis signs in
CT. This is a daunting task. In this work, we propose a unified LN appearance
and inter-LN relationship learning framework to detect the true GTV$_{LN}$.
This is motivated by the prior clinical knowledge that LNs form a connected
lymphatic system, and the spread of cancer cells among LNs often follows
certain pathways. Specifically, we first utilize a 3D convolutional neural
network with ROI-pooling to extract the GTV$_{LN}$'s instance-wise appearance
features. Next, we introduce a graph neural network to further model the
inter-LN relationships where the global LN-tumor spatial priors are included in
the learning process. This leads to an end-to-end trainable network to detect
by classifying GTV$_{LN}$. We operate our model on a set of GTV$_{LN}$
candidates generated by a preliminary 1st-stage method, which has a sensitivity
of $>85\%$ at the cost of high false positive (FP) ($>15$ FPs per patient). We
validate our approach on a radiotherapy dataset with 142 paired PET/RTCT scans
containing the chest and upper abdominal body parts. The proposed method
significantly improves over the state-of-the-art (SOTA) LN classification
method by $5.5\%$ and $13.1\%$ in F1 score and the averaged sensitivity value
at $2, 3, 4, 6$ FPs per patient, respectively. | [
"cs.CV"
] |
We introduce a collection of datasets from fundamental physics research --
including particle physics, astroparticle physics, and hadron- and nuclear
physics -- for supervised machine learning studies. These datasets, containing
hadronic top quarks, cosmic-ray induced air showers, phase transitions in
hadronic matter, and generator-level histories, are made public to simplify
future work on cross-disciplinary machine learning and transfer learning in
fundamental physics. Based on these data, we present a simple yet flexible
graph-based neural network architecture that can easily be applied to a wide
range of supervised learning tasks in these domains. We show that our approach
reaches performance close to state-of-the-art dedicated methods on all
datasets. To simplify adaptation for various problems, we provide
easy-to-follow instructions on how graph-based representations of data
structures, relevant for fundamental physics, can be constructed and provide
code implementations for several of them. Implementations are also provided for
our proposed method and all reference algorithms. | [
"cs.LG",
"astro-ph.IM",
"hep-ph",
"nucl-th",
"physics.data-an",
"stat.ML"
] |
Direct policy gradient methods for reinforcement learning are a successful
approach for a variety of reasons: they are model free, they directly optimize
the performance metric of interest, and they allow for richly parameterized
policies. Their primary drawback is that, by being local in nature, they fail
to adequately explore the environment. In contrast, while model-based
approaches and Q-learning directly handle exploration through the use of
optimism, their ability to handle model misspecification and function
approximation is far less evident. This work introduces the the Policy
Cover-Policy Gradient (PC-PG) algorithm, which provably balances the
exploration vs. exploitation tradeoff using an ensemble of learned policies
(the policy cover). PC-PG enjoys polynomial sample complexity and run time for
both tabular MDPs and, more generally, linear MDPs in an infinite dimensional
RKHS. Furthermore, PC-PG also has strong guarantees under model
misspecification that go beyond the standard worst case $\ell_{\infty}$
assumptions; this includes approximation guarantees for state aggregation under
an average case error assumption, along with guarantees under a more general
assumption where the approximation error under distribution shift is
controlled. We complement the theory with empirical evaluation across a variety
of domains in both reward-free and reward-driven settings. | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
Self-supervised learning has emerged as a strategy to reduce the reliance on
costly supervised signal by pretraining representations only using unlabeled
data. These methods combine heuristic proxy classification tasks with data
augmentations and have achieved significant success, but our theoretical
understanding of this success remains limited. In this paper we analyze
self-supervised representation learning using a causal framework. We show how
data augmentations can be more effectively utilized through explicit invariance
constraints on the proxy classifiers employed during pretraining. Based on
this, we propose a novel self-supervised objective, Representation Learning via
Invariant Causal Mechanisms (ReLIC), that enforces invariant prediction of
proxy targets across augmentations through an invariance regularizer which
yields improved generalization guarantees. Further, using causality we
generalize contrastive learning, a particular kind of self-supervised method,
and provide an alternative theoretical explanation for the success of these
methods. Empirically, ReLIC significantly outperforms competing methods in
terms of robustness and out-of-distribution generalization on ImageNet, while
also significantly outperforming these methods on Atari achieving above
human-level performance on $51$ out of $57$ games. | [
"cs.LG",
"cs.CV",
"stat.ML"
] |
Link prediction is one of the key problems for graph-structured data. With
the advancement of graph neural networks, graph autoencoders (GAEs) and
variational graph autoencoders (VGAEs) have been proposed to learn graph
embeddings in an unsupervised way. It has been shown that these methods are
effective for link prediction tasks. However, they do not work well in link
predictions when a node whose degree is zero (i.g., isolated node) is involved.
We have found that GAEs/VGAEs make embeddings of isolated nodes close to zero
regardless of their content features. In this paper, we propose a novel
Variational Graph Normalized AutoEncoder (VGNAE) that utilize L2-normalization
to derive better embeddings for isolated nodes. We show that our VGNAEs
outperform the existing state-of-the-art models for link prediction tasks. The
code is available at https://github.com/SeongJinAhn/VGNAE. | [
"cs.LG",
"cs.AI"
] |
In recent years the importance of finding a meaningful pattern from huge
datasets has become more challenging. Data miners try to adopt innovative
methods to face this problem by applying feature selection methods. In this
paper we propose a new hybrid method in which we use a combination of
resampling, filtering the sample domain and wrapper subset evaluation method
with genetic search to reduce dimensions of Lung-Cancer dataset that we
received from UCI Repository of Machine Learning databases. Finally, we apply
some well- known classification algorithms (Na\"ive Bayes, Logistic, Multilayer
Perceptron, Best First Decision Tree and JRIP) to the resulting dataset and
compare the results and prediction rates before and after the application of
our feature selection method on that dataset. The results show a substantial
progress in the average performance of five classification algorithms
simultaneously and the classification error for these classifiers decreases
considerably. The experiments also show that this method outperforms other
feature selection methods with a lower cost. | [
"cs.LG"
] |
Despite the growing discriminative capabilities of modern deep learning
methods for recognition tasks, the inner workings of the state-of-art models
still remain mostly black-boxes. In this paper, we propose a systematic
interpretation of model parameters and hidden representations of Residual
Temporal Convolutional Networks (Res-TCN) for action recognition in time-series
data. We also propose a Feature Map Decoder as part of the interpretation
analysis, which outputs a representation of model's hidden variables in the
same domain as the input. Such analysis empowers us to expose model's
characteristic learning patterns in an interpretable way. For example, through
the diagnosis analysis, we discovered that our model has learned to achieve
view-point invariance by implicitly learning to perform rotational
normalization of the input to a more discriminative view. Based on the findings
from the model interpretation analysis, we propose a targeted refinement
technique, which can generalize to various other recognition models. The
proposed work introduces a three-stage paradigm for model learning: training,
interpretable diagnosis and targeted refinement. We validate our approach on
skeleton based 3D human action recognition benchmark of NTU RGB+D. We show that
the proposed workflow is an effective model learning strategy and the resulting
Multi-stream Residual Temporal Convolutional Network (MS-Res-TCN) achieves the
state-of-the-art performance on NTU RGB+D. | [
"cs.CV"
] |
X-ray security screening is widely used to maintain aviation/transport
security, and its significance poses a particular interest in automated
screening systems. This paper aims to review computerised X-ray security
imaging algorithms by taxonomising the field into conventional machine learning
and contemporary deep learning applications. The first part briefly discusses
the classical machine learning approaches utilised within X-ray security
imaging, while the latter part thoroughly investigates the use of modern deep
learning algorithms. The proposed taxonomy sub-categorises the use of deep
learning approaches into supervised, semi-supervised and unsupervised learning,
with a particular focus on object classification, detection, segmentation and
anomaly detection tasks. The paper further explores well-established X-ray
datasets and provides a performance benchmark. Based on the current and future
trends in deep learning, the paper finally presents a discussion and future
directions for X-ray security imagery. | [
"cs.CV"
] |
Graph Neural Networks are perfectly suited to capture latent interactions
between various entities in the spatio-temporal domain (e.g. videos). However,
when an explicit structure is not available, it is not obvious what atomic
elements should be represented as nodes. Current works generally use
pre-trained object detectors or fixed, predefined regions to extract graph
nodes. In turn, our proposed model learns nodes that dynamically attach to
salient space-time regions, which are relevant for a higher-level task, without
using any object-level supervision. Constructing these localised, adaptive
nodes gives our model inductive bias towards object-centric representations and
we show that it discovers regions that are well correlated with objects in the
video. The localised nodes are the key components of the method and visualising
their regions leads to a more explainable model. In extensive ablation studies
and experiments on two challenging datasets we show superior performance to
previous graph neural networks models for video classification. | [
"cs.CV",
"cs.LG"
] |
Traditional action recognition models are constructed around the paradigm of
2D perspective imagery. Though sophisticated time-series models have pushed the
field forward, much of the information is still not exploited by confining the
domain to 2D. In this work, we introduce a novel representation of motion as a
voxelized 3D vector field and demonstrate how it can be used to improve
performance of action recognition networks. This volumetric representation is a
natural fit for 3D CNNs, and allows out-of-plane data augmentation techniques
during training of these networks. Both the construction of this representation
from RGB-D video and inference can be run in real time. We demonstrate superior
results using this representation with our network design on the open-source
NTU RGB+D dataset where it outperforms state-of-the-art on both of the defined
evaluation metrics. Furthermore, we experimentally show how the out-of-plane
augmentation techniques create viewpoint invariance and allow the model trained
using this representation to generalize to unseen camera angles. Code is
available here: https://github.com/mpeven/ntu_rgb. | [
"cs.CV",
"eess.IV"
] |
Convolutional neural networks (CNN) have been frequently used to extract
subject-invariant features from electroencephalogram (EEG) for classification
tasks. This approach holds the underlying assumption that electrodes are
equidistant analogous to pixels of an image and hence fails to explore/exploit
the complex functional neural connectivity between different electrode sites.
We overcome this limitation by tailoring the concepts of convolution and
pooling applied to 2D grid-like inputs for the functional network of electrode
sites. Furthermore, we develop various graph neural network (GNN) models that
project electrodes onto the nodes of a graph, where the node features are
represented as EEG channel samples collected over a trial, and nodes can be
connected by weighted/unweighted edges according to a flexible policy
formulated by a neuroscientist. The empirical evaluations show that our
proposed GNN-based framework outperforms standard CNN classifiers across ErrP,
and RSVP datasets, as well as allowing neuroscientific interpretability and
explainability to deep learning methods tailored to EEG related classification
problems. Another practical advantage of our GNN-based framework is that it can
be used in EEG channel selection, which is critical for reducing computational
cost, and designing portable EEG headsets. | [
"cs.LG",
"eess.SP"
] |
This paper tackles a new problem setting: reinforcement learning with
pixel-wise rewards (pixelRL) for image processing. After the introduction of
the deep Q-network, deep RL has been achieving great success. However, the
applications of deep RL for image processing are still limited. Therefore, we
extend deep RL to pixelRL for various image processing applications. In
pixelRL, each pixel has an agent, and the agent changes the pixel value by
taking an action. We also propose an effective learning method for pixelRL that
significantly improves the performance by considering not only the future
states of the own pixel but also those of the neighbor pixels. The proposed
method can be applied to some image processing tasks that require pixel-wise
manipulations, where deep RL has never been applied. We apply the proposed
method to three image processing tasks: image denoising, image restoration, and
local color enhancement. Our experimental results demonstrate that the proposed
method achieves comparable or better performance, compared with the
state-of-the-art methods based on supervised learning. | [
"cs.CV",
"cs.AI"
] |
We present a probabilistic forecasting framework based on convolutional
neural network for multiple related time series forecasting. The framework can
be applied to estimate probability density under both parametric and
non-parametric settings. More specifically, stacked residual blocks based on
dilated causal convolutional nets are constructed to capture the temporal
dependencies of the series. Combined with representation learning, our approach
is able to learn complex patterns such as seasonality, holiday effects within
and across series, and to leverage those patterns for more accurate forecasts,
especially when historical data is sparse or unavailable. Extensive empirical
studies are performed on several real-world datasets, including datasets from
JD.com, China's largest online retailer. The results show that our framework
outperforms other state-of-the-art methods in both accuracy and efficiency. | [
"stat.ML",
"cs.LG"
] |
Deep learning techniques have provided significant improvements in
hyperspectral image (HSI) classification. The current deep learning based HSI
classifiers follow a patch-based learning framework by dividing the image into
overlapping patches. As such, these methods are local learning methods, which
have a high computational cost. In this paper, a fast patch-free global
learning (FPGA) framework is proposed for HSI classification. In FPGA, an
encoder-decoder based FCN is utilized to consider the global spatial
information by processing the whole image, which results in fast inference.
However, it is difficult to directly utilize the encoder-decoder based FCN for
HSI classification as it always fails to converge due to the insufficiently
diverse gradients caused by the limited training samples. To solve the
divergence problem and maintain the abilities of FCN of fast inference and
global spatial information mining, a global stochastic stratified sampling
strategy is first proposed by transforming all the training samples into a
stochastic sequence of stratified samples. This strategy can obtain diverse
gradients to guarantee the convergence of the FCN in the FPGA framework. For a
better design of FCN architecture, FreeNet, which is a fully end-to-end network
for HSI classification, is proposed to maximize the exploitation of the global
spatial information and boost the performance via a spectral attention based
encoder and a lightweight decoder. A lateral connection module is also designed
to connect the encoder and decoder, fusing the spatial details in the encoder
and the semantic features in the decoder. The experimental results obtained
using three public benchmark datasets suggest that the FPGA framework is
superior to the patch-based framework in both speed and accuracy for HSI
classification. Code has been made available at:
https://github.com/Z-Zheng/FreeNet. | [
"cs.CV",
"eess.IV"
] |
Decision trees are a popular choice of explainable model, but just like
neural networks, they suffer from adversarial examples. Existing algorithms for
fitting decision trees robust against adversarial examples are greedy
heuristics and lack approximation guarantees. In this paper we propose ROCT, a
collection of methods to train decision trees that are optimally robust against
user-specified attack models. We show that the min-max optimization problem
that arises in adversarial learning can be solved using a single minimization
formulation for decision trees with 0-1 loss. We propose such formulations in
Mixed-Integer Linear Programming and Maximum Satisfiability, which widely
available solvers can optimize. We also present a method that determines the
upper bound on adversarial accuracy for any model using bipartite matching. Our
experimental results demonstrate that the existing heuristics achieve close to
optimal scores while ROCT achieves state-of-the-art scores. | [
"cs.LG",
"cs.AI"
] |
Confused about renovating your space? Choosing the perfect color for your
walls is always a challenging task. One does rounds of color consultation and
several patch tests. This paper proposes an AI tool to pitch paint based on
attributes of your room and other furniture, and visualize it on your walls. It
makes the color selection process easy. It takes in images of a room, detects
furniture objects using YOLO object detection. Once these objects have been
detected, the tool picks out color of the object. Later this object specific
information gets appended to the room attributes (room_type, room_size,
preferred_tone, etc) and a deep neural net is trained to make predictions for
color/texture/wallpaper for the walls. Finally, these predictions are
visualized on the walls from the images provided. The idea is to take the
knowledge of a color consultant and pitch colors that suit the walls and
provide a good contrast with the furniture and harmonize with different colors
in the room. Transfer learning for YOLO object detection from the COCO dataset
was used as a starting point and the weights were later fine-tuned by training
on additional images. The model was trained on 1000 records listing the room
and furniture attributes, to predict colors. Given the room image, this method
finds the best color scheme for the walls. These predictions are then
visualized on the walls in the image using image segmentation. The results are
visually appealing and automatically enhance the color look-and-feel. | [
"cs.CV",
"cs.AI"
] |
Deepfake represents a category of face-swapping attacks that leverage machine
learning models such as autoencoders or generative adversarial networks.
Although the concept of the face-swapping is not new, its recent technical
advances make fake content (e.g., images, videos) more realistic and
imperceptible to Humans. Various detection techniques for Deepfake attacks have
been explored. These methods, however, are passive measures against Deepfakes
as they are mitigation strategies after the high-quality fake content is
generated. More importantly, we would like to think ahead of the attackers with
robust defenses. This work aims to take an offensive measure to impede the
generation of high-quality fake images or videos. Specifically, we propose to
use novel transformation-aware adversarially perturbed faces as a defense
against GAN-based Deepfake attacks. Different from the naive adversarial faces,
our proposed approach leverages differentiable random image transformations
during the generation. We also propose to use an ensemble-based approach to
enhance the defense robustness against GAN-based Deepfake variants under the
black-box setting. We show that training a Deepfake model with adversarial
faces can lead to a significant degradation in the quality of synthesized
faces. This degradation is twofold. On the one hand, the quality of the
synthesized faces is reduced with more visual artifacts such that the
synthesized faces are more obviously fake or less convincing to human
observers. On the other hand, the synthesized faces can easily be detected
based on various metrics. | [
"cs.CV",
"cs.CR",
"eess.IV"
] |
This paper proposes an unsupervised bottom-up saliency detection approach by
aggregating complementary background template with refinement. Feature vectors
are extracted from each superpixel to cover regional color, contrast and
texture information. By using these features, a coarse detection for salient
region is realized based on background template achieved by different
combinations of boundary regions instead of only treating four boundaries as
background. Then, by ranking the relevance of the image nodes with foreground
cues extracted from the former saliency map, we obtain an improved result.
Finally, smoothing operation is utilized to refine the foreground-based
saliency map to improve the contrast between salient and non-salient regions
until a close to binary saliency map is reached. Experimental results show that
the proposed algorithm generates more accurate saliency maps and performs
favorably against the state-off-the-art saliency detection methods on four
publicly available datasets. | [
"cs.CV"
] |
Multiple Instance Learning (MIL) gains popularity in many real-life machine
learning applications due to its weakly supervised nature. However, the
corresponding effort on explaining MIL lags behind, and it is usually limited
to presenting instances of a bag that are crucial for a particular prediction.
In this paper, we fill this gap by introducing ProtoMIL, a novel
self-explainable MIL method inspired by the case-based reasoning process that
operates on visual prototypes. Thanks to incorporating prototypical features
into objects description, ProtoMIL unprecedentedly joins the model accuracy and
fine-grained interpretability, which we present with the experiments on five
recognized MIL datasets. | [
"cs.LG",
"cs.AI",
"cs.CV"
] |
Deep neural networks have achieved remarkable success in various challenging
tasks. However, the black-box nature of such networks is not acceptable to
critical applications, such as healthcare. In particular, the existence of
adversarial examples and their overgeneralization to irrelevant,
out-of-distribution inputs with high confidence makes it difficult, if not
impossible, to explain decisions by such networks. In this paper, we analyze
the underlying mechanism of generalization of deep neural networks and propose
an ($n$, $k$) consensus algorithm which is insensitive to adversarial examples
and can reliably reject out-of-distribution samples. Furthermore, the consensus
algorithm is able to improve classification accuracy by using multiple trained
deep neural networks. To handle the complexity of deep neural networks, we
cluster linear approximations of individual models and identify highly
correlated clusters among different models to capture feature importance
robustly, resulting in improved interpretability. Motivated by the importance
of building accurate and interpretable prediction models for healthcare, our
experimental results on an ICU dataset show the effectiveness of our algorithm
in enhancing both the prediction accuracy and the interpretability of deep
neural network models on one-year patient mortality prediction. In particular,
while the proposed method maintains similar interpretability as conventional
shallow models such as logistic regression, it improves the prediction accuracy
significantly. | [
"cs.LG",
"cs.AI",
"cs.CV",
"cs.NE",
"stat.ML"
] |
It has been a primary concern in recent studies of vision and language tasks
to design an effective attention mechanism dealing with interactions between
the two modalities. The Transformer has recently been extended and applied to
several bi-modal tasks, yielding promising results. For visual dialog, it
becomes necessary to consider interactions between three or more inputs, i.e.,
an image, a question, and a dialog history, or even its individual dialog
components. In this paper, we present a neural architecture named Light-weight
Transformer for Many Inputs (LTMI) that can efficiently deal with all the
interactions between multiple such inputs in visual dialog. It has a block
structure similar to the Transformer and employs the same design of attention
computation, whereas it has only a small number of parameters, yet has
sufficient representational power for the purpose. Assuming a standard setting
of visual dialog, a layer built upon the proposed attention block has less than
one-tenth of parameters as compared with its counterpart, a natural Transformer
extension. The experimental results on the VisDial datasets validate the
effectiveness of the proposed approach, showing improvements of the best NDCG
score on the VisDial v1.0 dataset from 57.59 to 60.92 with a single model, from
64.47 to 66.53 with ensemble models, and even to 74.88 with additional
finetuning. Our implementation code is available at
https://github.com/davidnvq/visdial. | [
"cs.CV"
] |
The objective of this paper is to learn context- and depth-aware feature
representation to solve the problem of monocular 3D object detection. We make
following contributions: (i) rather than appealing to the complicated
pseudo-LiDAR based approach, we propose a depth-conditioned dynamic message
propagation (DDMP) network to effectively integrate the multi-scale depth
information with the image context;(ii) this is achieved by first adaptively
sampling context-aware nodes in the image context and then dynamically
predicting hybrid depth-dependent filter weights and affinity matrices for
propagating information; (iii) by augmenting a center-aware depth encoding
(CDE) task, our method successfully alleviates the inaccurate depth prior; (iv)
we thoroughly demonstrate the effectiveness of our proposed approach and show
state-of-the-art results among the monocular-based approaches on the KITTI
benchmark dataset. Particularly, we rank $1^{st}$ in the highly competitive
KITTI monocular 3D object detection track on the submission day (November 16th,
2020). Code and models are released at \url{https://github.com/fudan-zvg/DDMP} | [
"cs.CV"
] |
Generative flows are promising tractable models for density modeling that
define probabilistic distributions with invertible transformations. However,
tractability imposes architectural constraints on generative flows, making them
less expressive than other types of generative models. In this work, we study a
previously overlooked constraint that all the intermediate representations must
have the same dimensionality with the original data due to invertibility,
limiting the width of the network. We tackle this constraint by augmenting the
data with some extra dimensions and jointly learning a generative flow for
augmented data as well as the distribution of augmented dimensions under a
variational inference framework. Our approach, VFlow, is a generalization of
generative flows and therefore always performs better. Combining with existing
generative flows, VFlow achieves a new state-of-the-art 2.98 bits per dimension
on the CIFAR-10 dataset and is more compact than previous models to reach
similar modeling quality. | [
"stat.ML",
"cs.LG",
"G.3; I.2.6"
] |
3D vehicle detection based on point cloud is a challenging task in real-world
applications such as autonomous driving. Despite significant progress has been
made, we observe two aspects to be further improved. First, the semantic
context information in LiDAR is seldom explored in previous works, which may
help identify ambiguous vehicles. Second, the distribution of point cloud on
vehicles varies continuously with increasing depths, which may not be well
modeled by a single model. In this work, we propose a unified model SegVoxelNet
to address the above two problems. A semantic context encoder is proposed to
leverage the free-of-charge semantic segmentation masks in the bird's eye view.
Suspicious regions could be highlighted while noisy regions are suppressed by
this module. To better deal with vehicles at different depths, a novel
depth-aware head is designed to explicitly model the distribution differences
and each part of the depth-aware head is made to focus on its own target
detection range. Extensive experiments on the KITTI dataset show that the
proposed method outperforms the state-of-the-art alternatives in both accuracy
and efficiency with point cloud as input only. | [
"cs.CV"
] |
Image captioning is a multimodal problem that has drawn extensive attention
in both the natural language processing and computer vision community. In this
paper, we present a novel image captioning architecture to better explore
semantics available in captions and leverage that to enhance both image
representation and caption generation. Our models first construct
caption-guided visual relationship graphs that introduce beneficial inductive
bias using weakly supervised multi-instance learning. The representation is
then enhanced with neighbouring and contextual nodes with their textual and
visual features. During generation, the model further incorporates visual
relationships using multi-task learning for jointly predicting word and
object/predicate tag sequences. We perform extensive experiments on the MSCOCO
dataset, showing that the proposed framework significantly outperforms the
baselines, resulting in the state-of-the-art performance under a wide range of
evaluation metrics. | [
"cs.CV",
"cs.CL"
] |
We present a method for inferring dense depth maps from images and sparse
depth measurements by leveraging synthetic data to learn the association of
sparse point clouds with dense natural shapes, and using the image as evidence
to validate the predicted depth map. Our learned prior for natural shapes uses
only sparse depth as input, not images, so the method is not affected by the
covariate shift when attempting to transfer learned models from synthetic data
to real ones. This allows us to use abundant synthetic data with ground truth
to learn the most difficult component of the reconstruction process, which is
topology estimation, and use the image to refine the prediction based on
photometric evidence. Our approach uses fewer parameters than previous methods,
yet, achieves the state of the art on both indoor and outdoor benchmark
datasets. Code available at:
https://github.com/alexklwong/learning-topology-synthetic-data. | [
"cs.CV",
"cs.LG",
"cs.RO"
] |
One of the successful early implementation of deep learning AI technology was
on letter recognition. With the recent breakthrough of artificial intelligence
(AI) brings more solid technology for complex problems like handwritten letter
recognition and even automatic generation of them. In this research, we
proposed deep learning framework called Ludwig AI Framework(LAIF) for Germany
Suetterlin letter recognition and generation. To recognize Suetterlin letter,
we proposed deep convolutional neural network. Since lack of big amount of data
to train for the deep models and huge cost to label existing hard copy of
handwritten letters, we also introduce the methodology with deep generative
adversarial network to generate handwritten letters as synthetic data. Main
source code is in https://github.com/enkhtogtokh/LAIF repository. | [
"cs.CV",
"cs.LG"
] |
Deep metric learning plays a key role in various machine learning tasks. Most
of the previous works have been confined to sampling from a mini-batch, which
cannot precisely characterize the global geometry of the embedding space.
Although researchers have developed proxy- and classification-based methods to
tackle the sampling issue, those methods inevitably incur a redundant
computational cost. In this paper, we propose a novel Proxy-based deep Graph
Metric Learning (ProxyGML) approach from the perspective of graph
classification, which uses fewer proxies yet achieves better comprehensive
performance. Specifically, multiple global proxies are leveraged to
collectively approximate the original data points for each class. To
efficiently capture local neighbor relationships, a small number of such
proxies are adaptively selected to construct similarity subgraphs between these
proxies and each data point. Further, we design a novel reverse label
propagation algorithm, by which the neighbor relationships are adjusted
according to ground-truth labels, so that a discriminative metric space can be
learned during the process of subgraph classification. Extensive experiments
carried out on widely-used CUB-200-2011, Cars196, and Stanford Online Products
datasets demonstrate the superiority of the proposed ProxyGML over the
state-of-the-art methods in terms of both effectiveness and efficiency. The
source code is publicly available at https://github.com/YuehuaZhu/ProxyGML. | [
"cs.CV",
"cs.LG"
] |
Hyperbolic spaces, which have the capacity to embed tree structures without
distortion owing to their exponential volume growth, have recently been applied
to machine learning to better capture the hierarchical nature of data. In this
study, we generalize the fundamental components of neural networks in a single
hyperbolic geometry model, namely, the Poincar\'e ball model. This novel
methodology constructs a multinomial logistic regression, fully-connected
layers, convolutional layers, and attention mechanisms under a unified
mathematical interpretation, without increasing the parameters. Experiments
show the superior parameter efficiency of our methods compared to conventional
hyperbolic components, and stability and outperformance over their Euclidean
counterparts. | [
"cs.LG",
"stat.ML"
] |
We propose a fully automatic method to find standardized view planes in 3D
image acquisitions. Standard view images are important in clinical practice as
they provide a means to perform biometric measurements from similar anatomical
regions. These views are often constrained to the native orientation of a 3D
image acquisition. Navigating through target anatomy to find the required view
plane is tedious and operator-dependent. For this task, we employ a multi-scale
reinforcement learning (RL) agent framework and extensively evaluate several
Deep Q-Network (DQN) based strategies. RL enables a natural learning paradigm
by interaction with the environment, which can be used to mimic experienced
operators. We evaluate our results using the distance between the anatomical
landmarks and detected planes, and the angles between their normal vector and
target. The proposed algorithm is assessed on the mid-sagittal and
anterior-posterior commissure planes of brain MRI, and the 4-chamber long-axis
plane commonly used in cardiac MRI, achieving accuracy of 1.53mm, 1.98mm and
4.84mm, respectively. | [
"cs.CV"
] |
For machines to interact with the physical world, they must understand the
physical properties of objects and materials they encounter. We use fabrics as
an example of a deformable material with a rich set of mechanical properties. A
thin flexible fabric, when draped, tends to look different from a heavy stiff
fabric. It also feels different when touched. Using a collection of 118 fabric
sample, we captured color and depth images of draped fabrics along with tactile
data from a high resolution touch sensor. We then sought to associate the
information from vision and touch by jointly training CNNs across the three
modalities. Through the CNN, each input, regardless of the modality, generates
an embedding vector that records the fabric's physical property. By comparing
the embeddings, our system is able to look at a fabric image and predict how it
will feel, and vice versa. We also show that a system jointly trained on vision
and touch data can outperform a similar system trained only on visual data when
tested purely with visual inputs. | [
"cs.CV"
] |
Autonomous learning has been a promising direction in control and robotics
for more than a decade since data-driven learning allows to reduce the amount
of engineering knowledge, which is otherwise required. However, autonomous
reinforcement learning (RL) approaches typically require many interactions with
the system to learn controllers, which is a practical limitation in real
systems, such as robots, where many interactions can be impractical and time
consuming. To address this problem, current learning approaches typically
require task-specific knowledge in form of expert demonstrations, realistic
simulators, pre-shaped policies, or specific knowledge about the underlying
dynamics. In this article, we follow a different approach and speed up learning
by extracting more information from data. In particular, we learn a
probabilistic, non-parametric Gaussian process transition model of the system.
By explicitly incorporating model uncertainty into long-term planning and
controller learning our approach reduces the effects of model errors, a key
problem in model-based learning. Compared to state-of-the art RL our
model-based policy search method achieves an unprecedented speed of learning.
We demonstrate its applicability to autonomous learning in real robot and
control tasks. | [
"stat.ML",
"cs.LG",
"cs.RO",
"cs.SY"
] |
Recognizing actions in ice hockey using computer vision poses challenges due
to bulky equipment and inadequate image quality. A novel two-stream framework
has been designed to improve action recognition accuracy for hockey using three
main components. First, pose is estimated via the Part Affinity Fields model to
extract meaningful cues from the player. Second, optical flow (using
LiteFlowNet) is used to extract temporal features. Third, pose and optical flow
streams are fused and passed to fully-connected layers to estimate the hockey
player's action. A novel publicly available dataset named HARPET (Hockey Action
Recognition Pose Estimation, Temporal) was created, composed of sequences of
annotated actions and pose of hockey players including their hockey sticks as
an extension of human body pose. Three contributions are recognized. (1) The
novel two-stream architecture achieves 85% action recognition accuracy, with
the inclusion of optical flows increasing accuracy by about 10%. (2) The unique
localization of hand-held objects (e.g., hockey sticks) as part of pose
increases accuracy by about 13%. (3) For pose estimation, a bigger and more
general dataset, MSCOCO, is successfully used for transfer learning to a
smaller and more specific dataset, HARPET, achieving a PCKh of 87%. | [
"cs.CV"
] |
State-of-the-art object detectors rely on regressing and classifying an
extensive list of possible anchors, which are divided into positive and
negative samples based on their intersection-over-union (IoU) with
corresponding groundtruth objects. Such a harsh split conditioned on IoU
results in binary labels that are potentially noisy and challenging for
training. In this paper, we propose to mitigate noise incurred by imperfect
label assignment such that the contributions of anchors are dynamically
determined by a carefully constructed cleanliness score associated with each
anchor. Exploring outputs from both regression and classification branches, the
cleanliness scores, estimated without incurring any additional computational
overhead, are used not only as soft labels to supervise the training of the
classification branch but also sample re-weighting factors for improved
localization and classification accuracy. We conduct extensive experiments on
COCO, and demonstrate, among other things, the proposed approach steadily
improves RetinaNet by ~2% with various backbones. | [
"cs.CV"
] |
Relative position encoding (RPE) is important for transformer to capture
sequence ordering of input tokens. General efficacy has been proven in natural
language processing. However, in computer vision, its efficacy is not well
studied and even remains controversial, e.g., whether relative position
encoding can work equally well as absolute position? In order to clarify this,
we first review existing relative position encoding methods and analyze their
pros and cons when applied in vision transformers. We then propose new relative
position encoding methods dedicated to 2D images, called image RPE (iRPE). Our
methods consider directional relative distance modeling as well as the
interactions between queries and relative position embeddings in self-attention
mechanism. The proposed iRPE methods are simple and lightweight. They can be
easily plugged into transformer blocks. Experiments demonstrate that solely due
to the proposed encoding methods, DeiT and DETR obtain up to 1.5% (top-1 Acc)
and 1.3% (mAP) stable improvements over their original versions on ImageNet and
COCO respectively, without tuning any extra hyperparameters such as learning
rate and weight decay. Our ablation and analysis also yield interesting
findings, some of which run counter to previous understanding. Code and models
are open-sourced at https://github.com/microsoft/Cream/tree/main/iRPE. | [
"cs.CV"
] |
In this paper, we study the influence of both long and short skip connections
on Fully Convolutional Networks (FCN) for biomedical image segmentation. In
standard FCNs, only long skip connections are used to skip features from the
contracting path to the expanding path in order to recover spatial information
lost during downsampling. We extend FCNs by adding short skip connections, that
are similar to the ones introduced in residual networks, in order to build very
deep FCNs (of hundreds of layers). A review of the gradient flow confirms that
for a very deep FCN it is beneficial to have both long and short skip
connections. Finally, we show that a very deep FCN can achieve
near-to-state-of-the-art results on the EM dataset without any further
post-processing. | [
"cs.CV"
] |
In 1950, Forsythe and Leibler (1950) introduced a statistical technique for
finding the inverse of a matrix by characterizing the elements of the matrix
inverse as expected values of a sequence of random walks. Barto and Duff (1994)
subsequently showed relations between this technique and standard dynamic
programming and temporal differencing methods. The advantage of the Monte Carlo
matrix inversion (MCMI) approach is that it scales better with respect to
state-space size than alternative techniques. In this paper, we introduce an
algorithm for performing reinforcement learning policy evaluation using MCMI.
We demonstrate that MCMI improves on runtime over a maximum likelihood
model-based policy evaluation approach and on both runtime and accuracy over
the temporal differencing (TD) policy evaluation approach. We further improve
on MCMI policy evaluation by adding an importance sampling technique to our
algorithm to reduce the variance of our estimator. Lastly, we illustrate
techniques for scaling up MCMI to large state spaces in order to perform policy
improvement. | [
"cs.LG",
"cs.AI",
"cs.NA"
] |
In this work, we propose a (linearized) Alternating Direction
Method-of-Multipliers (ADMM) algorithm for minimizing a convex function subject
to a nonconvex constraint. We focus on the special case where such constraint
arises from the specification that a variable should lie in the range of a
neural network. This is motivated by recent successful applications of
Generative Adversarial Networks (GANs) in tasks like compressive sensing,
denoising and robustness against adversarial examples. The derived rates for
our algorithm are characterized in terms of certain geometric properties of the
generator network, which we show hold for feedforward architectures, under mild
assumptions. Unlike gradient descent (GD), it can efficiently handle non-smooth
objectives as well as exploit efficient partial minimization procedures, thus
being faster in many practical scenarios. | [
"cs.LG",
"math.OC",
"stat.ML"
] |
Humans are very good at directing their visual attention toward relevant
areas when they search for different types of objects. For instance, when we
search for cars, we will look at the streets, not at the top of buildings. The
motivation of this paper is to train a network to do the same via a multi-task
learning approach. To train visual attention, we produce foreground/background
segmentation labels in a semi-supervised way, using background subtraction or
optical flow. Using these labels, we train an object detection model to produce
foreground/background segmentation maps as well as bounding boxes while sharing
most model parameters. We use those segmentation maps inside the network as a
self-attention mechanism to weight the feature map used to produce the bounding
boxes, decreasing the signal of non-relevant areas. We show that by using this
method, we obtain a significant mAP improvement on two traffic surveillance
datasets, with state-of-the-art results on both UA-DETRAC and UAVDT. | [
"cs.CV"
] |
In this paper, we propose Generative Adversarial Network (GAN) architectures
that use Capsule Networks for image-synthesis. Based on the principal of
positional-equivariance of features, Capsule Network's ability to encode
spatial relationships between the features of the image helps it become a more
powerful critic in comparison to Convolutional Neural Networks (CNNs) used in
current architectures for image synthesis. Our proposed GAN architectures learn
the data manifold much faster and therefore, synthesize visually accurate
images in significantly lesser number of training samples and training epochs
in comparison to GANs and its variants that use CNNs. Apart from analyzing the
quantitative results corresponding the images generated by different
architectures, we also explore the reasons for the lower coverage and diversity
explored by the GAN architectures that use CNN critics. | [
"cs.CV",
"cs.LG",
"cs.NE",
"stat.ML"
] |
Composing previously mastered skills to solve novel tasks promises dramatic
improvements in the data efficiency of reinforcement learning. Here, we analyze
two recent works composing behaviors represented in the form of action-value
functions and show that they perform poorly in some situations. As part of this
analysis, we extend an important generalization of policy improvement to the
maximum entropy framework and introduce an algorithm for the practical
implementation of successor features in continuous action spaces. Then we
propose a novel approach which addresses the failure cases of prior work and,
in principle, recovers the optimal policy during transfer. This method works by
explicitly learning the (discounted, future) divergence between base policies.
We study this approach in the tabular case and on non-trivial continuous
control problems with compositional structure and show that it outperforms or
matches existing methods across all tasks considered. | [
"cs.LG",
"stat.ML"
] |
Pushing forward the compute efficacy frontier in deep learning is critical
for tasks that require frequent model re-training or workloads that entail
training a large number of models. We introduce SliceOut -- a dropout-inspired
scheme designed to take advantage of GPU memory layout to train deep learning
models faster without impacting final test accuracy. By dropping contiguous
sets of units at random, our method realises training speedups through (1) fast
memory access and matrix multiplication of smaller tensors, and (2) memory
savings by avoiding allocating memory to zero units in weight gradients and
activations. At test time, turning off SliceOut performs an implicit ensembling
across a linear number of architectures that preserves test accuracy. We
demonstrate 10-40% speedups and memory reduction with Wide ResNets,
EfficientNets, and Transformer models, with minimal to no loss in accuracy.
This leads to faster processing of large computational workloads overall, and
significantly reduce the resulting energy consumption and CO2emissions. | [
"cs.LG",
"stat.ML"
] |
Detecting facial forgery images and videos is an increasingly important topic
in multimedia forensics. As forgery images and videos are usually compressed
into different formats such as JPEG and H264 when circulating on the Internet,
existing forgery-detection methods trained on uncompressed data often suffer
from significant performance degradation in identifying them. To solve this
problem, we propose a novel anti-compression facial forgery detection
framework, which learns a compression-insensitive embedding feature space
utilizing both original and compressed forgeries. Specifically, our approach
consists of three ideas: (i) extracting compression-insensitive features from
both uncompressed and compressed forgeries using an adversarial learning
strategy; (ii) learning a robust partition by constructing a metric loss that
can reduce the distance of the paired original and compressed images in the
embedding space; (iii) improving the accuracy of tampered localization with an
attention-transfer module. Experimental results demonstrate that, the proposed
method is highly effective in handling both compressed and uncompressed facial
forgery images. | [
"cs.CV"
] |
Generative adversarial networks (GANs) are a powerful approach to
unsupervised learning. They have achieved state-of-the-art performance in the
image domain. However, GANs are limited in two ways. They often learn
distributions with low support---a phenomenon known as mode collapse---and they
do not guarantee the existence of a probability density, which makes evaluating
generalization using predictive log-likelihood impossible. In this paper, we
develop the prescribed GAN (PresGAN) to address these shortcomings. PresGANs
add noise to the output of a density network and optimize an
entropy-regularized adversarial loss. The added noise renders tractable
approximations of the predictive log-likelihood and stabilizes the training
procedure. The entropy regularizer encourages PresGANs to capture all the modes
of the data distribution. Fitting PresGANs involves computing the intractable
gradients of the entropy regularization term; PresGANs sidestep this
intractability using unbiased stochastic estimates. We evaluate PresGANs on
several datasets and found they mitigate mode collapse and generate samples
with high perceptual quality. We further found that PresGANs reduce the gap in
performance in terms of predictive log-likelihood between traditional GANs and
variational autoencoders (VAEs). | [
"stat.ML",
"cs.LG",
"stat.ME"
] |
In this paper, we propose Multiresolution Graph Networks (MGN) and
Multiresolution Graph Variational Autoencoders (MGVAE) to learn and generate
graphs in a multiresolution and equivariant manner. At each resolution level,
MGN employs higher order message passing to encode the graph while learning to
partition it into mutually exclusive clusters and coarsening into a lower
resolution. MGVAE constructs a hierarchical generative model based on MGN to
variationally autoencode the hierarchy of coarsened graphs. Our proposed
framework is end-to-end permutation equivariant with respect to node ordering.
Our methods have been successful with several generative tasks including link
prediction on citation graphs, unsupervised molecular representation learning
to predict molecular properties, molecular generation, general graph generation
and graph-based image generation. | [
"cs.LG",
"cs.SI",
"physics.chem-ph"
] |
We are interested in attribute-guided face generation: given a low-res face
input image, an attribute vector that can be extracted from a high-res image
(attribute image), our new method generates a high-res face image for the
low-res input that satisfies the given attributes. To address this problem, we
condition the CycleGAN and propose conditional CycleGAN, which is designed to
1) handle unpaired training data because the training low/high-res and high-res
attribute images may not necessarily align with each other, and to 2) allow
easy control of the appearance of the generated face via the input attributes.
We demonstrate impressive results on the attribute-guided conditional CycleGAN,
which can synthesize realistic face images with appearance easily controlled by
user-supplied attributes (e.g., gender, makeup, hair color, eyeglasses). Using
the attribute image as identity to produce the corresponding conditional vector
and by incorporating a face verification network, the attribute-guided network
becomes the identity-guided conditional CycleGAN which produces impressive and
interesting results on identity transfer. We demonstrate three applications on
identity-guided conditional CycleGAN: identity-preserving face superresolution,
face swapping, and frontal face generation, which consistently show the
advantage of our new method. | [
"cs.CV",
"cs.LG",
"stat.ML"
] |
Among numerous videos shared on the web, well-edited ones always attract more
attention. However, it is difficult for inexperienced users to make well-edited
videos because it requires professional expertise and immense manual labor. To
meet the demands for non-experts, we present Transcript-to-Video -- a
weakly-supervised framework that uses texts as input to automatically create
video sequences from an extensive collection of shots. Specifically, we propose
a Content Retrieval Module and a Temporal Coherent Module to learn
visual-language representations and model shot sequencing styles, respectively.
For fast inference, we introduce an efficient search strategy for real-time
video clip sequencing. Quantitative results and user studies demonstrate
empirically that the proposed learning framework can retrieve content-relevant
shots while creating plausible video sequences in terms of style. Besides, the
run-time performance analysis shows that our framework can support real-world
applications. | [
"cs.CV"
] |
We introduce an asymmetric distance in the space of learning tasks, and a
framework to compute their complexity. These concepts are foundational for the
practice of transfer learning, whereby a parametric model is pre-trained for a
task, and then fine-tuned for another. The framework we develop is
non-asymptotic, captures the finite nature of the training dataset, and allows
distinguishing learning from memorization. It encompasses, as special cases,
classical notions from Kolmogorov complexity, Shannon, and Fisher Information.
However, unlike some of those frameworks, it can be applied to large-scale
models and real-world datasets. Our framework is the first to measure
complexity in a way that accounts for the effect of the optimization scheme,
which is critical in Deep Learning. | [
"cs.LG",
"cs.IT",
"math.IT",
"stat.ML"
] |
We discuss the relative merits of optimistic and randomized approaches to
exploration in reinforcement learning. Optimistic approaches presented in the
literature apply an optimistic boost to the value estimate at each state-action
pair and select actions that are greedy with respect to the resulting
optimistic value function. Randomized approaches sample from among
statistically plausible value functions and select actions that are greedy with
respect to the random sample. Prior computational experience suggests that
randomized approaches can lead to far more statistically efficient learning. We
present two simple analytic examples that elucidate why this is the case. In
principle, there should be optimistic approaches that fare well relative to
randomized approaches, but that would require intractable computation.
Optimistic approaches that have been proposed in the literature sacrifice
statistical efficiency for the sake of computational efficiency. Randomized
approaches, on the other hand, may enable simultaneous statistical and
computational efficiency. | [
"stat.ML",
"cs.LG"
] |
The Correlation Filter is an algorithm that trains a linear template to
discriminate between images and their translations. It is well suited to object
tracking because its formulation in the Fourier domain provides a fast
solution, enabling the detector to be re-trained once per frame. Previous works
that use the Correlation Filter, however, have adopted features that were
either manually designed or trained for a different task. This work is the
first to overcome this limitation by interpreting the Correlation Filter
learner, which has a closed-form solution, as a differentiable layer in a deep
neural network. This enables learning deep features that are tightly coupled to
the Correlation Filter. Experiments illustrate that our method has the
important practical benefit of allowing lightweight architectures to achieve
state-of-the-art performance at high framerates. | [
"cs.CV",
"cs.LG"
] |
In this paper, we present a robust spherical harmonics approach for the
classification of point cloud-based objects. Spherical harmonics have been used
for classification over the years, with several frameworks existing in the
literature. These approaches use variety of spherical harmonics based
descriptors to classify objects. We first investigated these frameworks
robustness against data augmentation, such as outliers and noise, as it has not
been studied before. Then we propose a spherical convolution neural network
framework for robust object classification. The proposed framework uses the
voxel grid of concentric spheres to learn features over the unit ball. Our
proposed model learn features that are less sensitive to data augmentation due
to the selected sampling strategy and the designed convolution operation. We
tested our proposed model against several types of data augmentation, such as
noise and outliers. Our results show that the proposed model outperforms the
state of art networks in terms of robustness to data augmentation. | [
"cs.CV",
"cs.LG"
] |
Populations of neurons in inferotemporal cortex (IT) maintain an explicit
code for object identity that also tolerates transformations of object
appearance e.g., position, scale, viewing angle [1, 2, 3]. Though the learning
rules are not known, recent results [4, 5, 6] suggest the operation of an
unsupervised temporal-association-based method e.g., Foldiak's trace rule [7].
Such methods exploit the temporal continuity of the visual world by assuming
that visual experience over short timescales will tend to have invariant
identity content. Thus, by associating representations of frames from nearby
times, a representation that tolerates whatever transformations occurred in the
video may be achieved. Many previous studies verified that such rules can work
in simple situations without background clutter, but the presence of visual
clutter has remained problematic for this approach. Here we show that temporal
association based on large class-specific filters (templates) avoids the
problem of clutter. Our system learns in an unsupervised way from natural
videos gathered from the internet, and is able to perform a difficult
unconstrained face recognition task on natural images: Labeled Faces in the
Wild [8]. | [
"cs.CV",
"cs.LG"
] |
The vast majority of research on explainability focuses on
post-explainability rather than explainable modeling. Namely, an explanation
model is derived to explain a complex black box model built with the sole
purpose of achieving the highest performance possible. In part, this trend
might be driven by the misconception that there is a trade-off between
explainability and accuracy. Furthermore, the consequential work on Shapely
values, grounded in game theory, has also contributed to a new wave of
post-explainability research on better approximations for various machine
learning models, including deep learning models. We propose a new architecture
that inherently produces explainable predictions in the form of additive
feature attributions. Our approach learns a graph representation for each
record in the dataset. Attribute centric features are then derived from the
graph and fed into a contribution deep set model to produce the final
predictions. We show that our explainable model attains the same level of
performance as black box models. Finally, we provide an augmented model
training approach that leverages the missingness property and yields high
levels of consistency (as required for the Shapely values) without loss of
accuracy. | [
"cs.LG",
"cs.AI"
] |
Frequency control is an important problem in modern recommender systems. It
dictates the delivery frequency of recommendations to maintain product quality
and efficiency. For example, the frequency of delivering promotional
notifications impacts daily metrics as well as the infrastructure resource
consumption (e.g. CPU and memory usage). There remain open questions on what
objective we should optimize to represent business values in the long term
best, and how we should balance between daily metrics and resource consumption
in a dynamically fluctuating environment. We propose a personalized methodology
for the frequency control problem, which combines long-term value optimization
using reinforcement learning (RL) with a robust volume control technique we
termed "Effective Factor". We demonstrate statistically significant improvement
in daily metrics and resource efficiency by our method in several notification
applications at a scale of billions of users. To our best knowledge, our study
represents the first deep RL application on the frequency control problem at
such an industrial scale. | [
"cs.LG",
"cs.AI"
] |
In recent years, deep convolutional neural network (DCNN) has seen a
breakthrough progress in natural image recognition because of three points:
universal approximation ability via DCNN, large-scale database (such as
ImageNet), and supercomputing ability powered by GPU. The remote sensing field
is still lacking a large-scale benchmark compared to ImageNet and Place2. In
this paper, we propose a remote sensing image classification benchmark (RSI-CB)
based on massive, scalable, and diverse crowdsource data. Using crowdsource
data, such as Open Street Map (OSM) data, ground objects in remote sensing
images can be annotated effectively by points of interest, vector data from
OSM, or other crowdsource data. The annotated images can be used in remote
sensing image classification tasks. Based on this method, we construct a
worldwide large-scale benchmark for remote sensing image classification. This
benchmark has two sub-datasets with 256 by 256 and 128 by 128 sizes because
different DCNNs require different image sizes. The former contains 6 categories
with 35 subclasses of more than 24,000 images. The latter contains 6 categories
with 45 subclasses of more than 36,000 images. This classification system of
ground objects is defined according to the national standard of land-use
classification in China and is inspired by the hierarchy mechanism of ImageNet.
Finally, we conduct many experiments to compare RSI-CB with the SAT-4, SAT-6,
and UC-Merced datasets on handcrafted features, such as scale-invariant feature
transform, color histogram, local binary patterns, and GIST, and classical DCNN
models, such as AlexNet, VGGNet, GoogLeNet, and ResNet. | [
"cs.CV"
] |
We explore how to enable machines to model 3D shapes like human modelers
using deep reinforcement learning (RL). In 3D modeling software like Maya, a
modeler usually creates a mesh model in two steps: (1) approximating the shape
using a set of primitives; (2) editing the meshes of the primitives to create
detailed geometry. Inspired by such artist-based modeling, we propose a
two-step neural framework based on RL to learn 3D modeling policies. By taking
actions and collecting rewards in an interactive environment, the agents first
learn to parse a target shape into primitives and then to edit the geometry. To
effectively train the modeling agents, we introduce a novel training algorithm
that combines heuristic policy, imitation learning and reinforcement learning.
Our experiments show that the agents can learn good policies to produce regular
and structure-aware mesh models, which demonstrates the feasibility and
effectiveness of the proposed RL framework. | [
"cs.CV"
] |
Cost volume is an essential component of recent deep models for optical flow
estimation and is usually constructed by calculating the inner product between
two feature vectors. However, the standard inner product in the commonly-used
cost volume may limit the representation capacity of flow models because it
neglects the correlation among different channel dimensions and weighs each
dimension equally. To address this issue, we propose a learnable cost volume
(LCV) using an elliptical inner product, which generalizes the standard inner
product by a positive definite kernel matrix. To guarantee its positive
definiteness, we perform spectral decomposition on the kernel matrix and
re-parameterize it via the Cayley representation. The proposed LCV is a
lightweight module and can be easily plugged into existing models to replace
the vanilla cost volume. Experimental results show that the LCV module not only
improves the accuracy of state-of-the-art models on standard benchmarks, but
also promotes their robustness against illumination change, noises, and
adversarial perturbations of the input signals. | [
"cs.CV"
] |
While deep reinforcement learning (RL) promises freedom from hand-labeled
data, great successes, especially for Embodied AI, require significant work to
create supervision via carefully shaped rewards. Indeed, without shaped
rewards, i.e., with only terminal rewards, present-day Embodied AI results
degrade significantly across Embodied AI problems from single-agent
Habitat-based PointGoal Navigation (SPL drops from 55 to 0) and two-agent
AI2-THOR-based Furniture Moving (success drops from 58% to 1%) to three-agent
Google Football-based 3 vs. 1 with Keeper (game score drops from 0.6 to 0.1).
As training from shaped rewards doesn't scale to more realistic tasks, the
community needs to improve the success of training with terminal rewards. For
this we propose GridToPix: 1) train agents with terminal rewards in gridworlds
that generically mirror Embodied AI environments, i.e., they are independent of
the task; 2) distill the learned policy into agents that reside in complex
visual worlds. Despite learning from only terminal rewards with identical
models and RL algorithms, GridToPix significantly improves results across
tasks: from PointGoal Navigation (SPL improves from 0 to 64) and Furniture
Moving (success improves from 1% to 25%) to football gameplay (game score
improves from 0.1 to 0.6). GridToPix even helps to improve the results of
shaped reward training. | [
"cs.CV",
"cs.AI",
"cs.LG",
"cs.MA"
] |
Deep learning based models have had great success in object detection, but
the state of the art models have not yet been widely applied to biological
image data. We apply for the first time an object detection model previously
used on natural images to identify cells and recognize their stages in
brightfield microscopy images of malaria-infected blood. Many micro-organisms
like malaria parasites are still studied by expert manual inspection and hand
counting. This type of object detection task is challenging due to factors like
variations in cell shape, density, and color, and uncertainty of some cell
classes. In addition, annotated data useful for training is scarce, and the
class distribution is inherently highly imbalanced due to the dominance of
uninfected red blood cells. We use Faster Region-based Convolutional Neural
Network (Faster R-CNN), one of the top performing object detection models in
recent years, pre-trained on ImageNet but fine tuned with our data, and compare
it to a baseline, which is based on a traditional approach consisting of cell
segmentation, extraction of several single-cell features, and classification
using random forests. To conduct our initial study, we collect and label a
dataset of 1300 fields of view consisting of around 100,000 individual cells.
We demonstrate that Faster R-CNN outperforms our baseline and put the results
in context of human performance. | [
"cs.CV"
] |
The COVID-19 pandemic has caused many shutdowns in different industries
around the world. Sectors such as infrastructure construction and maintenance
projects have not been suspended due to their significant effect on people's
routine life. In such projects, workers work close together that makes a high
risk of infection. The World Health Organization recommends wearing a face mask
and practicing physical distancing to mitigate the virus's spread. This paper
developed a computer vision system to automatically detect the violation of
face mask wearing and physical distancing among construction workers to assure
their safety on infrastructure projects during the pandemic. For the face mask
detection, the paper collected and annotated 1,000 images, including different
types of face mask wearing, and added them to a pre-existing face mask dataset
to develop a dataset of 1,853 images. Then trained and tested multiple
Tensorflow state-of-the-art object detection models on the face mask dataset
and chose the Faster R-CNN Inception ResNet V2 network that yielded the
accuracy of 99.8%. For physical distance detection, the paper employed the
Faster R-CNN Inception V2 to detect people. A transformation matrix was used to
eliminate the camera angle's effect on the object distances on the image. The
Euclidian distance used the pixels of the transformed image to compute the
actual distance between people. A threshold of six feet was considered to
capture physical distance violation. The paper also used transfer learning for
training the model. The final model was applied on four videos of road
maintenance projects in Houston, TX, that effectively detected the face mask
and physical distance. We recommend that construction owners use the proposed
system to enhance construction workers' safety in the pandemic situation. | [
"cs.CV",
"cs.CY"
] |
A 3D point cloud describes the real scene precisely and intuitively.To date
how to segment diversified elements in such an informative 3D scene is rarely
discussed. In this paper, we first introduce a simple and flexible framework to
segment instances and semantics in point clouds simultaneously. Then, we
propose two approaches which make the two tasks take advantage of each other,
leading to a win-win situation. Specifically, we make instance segmentation
benefit from semantic segmentation through learning semantic-aware point-level
instance embedding. Meanwhile, semantic features of the points belonging to the
same instance are fused together to make more accurate per-point semantic
predictions. Our method largely outperforms the state-of-the-art method in 3D
instance segmentation along with a significant improvement in 3D semantic
segmentation. Code has been made available at:
https://github.com/WXinlong/ASIS. | [
"cs.CV"
] |
Capsule neural network is a new and popular technique in deep learning.
However, the traditional capsule neural network does not extract features
sufficiently before the dynamic routing between the capsules. In this paper,
the one Double Enhanced Capsule Neural Network (E2-Capsnet) that uses AU-aware
attention for facial expression recognition (FER) is proposed. The E2-Capsnet
takes advantage of dynamic routing between the capsules, and has two
enhancement modules which are beneficial for FER. The first enhancement module
is the convolutional neural network with AU-aware attention, which can help
focus on the active areas of the expression. The second enhancement module is
the capsule neural network with multiple convolutional layers, which enhances
the ability of the feature representation. Finally, squashing function is used
to classify the facial expression. We demonstrate the effectiveness of
E2-Capsnet on the two public benchmark datasets, RAF-DB and EmotioNet. The
experimental results show that our E2-Capsnet is superior to the
state-of-the-art methods. Our implementation will be publicly available online. | [
"cs.CV"
] |
Natural language processing has improved tremendously after the success of
word embedding techniques such as word2vec. Recently, the same idea has been
applied on source code with encouraging results. In this survey, we aim to
collect and discuss the usage of word embedding techniques on programs and
source code. The articles in this survey have been collected by asking authors
of related work and with an extensive search on Google Scholar. Each article is
categorized into five categories: 1. embedding of tokens 2. embedding of
functions or methods 3. embedding of sequences or sets of method calls 4.
embedding of binary code 5. other embeddings. We also provide links to
experimental data and show some remarkable visualization of code embeddings. In
summary, word embedding has been successfully applied on different
granularities of source code. With access to countless open-source
repositories, we see a great potential of applying other data-driven natural
language processing techniques on source code in the future. | [
"cs.LG",
"cs.PL",
"cs.SE",
"stat.ML"
] |
Pathologists find tedious to examine the status of the sentinel lymph node on
a large number of pathological scans. The examination process of such lymph
node which encompasses metastasized cancer cells is histopathologically
organized. However, the task of finding metastatic tissues is gradual which is
often challenging. In this work, we present our deep convolutional neural
network based model validated on PatchCamelyon (PCam) benchmark dataset for
fundamental machine learning research in histopathology diagnosis. We find that
our proposed model trained with a semi-supervised learning approach by using
pseudo labels on PCam-level significantly leads to better performances to
strong CNN baseline on the AUC metric. | [
"cs.CV",
"cs.AI"
] |