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This paper addresses the issue on how to more effectively coordinate the
depth with RGB aiming at boosting the performance of RGB-D object detection.
Particularly, we investigate two primary ideas under the CNN model: property
derivation and property fusion. Firstly, we propose that the depth can be
utilized not only as a type of extra information besides RGB but also to derive
more visual properties for comprehensively describing the objects of interest.
So a two-stage learning framework consisting of property derivation and fusion
is constructed. Here the properties can be derived either from the provided
color/depth or their pairs (e.g. the geometry contour adopted in this paper).
Secondly, we explore the fusion method of different properties in feature
learning, which is boiled down to, under the CNN model, from which layer the
properties should be fused together. The analysis shows that different semantic
properties should be learned separately and combined before passing into the
final classifier. Actually, such a detection way is in accordance with the
mechanism of the primary neural cortex (V1) in brain. We experimentally
evaluate the proposed method on the challenging dataset, and have achieved
state-of-the-art performance. | [
"cs.CV"
] |
Model compression has been widely adopted to obtain light-weighted deep
neural networks. Most prevalent methods, however, require fine-tuning with
sufficient training data to ensure accuracy, which could be challenged by
privacy and security issues. As a compromise between privacy and performance,
in this paper we investigate few shot network compression: given few samples
per class, how can we effectively compress the network with negligible
performance drop? The core challenge of few shot network compression lies in
high estimation errors from the original network during inference, since the
compressed network can easily over-fits on the few training instances. The
estimation errors could propagate and accumulate layer-wisely and finally
deteriorate the network output. To address the problem, we propose cross
distillation, a novel layer-wise knowledge distillation approach. By
interweaving hidden layers of teacher and student network, layer-wisely
accumulated estimation errors can be effectively reduced.The proposed method
offers a general framework compatible with prevalent network compression
techniques such as pruning. Extensive experiments on benchmark datasets
demonstrate that cross distillation can significantly improve the student
network's accuracy when only a few training instances are available. | [
"cs.LG",
"cs.CV",
"stat.ML"
] |
We develop an effective generation of adversarial attacks on neural models
that output a sequence of probability distributions rather than a sequence of
single values. This setting includes the recently proposed deep probabilistic
autoregressive forecasting models that estimate the probability distribution of
a time series given its past and achieve state-of-the-art results in a diverse
set of application domains. The key technical challenge we address is
effectively differentiating through the Monte-Carlo estimation of statistics of
the joint distribution of the output sequence. Additionally, we extend prior
work on probabilistic forecasting to the Bayesian setting which allows
conditioning on future observations, instead of only on past observations. We
demonstrate that our approach can successfully generate attacks with small
input perturbations in two challenging tasks where robust decision making is
crucial: stock market trading and prediction of electricity consumption. | [
"cs.LG",
"stat.ML"
] |
Inspired by human vision, we propose a new periphery-fovea multi-resolution
driving model that predicts vehicle speed from dash camera videos. The
peripheral vision module of the model processes the full video frames in low
resolution. Its foveal vision module selects sub-regions and uses
high-resolution input from those regions to improve its driving performance. We
train the fovea selection module with supervision from driver gaze. We show
that adding high-resolution input from predicted human driver gaze locations
significantly improves the driving accuracy of the model. Our periphery-fovea
multi-resolution model outperforms a uni-resolution periphery-only model that
has the same amount of floating-point operations. More importantly, we
demonstrate that our driving model achieves a significantly higher performance
gain in pedestrian-involved critical situations than in other non-critical
situations. | [
"cs.CV"
] |
While the advent of Graph Neural Networks (GNNs) has greatly improved node
and graph representation learning in many applications, the neighborhood
aggregation scheme exposes additional vulnerabilities to adversaries seeking to
extract node-level information about sensitive attributes. In this paper, we
study the problem of protecting sensitive attributes by information obfuscation
when learning with graph structured data. We propose a framework to locally
filter out pre-determined sensitive attributes via adversarial training with
the total variation and the Wasserstein distance. Our method creates a strong
defense against inference attacks, while only suffering small loss in task
performance. Theoretically, we analyze the effectiveness of our framework
against a worst-case adversary, and characterize an inherent trade-off between
maximizing predictive accuracy and minimizing information leakage. Experiments
across multiple datasets from recommender systems, knowledge graphs and quantum
chemistry demonstrate that the proposed approach provides a robust defense
across various graph structures and tasks, while producing competitive GNN
encoders for downstream tasks. | [
"cs.LG",
"cs.AI",
"cs.CV",
"stat.ML"
] |
Reinforcement learning typically assumes that the state update from the
previous actions happens instantaneously, and thus can be used for making
future decisions. However, this may not always be true. When the state update
is not available, the decision taken is partly in the blind since it cannot
rely on the current state information. This paper proposes an approach, where
the delay in the knowledge of the state can be used, and the decisions are made
based on the available information which may not include the current state
information. One approach could be to include the actions after the last-known
state as a part of the state information, however, that leads to an increased
state-space making the problem complex and slower in convergence. The proposed
algorithm gives an alternate approach where the state space is not enlarged, as
compared to the case when there is no delay in the state update. Evaluations on
the basic RL environments further illustrate the improved performance of the
proposed algorithm. | [
"cs.LG"
] |
The present work shows the application of transfer learning for a pre-trained
deep neural network (DNN), using a small image dataset ($\approx$ 12,000) on a
single workstation with enabled NVIDIA GPU card that takes up to 1 hour to
complete the training task and archive an overall average accuracy of $94.7\%$.
The DNN presents a $20\%$ score of misclassification for an external test
dataset. The accuracy of the proposed methodology is equivalent to ones using
HSI methodology $(81\%-91\%)$ used for the same task, but with the advantage of
being independent on special equipment to classify wheat kernel for FHB
symptoms. | [
"cs.LG",
"stat.ML"
] |
The last decade has witnessed an experimental revolution in data science and
machine learning, epitomised by deep learning methods. Indeed, many
high-dimensional learning tasks previously thought to be beyond reach -- such
as computer vision, playing Go, or protein folding -- are in fact feasible with
appropriate computational scale. Remarkably, the essence of deep learning is
built from two simple algorithmic principles: first, the notion of
representation or feature learning, whereby adapted, often hierarchical,
features capture the appropriate notion of regularity for each task, and
second, learning by local gradient-descent type methods, typically implemented
as backpropagation.
While learning generic functions in high dimensions is a cursed estimation
problem, most tasks of interest are not generic, and come with essential
pre-defined regularities arising from the underlying low-dimensionality and
structure of the physical world. This text is concerned with exposing these
regularities through unified geometric principles that can be applied
throughout a wide spectrum of applications.
Such a 'geometric unification' endeavour, in the spirit of Felix Klein's
Erlangen Program, serves a dual purpose: on one hand, it provides a common
mathematical framework to study the most successful neural network
architectures, such as CNNs, RNNs, GNNs, and Transformers. On the other hand,
it gives a constructive procedure to incorporate prior physical knowledge into
neural architectures and provide principled way to build future architectures
yet to be invented. | [
"cs.LG",
"cs.AI",
"cs.CG",
"cs.CV",
"stat.ML"
] |
In this paper, the aim is multi-illumination color constancy. However, most
of the existing color constancy methods are designed for single light sources.
Furthermore, datasets for learning multiple illumination color constancy are
largely missing. We propose a seed (physics driven) based multi-illumination
color constancy method. GANs are exploited to model the illumination estimation
problem as an image-to-image domain translation problem. Additionally, a novel
multi-illumination data augmentation method is proposed. Experiments on single
and multi-illumination datasets show that our methods outperform sota methods. | [
"cs.CV"
] |
The use of semantic segmentation for masking and cropping input images has
proven to be a significant aid in medical imaging classification tasks by
decreasing the noise and variance of the training dataset. However,
implementing this approach with classical methods is challenging: the cost of
obtaining a dense segmentation is high, and the precise input area that is most
crucial to the classification task is difficult to determine a-priori. We
propose a novel joint-training deep reinforcement learning framework for image
augmentation. A segmentation network, weakly supervised with policy gradient
optimization, acts as an agent, and outputs masks as actions given samples as
states, with the goal of maximizing reward signals from the classification
network. In this way, the segmentation network learns to mask unimportant
imaging features. Our method, Adversarial Policy Gradient Augmentation (APGA),
shows promising results on Stanford's MURA dataset and on a hip fracture
classification task with an increase in global accuracy of up to 7.33% and
improved performance over baseline methods in 9/10 tasks evaluated. We discuss
the broad applicability of our joint training strategy to a variety of medical
imaging tasks. | [
"cs.CV",
"cs.LG"
] |
Deep learning based LiDAR odometry (LO) estimation attracts increasing
research interests in the field of autonomous driving and robotics. Existing
works feed consecutive LiDAR frames into neural networks as point clouds and
match pairs in the learned feature space. In contrast, motivated by the success
of image based feature extractors, we propose to transfer the LiDAR frames to
image space and reformulate the problem as image feature extraction. With the
help of scale-invariant feature transform (SIFT) for feature extraction, we are
able to generate matched keypoint pairs (MKPs) that can be precisely returned
to the 3D space. A convolutional neural network pipeline is designed for LiDAR
odometry estimation by extracted MKPs. The proposed scheme, namely LodoNet, is
then evaluated in the KITTI odometry estimation benchmark, achieving on par
with or even better results than the state-of-the-art. | [
"cs.CV",
"I.5.4"
] |
Spatially-varying intensity noise is a common source of distortion in medical
images. Bias field noise is one example of such a distortion that is often
present in the magnetic resonance (MR) images or other modalities such as
retina images. In this paper, we first show that the bias field noise can be
considerably reduced using Empirical Mode Decomposition (EMD) technique. EMD is
a multi-resolution tool that decomposes a signal into several principle
patterns and residual components. We show that the spatially-varying noise is
highly expressed in the residual component of the EMD and could be filtered
out. Then, we propose two hierarchical multi-resolution EMD-based algorithms
for robust registration of images in the presence of spatially varying noise.
One algorithm (LR-EMD) is based on registration of EMD feature-maps from both
floating and reference images in various resolution levels. In the second
algorithm (AFR-EMD), we first extract an average feature-map based on EMD from
both floating and reference images. Then, we use a simple hierarchical
multi-resolution algorithm to register the average feature-maps. For the brain
MR images, both algorithms achieve lower error rate and higher convergence
percentage compared to the intensity-based hierarchical registration.
Specifically, using mutual information as the similarity measure, AFR-EMD
achieves 42% lower error rate in intensity and 52% lower error rate in
transformation compared to intensity-based hierarchical registration. For
LR-EMD, the error rate is 32% lower for the intensity and 41% lower for the
transformation. Furthermore, we demonstrate that our proposed algorithms
improve the registration of retina images in the presence of spatially varying
noise. | [
"cs.CV"
] |
Single image super resolution (SR) has seen major performance leaps in recent
years. However, existing methods do not allow exploring the infinitely many
plausible reconstructions that might have given rise to the observed
low-resolution (LR) image. These different explanations to the LR image may
dramatically vary in their textures and fine details, and may often encode
completely different semantic information. In this paper, we introduce the task
of explorable super resolution. We propose a framework comprising a graphical
user interface with a neural network backend, allowing editing the SR output so
as to explore the abundance of plausible HR explanations to the LR input. At
the heart of our method is a novel module that can wrap any existing SR
network, analytically guaranteeing that its SR outputs would precisely match
the LR input, when downsampled. Besides its importance in our setting, this
module is guaranteed to decrease the reconstruction error of any SR network it
wraps, and can be used to cope with blur kernels that are different from the
one the network was trained for. We illustrate our approach in a variety of use
cases, ranging from medical imaging and forensics, to graphics. | [
"cs.CV"
] |
In this paper, we contrive a stereo matching algorithm with careful handling
of disparity, discontinuity and occlusion. This algorithm works a worldwide
matching stereo model which is based on minimization of energy. The global
energy comprises two terms, firstly the data term and secondly the smoothness
term. The data term is approximated by a color-weighted correlation, then
refined in obstruct and low-texture areas in many applications of hierarchical
loopy belief propagation algorithm. The results during the experiment are
evaluated on the Middlebury data sets, showing that out algorithm is the top
performer among all the algorithms listed there | [
"cs.CV"
] |
Self attention mechanisms have become a key building block in many
state-of-the-art language understanding models. In this paper, we show that the
self attention operator can be formulated in terms of 1x1 convolution
operations. Following this observation, we propose several novel operators:
First, we introduce a 2D version of self attention that is applicable for 2D
signals such as images. Second, we present the 1D and 2D Self Attentive
Convolutions (SAC) operator that generalizes self attention beyond 1x1
convolutions to 1xm and nxm convolutions, respectively. While 1D and 2D self
attention operate on individual words and pixels, SAC operates on m-grams and
image patches, respectively. Third, we present a multiscale version of SAC
(MSAC) which analyzes the input by employing multiple SAC operators that vary
by filter size, in parallel. Finally, we explain how MSAC can be utilized for
vision and language modeling, and further harness MSAC to form a cross
attentive image similarity machinery. | [
"cs.LG",
"cs.CL",
"cs.CV",
"stat.ML"
] |
Faults are endemic to all systems. Adaptive fault-tolerant control maintains
degraded performance when faults occur as opposed to unsafe conditions or
catastrophic events. In systems with abrupt faults and strict time constraints,
it is imperative for control to adapt quickly to system changes to maintain
system operations. We present a meta-reinforcement learning approach that
quickly adapts its control policy to changing conditions. The approach builds
upon model-agnostic meta learning (MAML). The controller maintains a complement
of prior policies learned under system faults. This "library" is evaluated on a
system after a new fault to initialize the new policy. This contrasts with
MAML, where the controller derives intermediate policies anew, sampled from a
distribution of similar systems, to initialize a new policy. Our approach
improves sample efficiency of the reinforcement learning process. We evaluate
our approach on an aircraft fuel transfer system under abrupt faults. | [
"cs.LG",
"cs.SY",
"eess.SY",
"stat.ML"
] |
Explaining a deep learning model can help users understand its behavior and
allow researchers to discern its shortcomings. Recent work has primarily
focused on explaining models for tasks like image classification or visual
question answering. In this paper, we introduce Salient Attributes for Network
Explanation (SANE) to explain image similarity models, where a model's output
is a score measuring the similarity of two inputs rather than a classification
score. In this task, an explanation depends on both of the input images, so
standard methods do not apply. Our SANE explanations pairs a saliency map
identifying important image regions with an attribute that best explains the
match. We find that our explanations provide additional information not
typically captured by saliency maps alone, and can also improve performance on
the classic task of attribute recognition. Our approach's ability to generalize
is demonstrated on two datasets from diverse domains, Polyvore Outfits and
Animals with Attributes 2. Code available at:
https://github.com/VisionLearningGroup/SANE | [
"cs.CV"
] |
Directly learning features from the point cloud has become an active research
direction in 3D understanding. Existing learning-based methods usually
construct local regions from the point cloud and extract the corresponding
features. However, most of these processes do not adequately take the spatial
distribution of the point cloud into account, limiting the ability to perceive
fine-grained patterns. We design a novel Local Spatial Aware (LSA) layer, which
can learn to generate Spatial Distribution Weights (SDWs) hierarchically based
on the spatial relationship in local region for spatial independent operations,
to establish the relationship between these operations and spatial
distribution, thus capturing the local geometric structure sensitively.We
further propose the LSANet, which is based on LSA layer, aggregating the
spatial information with associated features in each layer of the network
better in network design.The experiments show that our LSANet can achieve on
par or better performance than the state-of-the-art methods when evaluating on
the challenging benchmark datasets. For example, our LSANet can achieve 93.2%
accuracy on ModelNet40 dataset using only 1024 points, significantly higher
than other methods under the same conditions. The source code is available at
https://github.com/LinZhuoChen/LSANet. | [
"cs.CV"
] |
Over the past few years, deep neural networks (DNNs) have garnered remarkable
success in a diverse range of real-world applications. However, DNNs consider a
large number of inputs and consist of a large number of parameters, resulting
in high computational demand. We study the human somatosensory system and
propose the SpinalNet to achieve higher accuracy with less computational
resources. In a typical neural network (NN) architecture, the hidden layers
receive inputs in the first layer and then transfer the intermediate outcomes
to the next layer. In the proposed SpinalNet, the structure of hidden layers
allocates to three sectors: 1) Input row, 2) Intermediate row, and 3) output
row. The intermediate row of the SpinalNet contains a few neurons. The role of
input segmentation is in enabling each hidden layer to receive a part of the
inputs and outputs of the previous layer. Therefore, the number of incoming
weights in a hidden layer is significantly lower than traditional DNNs. As all
layers of the SpinalNet directly contributes to the output row, the vanishing
gradient problem does not exist. We also investigate the SpinalNet
fully-connected layer to several well-known DNN models and perform traditional
learning and transfer learning. We observe significant error reductions with
lower computational costs in most of the DNNs. We have also obtained the
state-of-the-art (SOTA) performance for QMNIST, Kuzushiji-MNIST, EMNIST
(Letters, Digits, and Balanced), STL-10, Bird225, Fruits 360, and Caltech-101
datasets. The scripts of the proposed SpinalNet are available with the
following link: https://github.com/dipuk0506/SpinalNet | [
"cs.CV",
"cs.LG",
"cs.NE",
"eess.IV"
] |
Infant motion analysis is a topic with critical importance in early childhood
development studies. However, while the applications of human pose estimation
have become more and more broad, models trained on large-scale adult pose
datasets are barely successful in estimating infant poses due to the
significant differences in their body ratio and the versatility of their poses.
Moreover, the privacy and security considerations hinder the availability of
adequate infant pose data required for training of a robust model from scratch.
To address this problem, this paper presents (1) building and publicly
releasing a hybrid synthetic and real infant pose (SyRIP) dataset with small
yet diverse real infant images as well as generated synthetic infant poses and
(2) a multi-stage invariant representation learning strategy that could
transfer the knowledge from the adjacent domains of adult poses and synthetic
infant images into our fine-tuned domain-adapted infant pose (FiDIP) estimation
model. In our ablation study, with identical network structure, models trained
on SyRIP dataset show noticeable improvement over the ones trained on the only
other public infant pose datasets. Integrated with pose estimation backbone
networks with varying complexity, FiDIP performs consistently better than the
fine-tuned versions of those models. One of our best infant pose estimation
performers on the state-of-the-art DarkPose model shows mean average precision
(mAP) of 93.6. | [
"cs.CV"
] |
Image generation has been heavily investigated in computer vision, where one
core research challenge is to generate images from arbitrarily complex
distributions with little supervision. Generative Adversarial Networks (GANs)
as an implicit approach have achieved great successes in this direction and
therefore been employed widely. However, GANs are known to suffer from issues
such as mode collapse, non-structured latent space, being unable to compute
likelihoods, etc. In this paper, we propose a new unsupervised non-parametric
method named mixture of infinite conditional GANs or MIC-GANs, to tackle
several GAN issues together, aiming for image generation with parsimonious
prior knowledge. Through comprehensive evaluations across different datasets,
we show that MIC-GANs are effective in structuring the latent space and
avoiding mode collapse, and outperform state-of-the-art methods. MICGANs are
adaptive, versatile, and robust. They offer a promising solution to several
well-known GAN issues. Code available: github.com/yinghdb/MICGANs. | [
"cs.CV"
] |
Sensors are an integral part of modern Internet of Things (IoT) applications.
There is a critical need for the analysis of heterogeneous multivariate
temporal data obtained from the individual sensors of these systems. In this
paper we particularly focus on the problem of the scarce amount of training
data available per sensor. We propose a novel federated multi-task hierarchical
attention model (FATHOM) that jointly trains classification/regression models
from multiple sensors. The attention mechanism of the proposed model seeks to
extract feature representations from the input and learn a shared
representation focused on time dimensions across multiple sensors. The
underlying temporal and non-linear relationships are modeled using a
combination of attention mechanism and long-short term memory (LSTM) networks.
We find that our proposed method outperforms a wide range of competitive
baselines in both classification and regression settings on activity
recognition and environment monitoring datasets. We further provide
visualization of feature representations learned by our model at the input
sensor level and central time level. | [
"cs.LG",
"stat.ML"
] |
Vision transformer has demonstrated promising performance on challenging
computer vision tasks. However, directly training the vision transformers may
yield unstable and sub-optimal results. Recent works propose to improve the
performance of the vision transformers by modifying the transformer structures,
e.g., incorporating convolution layers. In contrast, we investigate an
orthogonal approach to stabilize the vision transformer training without
modifying the networks. We observe the instability of the training can be
attributed to the significant similarity across the extracted patch
representations. More specifically, for deep vision transformers, the
self-attention blocks tend to map different patches into similar latent
representations, yielding information loss and performance degradation. To
alleviate this problem, in this work, we introduce novel loss functions in
vision transformer training to explicitly encourage diversity across patch
representations for more discriminative feature extraction. We empirically show
that our proposed techniques stabilize the training and allow us to train wider
and deeper vision transformers. We further show the diversified features
significantly benefit the downstream tasks in transfer learning. For semantic
segmentation, we enhance the state-of-the-art (SOTA) results on Cityscapes and
ADE20k. Our code is available at
https://github.com/ChengyueGongR/PatchVisionTransformer. | [
"cs.CV",
"cs.LG"
] |
Machine learning algorithms typically require abundant data under a
stationary environment. However, environments are nonstationary in many
real-world applications. Critical issues lie in how to effectively adapt models
under an ever-changing environment. We propose a method for transferring
knowledge from a source domain to a target domain via $\ell_1$ regularization.
We incorporate $\ell_1$ regularization of differences between source parameters
and target parameters, in addition to an ordinary $\ell_1$ regularization.
Hence, our method yields sparsity for both the estimates themselves and changes
of the estimates. The proposed method has a tight estimation error bound under
a stationary environment, and the estimate remains unchanged from the source
estimate under small residuals. Moreover, the estimate is consistent with the
underlying function, even when the source estimate is mistaken due to
nonstationarity. Empirical results demonstrate that the proposed method
effectively balances stability and plasticity. | [
"stat.ML",
"cs.LG"
] |
Multiview representation learning is very popular for latent factor analysis.
It naturally arises in many data analysis, machine learning, and information
retrieval applications to model dependent structures among multiple data
sources. For computational convenience, existing approaches usually formulate
the multiview representation learning as convex optimization problems, where
global optima can be obtained by certain algorithms in polynomial time.
However, many pieces of evidence have corroborated that heuristic nonconvex
approaches also have good empirical computational performance and convergence
to the global optima, although there is a lack of theoretical justification.
Such a gap between theory and practice motivates us to study a nonconvex
formulation for multiview representation learning, which can be efficiently
solved by a simple stochastic gradient descent (SGD) algorithm. We first
illustrate the geometry of the nonconvex formulation; Then, we establish
asymptotic global rates of convergence to the global optima by diffusion
approximations. Numerical experiments are provided to support our theory. | [
"cs.LG",
"math.OC",
"stat.ML"
] |
Image generation has raised tremendous attention in both academic and
industrial areas, especially for the conditional and target-oriented image
generation, such as criminal portrait and fashion design. Although the current
studies have achieved preliminary results along this direction, they always
focus on class labels as the condition where spatial contents are randomly
generated from latent vectors. Edge details are usually blurred since spatial
information is difficult to preserve. In light of this, we propose a novel
Spatially Constrained Generative Adversarial Network (SCGAN), which decouples
the spatial constraints from the latent vector and makes these constraints
feasible as additional controllable signals. To enhance the spatial
controllability, a generator network is specially designed to take a semantic
segmentation, a latent vector and an attribute-level label as inputs step by
step. Besides, a segmentor network is constructed to impose spatial constraints
on the generator. Experimentally, we provide both visual and quantitative
results on CelebA and DeepFashion datasets, and demonstrate that the proposed
SCGAN is very effective in controlling the spatial contents as well as
generating high-quality images. | [
"cs.CV"
] |
With widespread adoption of AI models for important decision making, ensuring
reliability of such models remains an important challenge. In this paper, we
present an end-to-end generic framework for testing AI Models which performs
automated test generation for different modalities such as text, tabular, and
time-series data and across various properties such as accuracy, fairness, and
robustness. Our tool has been used for testing industrial AI models and was
very effective to uncover issues present in those models. Demo video link:
https://youtu.be/984UCU17YZI | [
"cs.LG",
"cs.AI"
] |
In today's world, Neural Style Transfer (NST) has become a trendsetting term.
NST combines two pictures, a content picture and a reference image in style
(such as the work of a renowned painter) in a way that makes the output image
look like an image of the material, but rendered with the form of a reference
picture. However, there is no study using the artwork or painting of
Bangladeshi painters. Bangladeshi painting has a long history of more than two
thousand years and is still being practiced by Bangladeshi painters. This study
generates NST stylized image on Bangladeshi paintings and analyzes the human
point of view regarding the aesthetic preference of NST on Bangladeshi
paintings. To assure our study's acceptance, we performed qualitative human
evaluations on generated stylized images by 60 individual humans of different
age and gender groups. We have explained how NST works for Bangladeshi
paintings and assess NST algorithms, both qualitatively \& quantitatively. Our
study acts as a pre-requisite for the impact of NST stylized image using
Bangladeshi paintings on mobile UI/GUI and material translation from the human
perspective. We hope that this study will encourage new collaborations to
create more NST related studies and expand the use of Bangladeshi artworks. | [
"cs.CV",
"cs.AI"
] |
Stereo matching is one of the widely used techniques for inferring depth from
stereo images owing to its robustness and speed. It has become one of the major
topics of research since it finds its applications in autonomous driving,
robotic navigation, 3D reconstruction, and many other fields. Finding pixel
correspondences in non-textured, occluded and reflective areas is the major
challenge in stereo matching. Recent developments have shown that semantic cues
from image segmentation can be used to improve the results of stereo matching.
Many deep neural network architectures have been proposed to leverage the
advantages of semantic segmentation in stereo matching. This paper aims to give
a comparison among the state of art networks both in terms of accuracy and in
terms of speed which are of higher importance in real-time applications. | [
"cs.CV",
"cs.LG"
] |
Incorporating various modes of information into the machine learning
procedure is becoming a new trend. And data from various source can provide
more information than single one no matter they are heterogeneous or
homogeneous. Existing deep learning based algorithms usually directly
concatenate features from each domain to represent the input data. Seldom of
them take the quality of data into consideration which is a key issue in
related multimodal problems. In this paper, we propose an efficient
quality-aware deep neural network to model the weight of data from each domain
using deep reinforcement learning (DRL). Specifically, we take the weighting of
each domain as a decision-making problem and teach an agent learn to interact
with the environment. The agent can tune the weight of each domain through
discrete action selection and obtain a positive reward if the saliency results
are improved. The target of the agent is to achieve maximum rewards after
finished its sequential action selection. We validate the proposed algorithms
on multimodal saliency detection in a coarse-to-fine way. The coarse saliency
maps are generated from an encoder-decoder framework which is trained with
content loss and adversarial loss. The final results can be obtained via
adaptive weighting of maps from each domain. Experiments conducted on two kinds
of salient object detection benchmarks validated the effectiveness of our
proposed quality-aware deep neural network. | [
"cs.CV"
] |
The growing number of dimensionality reduction methods available for data
visualization has recently inspired the development of quality assessment
measures, in order to evaluate the resulting low-dimensional representation
independently from a methods' inherent criteria. Several (existing) quality
measures can be (re)formulated based on the so-called co-ranking matrix, which
subsumes all rank errors (i.e. differences between the ranking of distances
from every point to all others, comparing the low-dimensional representation to
the original data). The measures are often based on the partioning of the
co-ranking matrix into 4 submatrices, divided at the K-th row and column,
calculating a weighted combination of the sums of each submatrix. Hence, the
evaluation process typically involves plotting a graph over several (or even
all possible) settings of the parameter K. Considering simple artificial
examples, we argue that this parameter controls two notions at once, that need
not necessarily be combined, and that the rectangular shape of submatrices is
disadvantageous for an intuitive interpretation of the parameter. We debate
that quality measures, as general and flexible evaluation tools, should have
parameters with a direct and intuitive interpretation as to which specific
error types are tolerated or penalized. Therefore, we propose to replace K with
two parameters to control these notions separately, and introduce a differently
shaped weighting on the co-ranking matrix. The two new parameters can then
directly be interpreted as a threshold up to which rank errors are tolerated,
and a threshold up to which the rank-distances are significant for the
evaluation. Moreover, we propose a color representation of local quality to
visually support the evaluation process for a given mapping, where every point
in the mapping is colored according to its local contribution to the overall
quality. | [
"cs.LG",
"cs.IR"
] |
Practical anomaly detection requires applying numerous approaches due to the
inherent difficulty of unsupervised learning. Direct comparison between complex
or opaque anomaly detection algorithms is intractable; we instead propose a
framework for associating the scores of multiple methods. Our aim is to answer
the question: how should one measure the similarity between anomaly scores
generated by different methods? The scoring crux is the extremes, which
identify the most anomalous observations. A pair of algorithms are defined here
to be similar if they assign their highest scores to roughly the same small
fraction of observations. To formalize this, we propose a measure based on
extremal similarity in scoring distributions through a novel upper quadrant
modeling approach, and contrast it with tail and other dependence measures. We
illustrate our method with simulated and real experiments, applying spectral
methods to cluster multiple anomaly detection methods and to contrast our
similarity measure with others. We demonstrate that our method is able to
detect the clusters of anomaly detection algorithms to achieve an accurate and
robust ensemble algorithm. | [
"stat.ML",
"cs.LG"
] |
Direct load control of a heterogeneous cluster of residential demand
flexibility sources is a high-dimensional control problem with partial
observability. This work proposes a novel approach that uses a convolutional
neural network to extract hidden state-time features to mitigate the curse of
partial observability. More specific, a convolutional neural network is used as
a function approximator to estimate the state-action value function or
Q-function in the supervised learning step of fitted Q-iteration. The approach
is evaluated in a qualitative simulation, comprising a cluster of
thermostatically controlled loads that only share their air temperature, whilst
their envelope temperature remains hidden. The simulation results show that the
presented approach is able to capture the underlying hidden features and
successfully reduce the electricity cost the cluster. | [
"cs.LG",
"cs.SY"
] |
Domain adaptation (DA) aims at improving the performance of a model on target
domains by transferring the knowledge contained in different but related source
domains. With recent advances in deep learning models which are extremely data
hungry, the interest for visual DA has significantly increased in the last
decade and the number of related work in the field exploded. The aim of this
paper, therefore, is to give a comprehensive overview of deep domain adaptation
methods for computer vision applications. First, we detail and compared
different possible ways of exploiting deep architectures for domain adaptation.
Then, we propose an overview of recent trends in deep visual DA. Finally, we
mention a few improvement strategies, orthogonal to these methods, that can be
applied to these models. While we mainly focus on image classification, we give
pointers to papers that extend these ideas for other applications such as
semantic segmentation, object detection, person re-identifications, and others. | [
"cs.CV"
] |
With superiorities on low cost, portability, and free of radiation,
echocardiogram is a widely used imaging modality for left ventricle (LV)
function quantification. However, automatic LV segmentation and motion tracking
is still a challenging task. In addition to fuzzy border definition, low
contrast, and abounding artifacts on typical ultrasound images, the shape and
size of the LV change significantly in a cardiac cycle. In this work, we
propose a temporal affine network (TAN) to perform image analysis in a warped
image space, where the shape and size variations due to the cardiac motion as
well as other artifacts are largely compensated. Furthermore, we perform three
frequent echocardiogram interpretation tasks simultaneously: standard cardiac
plane recognition, LV landmark detection, and LV segmentation. Instead of using
three networks with one dedicating to each task, we use a multi-task network to
perform three tasks simultaneously. Since three tasks share the same encoder,
the compact network improves the segmentation accuracy with more supervision.
The network is further finetuned with optical flow adjusted annotations to
enhance motion coherence in the segmentation result. Experiments on 1,714 2D
echocardiographic sequences demonstrate that the proposed method achieves
state-of-the-art segmentation accuracy with real-time efficiency. | [
"cs.CV"
] |
Recent network pruning methods focus on pruning models early-on in training.
To estimate the impact of removing a parameter, these methods use importance
measures that were originally designed to prune trained models. Despite lacking
justification for their use early-on in training, such measures result in
surprisingly low accuracy loss. To better explain this behavior, we develop a
general framework that uses gradient flow to unify state-of-the-art importance
measures through the norm of model parameters. We use this framework to
determine the relationship between pruning measures and evolution of model
parameters, establishing several results related to pruning models early-on in
training: (i) magnitude-based pruning removes parameters that contribute least
to reduction in loss, resulting in models that converge faster than
magnitude-agnostic methods; (ii) loss-preservation based pruning preserves
first-order model evolution dynamics and is therefore appropriate for pruning
minimally trained models; and (iii) gradient-norm based pruning affects
second-order model evolution dynamics, such that increasing gradient norm via
pruning can produce poorly performing models. We validate our claims on several
VGG-13, MobileNet-V1, and ResNet-56 models trained on CIFAR-10/CIFAR-100. Code
available at https://github.com/EkdeepSLubana/flowandprune. | [
"cs.LG",
"cs.CV",
"stat.ML"
] |
Visual Saliency is the capability of vision system to select distinctive
parts of scene and reduce the amount of visual data that need to be processed.
The presentpaper introduces (1) a novel approach to detect salient regions by
considering color and luminance based saliency scores using Dynamic Mode
Decomposition (DMD), (2) a new interpretation to use DMD approach in static
image processing. This approach integrates two data analysis methods: (1)
Fourier Transform, (2) Principle Component Analysis.The key idea of our work is
to create a color based saliency map. This is based on the observation
thatsalient part of an image usually have distinct colors compared to the
remaining portion of the image. We have exploited the power of different color
spaces to model the complex and nonlinear behavior of human visual system to
generate a color based saliency map. To further improve the effect of final
saliency map, weutilized luminance information exploiting the fact that human
eye is more sensitive towards brightness than color.The experimental results
shows that our method based on DMD theory is effective in comparison with
previous state-of-art saliency estimation approaches. The approach presented in
this paperis evaluated using ROC curve, F-measure rate, Precision-Recall rate,
AUC score etc. | [
"cs.CV"
] |
Decision forests (Forests), in particular random forests and gradient
boosting trees, have demonstrated state-of-the-art accuracy compared to other
methods in many supervised learning scenarios. In particular, Forests dominate
other methods in tabular data, that is, when the feature space is unstructured,
so that the signal is invariant to a permutation of the feature indices.
However, in structured data lying on a manifold (such as images, text, and
speech) deep networks (Networks), specifically convolutional deep networks
(ConvNets), tend to outperform Forests. We conjecture that at least part of the
reason for this is that the input to Networks is not simply the feature
magnitudes, but also their indices. In contrast, naive Forest implementations
fail to explicitly consider feature indices. A recently proposed Forest
approach demonstrates that Forests, for each node, implicitly sample a random
matrix from some specific distribution. These Forests, like some classes of
Networks, learn by partitioning the feature space into convex polytopes
corresponding to linear functions. We build on that approach and show that one
can choose distributions in a manifold-aware fashion to incorporate feature
locality. We demonstrate the empirical performance on data whose features live
on three different manifolds: a torus, images, and time-series. Moreover, we
demonstrate its strength in multivariate simulated settings and also show
superiority in predicting surgical outcome in epilepsy patients and predicting
movement direction from raw stereotactic EEG data from non-motor brain regions.
In all simulations and real data, Manifold Oblique Random Forest (MORF)
algorithm outperforms approaches that ignore feature space structure and
challenges the performance of ConvNets. Moreover, MORF runs fast and maintains
interpretability and theoretical justification. | [
"cs.LG",
"stat.ML",
"68T05"
] |
Parkinson's disease (PD) is a degenerative condition of the nervous system,
which manifests itself primarily as muscle stiffness, hypokinesia,
bradykinesia, and tremor. In patients suffering from advanced stages of PD,
Deep Brain Stimulation neurosurgery (DBS) is the best alternative to medical
treatment, especially when they become tolerant to the drugs. This surgery
produces a neuronal activity, a result from electrical stimulation, whose
quantification is known as Volume of Tissue Activated (VTA). To locate
correctly the VTA in the cerebral volume space, one should be aware exactly the
location of the tip of the DBS electrodes, as well as their spatial projection.
In this paper, we automatically locate DBS electrodes using a threshold-based
medical imaging segmentation methodology, determining the optimal value of this
threshold adaptively. The proposed methodology allows the localization of DBS
electrodes in Computed Tomography (CT) images, with high noise tolerance, using
automatic threshold detection methods. | [
"cs.CV",
"q-bio.NC"
] |
Due to the rapid increase in the diversity of image data, the problem of
domain generalization has received increased attention recently. While domain
generalization is a challenging problem, it has achieved great development
thanks to the fast development of AI techniques in computer vision. Most of
these advanced algorithms are proposed with deep architectures based on
convolution neural nets (CNN). However, though CNNs have a strong ability to
find the discriminative features, they do a poor job of modeling the relations
between different locations in the image due to the response to CNN filters are
mostly local. Since these local and global spatial relationships are
characterized to distinguish an object under consideration, they play a
critical role in improving the generalization ability against the domain gap.
In order to get the object parts relationships to gain better domain
generalization, this work proposes to use the self attention model. However,
the attention models are proposed for sequence, which are not expert in
discriminate feature extraction for 2D images. Considering this, we proposed a
hybrid architecture to discover the spatial relationships between these local
features, and derive a composite representation that encodes both the
discriminative features and their relationships to improve the domain
generalization. Evaluation on three well-known benchmarks demonstrates the
benefits of modeling relationships between the features of an image using the
proposed method and achieves state-of-the-art domain generalization
performance. More specifically, the proposed algorithm outperforms the
state-of-the-art by $2.2\%$ and $3.4\%$ on PACS and Office-Home databases,
respectively. | [
"cs.CV",
"cs.AI"
] |
LiDARs play a critical role in Autonomous Vehicles' (AVs) perception and
their safe operations. Recent works have demonstrated that it is possible to
spoof LiDAR return signals to elicit fake objects. In this work we demonstrate
how the same physical capabilities can be used to mount a new, even more
dangerous class of attacks, namely Object Removal Attacks (ORAs). ORAs aim to
force 3D object detectors to fail. We leverage the default setting of LiDARs
that record a single return signal per direction to perturb point clouds in the
region of interest (RoI) of 3D objects. By injecting illegitimate points behind
the target object, we effectively shift points away from the target objects'
RoIs. Our initial results using a simple random point selection strategy show
that the attack is effective in degrading the performance of commonly used 3D
object detection models. | [
"cs.CV",
"cs.CR",
"cs.LG"
] |
We introduce a flexible setup allowing for a neural network to learn both its
size and topology during the course of a standard gradient-based training. The
resulting network has the structure of a graph tailored to the particular
learning task and dataset. The obtained networks can also be trained from
scratch and achieve virtually identical performance. We explore the properties
of the network architectures for a number of datasets of varying difficulty
observing systematic regularities. The obtained graphs can be therefore
understood as encoding nontrivial characteristics of the particular
classification tasks. | [
"cs.LG",
"cs.CV",
"stat.ML"
] |
In this paper, we present a unified, end-to-end trainable spatiotemporal CNN
model for VOS, which consists of two branches, i.e., the temporal coherence
branch and the spatial segmentation branch. Specifically, the temporal
coherence branch pretrained in an adversarial fashion from unlabeled video
data, is designed to capture the dynamic appearance and motion cues of video
sequences to guide object segmentation. The spatial segmentation branch focuses
on segmenting objects accurately based on the learned appearance and motion
cues. To obtain accurate segmentation results, we design a coarse-to-fine
process to sequentially apply a designed attention module on multi-scale
feature maps, and concatenate them to produce the final prediction. In this
way, the spatial segmentation branch is enforced to gradually concentrate on
object regions. These two branches are jointly fine-tuned on video segmentation
sequences in an end-to-end manner. Several experiments are carried out on three
challenging datasets (i.e., DAVIS-2016, DAVIS-2017 and Youtube-Object) to show
that our method achieves favorable performance against the state-of-the-arts.
Code is available at https://github.com/longyin880815/STCNN. | [
"cs.CV"
] |
We introduce a novel fine-grained dataset and benchmark, the Danish Fungi
2020 (DF20). The dataset, constructed from observations submitted to the Atlas
of Danish Fungi, is unique in its taxonomy-accurate class labels, small number
of errors, highly unbalanced long-tailed class distribution, rich observation
metadata, and well-defined class hierarchy. DF20 has zero overlap with
ImageNet, allowing unbiased comparison of models fine-tuned from publicly
available ImageNet checkpoints. The proposed evaluation protocol enables
testing the ability to improve classification using metadata -- e.g. precise
geographic location, habitat, and substrate, facilitates classifier calibration
testing, and finally allows to study the impact of the device settings on the
classification performance. Experiments using Convolutional Neural Networks
(CNN) and the recent Vision Transformers (ViT) show that DF20 presents a
challenging task. Interestingly, ViT achieves results superior to CNN baselines
with 80.45% accuracy and 0.743 macro F1 score, reducing the CNN error by 9% and
12% respectively. A simple procedure for including metadata into the decision
process improves the classification accuracy by more than 2.95 percentage
points, reducing the error rate by 15%. The source code for all methods and
experiments is available at https://sites.google.com/view/danish-fungi-dataset. | [
"cs.CV",
"eess.IV"
] |
Recently, 3D deep learning models have been shown to be susceptible to
adversarial attacks like their 2D counterparts. Most of the state-of-the-art
(SOTA) 3D adversarial attacks perform perturbation to 3D point clouds. To
reproduce these attacks in pseudo physical scenario, a generated adversarial 3D
point cloud need to be reconstructed to mesh, which leads to a significant drop
in its adversarial effect. In this paper, we propose a strong 3D adversarial
attack named Mesh Attack to address this problem by directly performing
perturbation on mesh of a 3D object. Specifically, in each iteration of our
method, the mesh is first sampled to point cloud by a differentiable sample
module. Then a point cloud classifier is used to back-propagate a combined loss
to update the mesh vertices. The combined loss includes an adversarial loss to
mislead the point cloud classifier and three mesh losses to regularize the mesh
to be smooth. Extensive experiments demonstrate that the proposed scheme
outperforms SOTA 3D attacks by a significant margin in the pseudo physical
scenario. We also achieved SOTA performance under various defenses. Moreover,
to the best of our knowledge, our Mesh Attack is the first attempt of
adversarial attack on mesh classifier. Our code is available at:
{\footnotesize{\url{https://github.com/cuge1995/Mesh-Attack}}}. | [
"cs.CV"
] |
Bootstrapping provides a flexible and effective approach for assessing the
quality of batch reinforcement learning, yet its theoretical property is less
understood. In this paper, we study the use of bootstrapping in off-policy
evaluation (OPE), and in particular, we focus on the fitted Q-evaluation (FQE)
that is known to be minimax-optimal in the tabular and linear-model cases. We
propose a bootstrapping FQE method for inferring the distribution of the policy
evaluation error and show that this method is asymptotically efficient and
distributionally consistent for off-policy statistical inference. To overcome
the computation limit of bootstrapping, we further adapt a subsampling
procedure that improves the runtime by an order of magnitude. We numerically
evaluate the bootrapping method in classical RL environments for confidence
interval estimation, estimating the variance of off-policy evaluator, and
estimating the correlation between multiple off-policy evaluators. | [
"stat.ML",
"cs.LG",
"math.ST",
"stat.TH"
] |
The task of visual grounding requires locating the most relevant region or
object in an image, given a natural language query. So far, progress on this
task was mostly measured on curated datasets, which are not always
representative of human spoken language. In this work, we deviate from recent,
popular task settings and consider the problem under an autonomous vehicle
scenario. In particular, we consider a situation where passengers can give
free-form natural language commands to a vehicle which can be associated with
an object in the street scene. To stimulate research on this topic, we have
organized the \emph{Commands for Autonomous Vehicles} (C4AV) challenge based on
the recent \emph{Talk2Car} dataset (URL:
https://www.aicrowd.com/challenges/eccv-2020-commands-4-autonomous-vehicles).
This paper presents the results of the challenge. First, we compare the used
benchmark against existing datasets for visual grounding. Second, we identify
the aspects that render top-performing models successful, and relate them to
existing state-of-the-art models for visual grounding, in addition to detecting
potential failure cases by evaluating on carefully selected subsets. Finally,
we discuss several possibilities for future work. | [
"cs.CV",
"cs.AI"
] |
Two-view structure-from-motion (SfM) is the cornerstone of 3D reconstruction
and visual SLAM. Existing deep learning-based approaches formulate the problem
by either recovering absolute pose scales from two consecutive frames or
predicting a depth map from a single image, both of which are ill-posed
problems. In contrast, we propose to revisit the problem of deep two-view SfM
by leveraging the well-posedness of the classic pipeline. Our method consists
of 1) an optical flow estimation network that predicts dense correspondences
between two frames; 2) a normalized pose estimation module that computes
relative camera poses from the 2D optical flow correspondences, and 3) a
scale-invariant depth estimation network that leverages epipolar geometry to
reduce the search space, refine the dense correspondences, and estimate
relative depth maps. Extensive experiments show that our method outperforms all
state-of-the-art two-view SfM methods by a clear margin on KITTI depth, KITTI
VO, MVS, Scenes11, and SUN3D datasets in both relative pose and depth
estimation. | [
"cs.CV"
] |
The brain performs unsupervised learning and (perhaps) simultaneous
supervised learning. This raises the question as to whether a hybrid of
supervised and unsupervised methods will produce better learning. Inspired by
the rich space of Hebbian learning rules, we set out to directly learn the
unsupervised learning rule on local information that best augments a supervised
signal. We present the Hebbian-augmented training algorithm (HAT) for combining
gradient-based learning with an unsupervised rule on pre-synpatic activity,
post-synaptic activities, and current weights. We test HAT's effect on a simple
problem (Fashion-MNIST) and find consistently higher performance than
supervised learning alone. This finding provides empirical evidence that
unsupervised learning on synaptic activities provides a strong signal that can
be used to augment gradient-based methods.
We further find that the meta-learned update rule is a time-varying function;
thus, it is difficult to pinpoint an interpretable Hebbian update rule that
aids in training. We do find that the meta-learner eventually degenerates into
a non-Hebbian rule that preserves important weights so as not to disturb the
learner's convergence. | [
"cs.LG",
"cs.AI"
] |
This paper proposes MCSSL, a self-supervised learning approach for building
custom object detection models in multi-camera networks. MCSSL associates
bounding boxes between cameras with overlapping fields of view by leveraging
epipolar geometry and state-of-the-art tracking and reID algorithms, and
prudently generates two sets of pseudo-labels to fine-tune backbone and
detection networks respectively in an object detection model. To train
effectively on pseudo-labels,a powerful reID-like pretext task with consistency
loss is constructed for model customization. Our evaluation shows that compared
with legacy selftraining methods, MCSSL improves average mAP by 5.44% and 6.76%
on WildTrack and CityFlow dataset, respectively. | [
"cs.CV",
"cs.AI"
] |
Counterfactual explanations focus on "actionable knowledge" to help end-users
understand how a machine learning outcome could be changed to a more desirable
outcome. For this purpose a counterfactual explainer needs to discover input
dependencies that relate to outcome changes. Identifying the minimum subset of
feature changes needed to action an output change in the decision is an
interesting challenge for counterfactual explainers. The DisCERN algorithm
introduced in this paper is a case-based counter-factual explainer. Here
counterfactuals are formed by replacing feature values from a nearest unlike
neighbour (NUN) until an actionable change is observed. We show how widely
adopted feature relevance-based explainers (i.e. LIME, SHAP), can inform
DisCERN to identify the minimum subset of "actionable features". We demonstrate
our DisCERN algorithm on five datasets in a comparative study with the widely
used optimisation-based counterfactual approach DiCE. Our results demonstrate
that DisCERN is an effective strategy to minimise actionable changes necessary
to create good counterfactual explanations. | [
"cs.LG",
"cs.AI"
] |
In this paper, we introduce a novel visual representation learning which
relies on a handful of adaptively learned tokens, and which is applicable to
both image and video understanding tasks. Instead of relying on hand-designed
splitting strategies to obtain visual tokens and processing a large number of
densely sampled patches for attention, our approach learns to mine important
tokens in visual data. This results in efficiently and effectively finding a
few important visual tokens and enables modeling of pairwise attention between
such tokens, over a longer temporal horizon for videos, or the spatial content
in images. Our experiments demonstrate strong performance on several
challenging benchmarks for both image and video recognition tasks. Importantly,
due to our tokens being adaptive, we accomplish competitive results at
significantly reduced compute amount. | [
"cs.CV",
"cs.LG"
] |
Our objective in this work is fine-grained classification of actions in
untrimmed videos, where the actions may be temporally extended or may span only
a few frames of the video. We cast this into a query-response mechanism, where
each query addresses a particular question, and has its own response label set.
We make the following four contributions: (I) We propose a new model - a
Temporal Query Network - which enables the query-response functionality, and a
structural understanding of fine-grained actions. It attends to relevant
segments for each query with a temporal attention mechanism, and can be trained
using only the labels for each query. (ii) We propose a new way - stochastic
feature bank update - to train a network on videos of various lengths with the
dense sampling required to respond to fine-grained queries. (iii) We compare
the TQN to other architectures and text supervision methods, and analyze their
pros and cons. Finally, (iv) we evaluate the method extensively on the FineGym
and Diving48 benchmarks for fine-grained action classification and surpass the
state-of-the-art using only RGB features. | [
"cs.CV"
] |
We propose a new approach to determine correspondences between image pairs in
the wild under large changes in illumination, viewpoint, context, and material.
While other approaches find correspondences between pairs of images by treating
the images independently, we instead condition on both images to implicitly
take account of the differences between them. To achieve this, we introduce (i)
a spatial attention mechanism (a co-attention module, CoAM) for conditioning
the learned features on both images, and (ii) a distinctiveness score used to
choose the best matches at test time. CoAM can be added to standard
architectures and trained using self-supervision or supervised data, and
achieves a significant performance improvement under hard conditions, e.g.
large viewpoint changes. We demonstrate that models using CoAM achieve state of
the art or competitive results on a wide range of tasks: local matching, camera
localization, 3D reconstruction, and image stylization. | [
"cs.CV"
] |
In this work we consider the problem of learning a classifier from noisy
labels when a few clean labeled examples are given. The structure of clean and
noisy data is modeled by a graph per class and Graph Convolutional Networks
(GCN) are used to predict class relevance of noisy examples. For each class,
the GCN is treated as a binary classifier, which learns to discriminate clean
from noisy examples using a weighted binary cross-entropy loss function. The
GCN-inferred "clean" probability is then exploited as a relevance measure. Each
noisy example is weighted by its relevance when learning a classifier for the
end task. We evaluate our method on an extended version of a few-shot learning
problem, where the few clean examples of novel classes are supplemented with
additional noisy data. Experimental results show that our GCN-based cleaning
process significantly improves the classification accuracy over not cleaning
the noisy data, as well as standard few-shot classification where only few
clean examples are used. | [
"cs.CV",
"cs.LG"
] |
Score based learning (SBL) is a promising approach for learning Bayesian
networks in the discrete domain. However, when employing SBL in the continuous
domain, one is either forced to move the problem to the discrete domain or use
metrics such as BIC/AIC, and these approaches are often lacking. Discretization
can have an undesired impact on the accuracy of the results, and BIC/AIC can
fall short of achieving the desired accuracy. In this paper, we introduce two
new scoring metrics for scoring Bayesian networks in the continuous domain: the
three-part minimum description length and the renormalized normalized maximum
likelihood metric. We rely on the minimum description length principle in
formulating these metrics. The metrics proposed are free of hyperparameters,
decomposable, and are asymptotically consistent. We evaluate our solution by
studying the convergence rate of the learned graph to the generating network
and, also, the structural hamming distance of the learned graph to the
generating network. Our evaluations show that the proposed metrics outperform
their competitors, the BIC/AIC metrics. Furthermore, using the proposed RNML
metric, SBL will have the fastest rate of convergence with the smallest
structural hamming distance to the generating network. | [
"cs.LG",
"stat.ML"
] |
Deep learning has been broadly leveraged by major cloud providers, such as
Google, AWS and Baidu, to offer various computer vision related services
including image classification, object identification, illegal image detection,
etc. While recent works extensively demonstrated that deep learning
classification models are vulnerable to adversarial examples, cloud-based image
detection models, which are more complicated than classifiers, may also have
similar security concern but not get enough attention yet. In this paper, we
mainly focus on the security issues of real-world cloud-based image detectors.
Specifically, (1) based on effective semantic segmentation, we propose four
attacks to generate semantics-aware adversarial examples via only interacting
with black-box APIs; and (2) we make the first attempt to conduct an extensive
empirical study of black-box attacks against real-world cloud-based image
detectors. Through the comprehensive evaluations on five major cloud platforms:
AWS, Azure, Google Cloud, Baidu Cloud, and Alibaba Cloud, we demonstrate that
our image processing based attacks can reach a success rate of approximately
100%, and the semantic segmentation based attacks have a success rate over 90%
among different detection services, such as violence, politician, and
pornography detection. We also proposed several possible defense strategies for
these security challenges in the real-life situation. | [
"cs.CV",
"cs.CR",
"cs.LG"
] |
Recent self-supervised methods for image representation learning are based on
maximizing the agreement between embedding vectors from different views of the
same image. A trivial solution is obtained when the encoder outputs constant
vectors. This collapse problem is often avoided through implicit biases in the
learning architecture, that often lack a clear justification or interpretation.
In this paper, we introduce VICReg (Variance-Invariance-Covariance
Regularization), a method that explicitly avoids the collapse problem with a
simple regularization term on the variance of the embeddings along each
dimension individually. VICReg combines the variance term with a decorrelation
mechanism based on redundancy reduction and covariance regularization, and
achieves results on par with the state of the art on several downstream tasks.
In addition, we show that incorporating our new variance term into other
methods helps stabilize the training and leads to performance improvements. | [
"cs.CV",
"cs.AI",
"cs.LG"
] |
Given an unknown dynamic system such as a coupled harmonic oscillator with
$n$ springs and point masses. We are often interested in gaining insights into
its physical parameters, i.e. stiffnesses and masses, by observing trajectories
of motion. How do we achieve this from video frames or time-series data and
without the knowledge of the dynamics model? We present a neural framework for
estimating physical parameters in a manner consistent with the underlying
physics. The neural framework uses a deep latent variable model to disentangle
the system physical parameters from canonical coordinate observations. It then
returns a Hamiltonian parameterization that generalizes well with respect to
the discovered physical parameters. We tested our framework with simple
harmonic oscillators, $n=1$, and noisy observations and show that it discovers
the underlying system parameters and generalizes well with respect to these
discovered parameters. Our model also extrapolates the dynamics of the system
beyond the training interval and outperforms a non-physically constrained
baseline model. Our source code and datasets can be found at this URL:
https://github.com/gbarber94/ConSciNet. | [
"cs.LG",
"physics.comp-ph"
] |
In this paper, we consider a type of image quality assessment as a
task-specific measurement, which can be used to select images that are more
amenable to a given target task, such as image classification or segmentation.
We propose to train simultaneously two neural networks for image selection and
a target task using reinforcement learning. A controller network learns an
image selection policy by maximising an accumulated reward based on the target
task performance on the controller-selected validation set, whilst the target
task predictor is optimised using the training set. The trained controller is
therefore able to reject those images that lead to poor accuracy in the target
task. In this work, we show that the controller-predicted image quality can be
significantly different from the task-specific image quality labels that are
manually defined by humans. Furthermore, we demonstrate that it is possible to
learn effective image quality assessment without using a ``clean'' validation
set, thereby avoiding the requirement for human labelling of images with
respect to their amenability for the task. Using $6712$, labelled and
segmented, clinical ultrasound images from $259$ patients, experimental results
on holdout data show that the proposed image quality assessment achieved a mean
classification accuracy of $0.94\pm0.01$ and a mean segmentation Dice of
$0.89\pm0.02$, by discarding $5\%$ and $15\%$ of the acquired images,
respectively. The significantly improved performance was observed for both
tested tasks, compared with the respective $0.90\pm0.01$ and $0.82\pm0.02$ from
networks without considering task amenability. This enables image quality
feedback during real-time ultrasound acquisition among many other medical
imaging applications. | [
"cs.LG",
"cs.CV"
] |
We study on weakly-supervised object detection (WSOD) which plays a vital
role in relieving human involvement from object-level annotations. Predominant
works integrate region proposal mechanisms with convolutional neural networks
(CNN). Although CNN is proficient in extracting discriminative local features,
grand challenges still exist to measure the likelihood of a bounding box
containing a complete object (i.e., "objectness"). In this paper, we propose a
novel WSOD framework with Objectness Distillation (i.e., WSOD^2) by designing a
tailored training mechanism for weakly-supervised object detection. Multiple
regression targets are specifically determined by jointly considering bottom-up
(BU) and top-down (TD) objectness from low-level measurement and CNN
confidences with an adaptive linear combination. As bounding box regression can
facilitate a region proposal learning to approach its regression target with
high objectness during training, deep objectness representation learned from
bottom-up evidences can be gradually distilled into CNN by optimization. We
explore different adaptive training curves for BU/TD objectness, and show that
the proposed WSOD^2 can achieve state-of-the-art results. | [
"cs.CV"
] |
Collaborative bandit learning, i.e., bandit algorithms that utilize
collaborative filtering techniques to improve sample efficiency in online
interactive recommendation, has attracted much research attention as it enjoys
the best of both worlds. However, all existing collaborative bandit learning
solutions impose a stationary assumption about the environment, i.e., both user
preferences and the dependency among users are assumed static over time.
Unfortunately, this assumption hardly holds in practice due to users'
ever-changing interests and dependence relations, which inevitably costs a
recommender system sub-optimal performance in practice.
In this work, we develop a collaborative dynamic bandit solution to handle a
changing environment for recommendation. We explicitly model the underlying
changes in both user preferences and their dependency relation as a stochastic
process. Individual user's preference is modeled by a mixture of globally
shared contextual bandit models with a Dirichlet Process prior. Collaboration
among users is thus achieved via Bayesian inference over the global bandit
models. Model selection and arm selection for each user are done via Thompson
sampling to balance exploitation and exploration. Our solution is proved to
maintain a standard $\tilde O(\sqrt{T})$ sublinear regret even in such a
challenging environment. And extensive empirical evaluations on both synthetic
and real-world datasets further confirmed the necessity of modeling a changing
environment and our algorithm's practical advantages against several
state-of-the-art online learning solutions. | [
"cs.LG"
] |
Person re-identification (Re-ID) aims at retrieving a person of interest
across multiple non-overlapping cameras. With the advancement of deep neural
networks and increasing demand of intelligent video surveillance, it has gained
significantly increased interest in the computer vision community. By
dissecting the involved components in developing a person Re-ID system, we
categorize it into the closed-world and open-world settings. The widely studied
closed-world setting is usually applied under various research-oriented
assumptions, and has achieved inspiring success using deep learning techniques
on a number of datasets. We first conduct a comprehensive overview with
in-depth analysis for closed-world person Re-ID from three different
perspectives, including deep feature representation learning, deep metric
learning and ranking optimization. With the performance saturation under
closed-world setting, the research focus for person Re-ID has recently shifted
to the open-world setting, facing more challenging issues. This setting is
closer to practical applications under specific scenarios. We summarize the
open-world Re-ID in terms of five different aspects. By analyzing the
advantages of existing methods, we design a powerful AGW baseline, achieving
state-of-the-art or at least comparable performance on twelve datasets for FOUR
different Re-ID tasks. Meanwhile, we introduce a new evaluation metric (mINP)
for person Re-ID, indicating the cost for finding all the correct matches,
which provides an additional criteria to evaluate the Re-ID system for real
applications. Finally, some important yet under-investigated open issues are
discussed. | [
"cs.CV"
] |
Organ transplantation is often the last resort for treating end-stage
illness, but the probability of a successful transplantation depends greatly on
compatibility between donors and recipients. Current medical practice relies on
coarse rules for donor-recipient matching, but is short of domain knowledge
regarding the complex factors underlying organ compatibility. In this paper, we
formulate the problem of learning data-driven rules for organ matching using
observational data for organ allocations and transplant outcomes. This problem
departs from the standard supervised learning setup in that it involves
matching the two feature spaces (i.e., donors and recipients), and requires
estimating transplant outcomes under counterfactual matches not observed in the
data. To address these problems, we propose a model based on representation
learning to predict donor-recipient compatibility; our model learns
representations that cluster donor features, and applies donor-invariant
transformations to recipient features to predict outcomes for a given
donor-recipient feature instance. Experiments on semi-synthetic and real-world
datasets show that our model outperforms state-of-art allocation methods and
policies executed by human experts. | [
"stat.ML",
"cs.LG"
] |
In this paper we propose several novel distributed gradient-based temporal
difference algorithms for multi-agent off-policy learning of linear
approximation of the value function in Markov decision processes with strict
information structure constraints, limiting inter-agent communications to small
neighborhoods. The algorithms are composed of: 1) local parameter updates based
on single-agent off-policy gradient temporal difference learning algorithms,
including eligibility traces with state dependent parameters, and 2) linear
stochastic time varying consensus schemes, represented by directed graphs. The
proposed algorithms differ by their form, definition of eligibility traces,
selection of time scales and the way of incorporating consensus iterations. The
main contribution of the paper is a convergence analysis based on the general
properties of the underlying Feller-Markov processes and the stochastic time
varying consensus model. We prove, under general assumptions, that the
parameter estimates generated by all the proposed algorithms weakly converge to
the corresponding ordinary differential equations (ODE) with precisely defined
invariant sets. It is demonstrated how the adopted methodology can be applied
to temporal-difference algorithms under weaker information structure
constraints. The variance reduction effect of the proposed algorithms is
demonstrated by formulating and analyzing an asymptotic stochastic differential
equation. Specific guidelines for communication network design are provided.
The algorithms' superior properties are illustrated by characteristic
simulation results. | [
"cs.LG",
"cs.DC",
"cs.SY",
"eess.SY",
"stat.ML"
] |
To enhance the ability of neural networks to extract local point cloud
features and improve their quality, in this paper, we propose a multiscale
graph generation method and a self-adaptive graph convolution method. First, we
propose a multiscale graph generation method for point clouds. This approach
transforms point clouds into a structured multiscale graph form that supports
multiscale analysis of point clouds in the scale space and can obtain the
dimensional features of point cloud data at different scales, thus making it
easier to obtain the best point cloud features. Because traditional
convolutional neural networks are not applicable to graph data with irregular
vertex neighborhoods, this paper presents an sef-adaptive graph convolution
kernel that uses the Chebyshev polynomial to fit an irregular convolution
filter based on the theory of optimal approximation. In this paper, we adopt
max pooling to synthesize the features of different scale maps and generate the
point cloud features. In experiments conducted on three widely used public
datasets, the proposed method significantly outperforms other state-of-the-art
models, demonstrating its effectiveness and generalizability. | [
"cs.CV"
] |
We present RL-GAN-Net, where a reinforcement learning (RL) agent provides
fast and robust control of a generative adversarial network (GAN). Our
framework is applied to point cloud shape completion that converts noisy,
partial point cloud data into a high-fidelity completed shape by controlling
the GAN. While a GAN is unstable and hard to train, we circumvent the problem
by (1) training the GAN on the latent space representation whose dimension is
reduced compared to the raw point cloud input and (2) using an RL agent to find
the correct input to the GAN to generate the latent space representation of the
shape that best fits the current input of incomplete point cloud. The suggested
pipeline robustly completes point cloud with large missing regions. To the best
of our knowledge, this is the first attempt to train an RL agent to control the
GAN, which effectively learns the highly nonlinear mapping from the input noise
of the GAN to the latent space of point cloud. The RL agent replaces the need
for complex optimization and consequently makes our technique real time.
Additionally, we demonstrate that our pipelines can be used to enhance the
classification accuracy of point cloud with missing data. | [
"cs.CV",
"cs.AI"
] |
Self-attention mechanism recently achieves impressive advancement in Natural
Language Processing (NLP) and Image Processing domains. And its permutation
invariance property makes it ideally suitable for point cloud processing.
Inspired by this remarkable success, we propose an end-to-end architecture,
dubbed Cross-Level Cross-Scale Cross-Attention Network (CLCSCANet), for point
cloud representation learning. First, a point-wise feature pyramid module is
introduced to hierarchically extract features from different scales or
resolutions. Then a cross-level cross-attention is designed to model long-range
inter-level and intra-level dependencies. Finally, we develop a cross-scale
cross-attention module to capture interactions between-and-within scales for
representation enhancement. Compared with state-of-the-art approaches, our
network can obtain competitive performance on challenging 3D object
classification, point cloud segmentation tasks via comprehensive experimental
evaluation. | [
"cs.CV",
"cs.MM"
] |
In the big data era, cloud-based machine learning as a service (MLaaS) has
attracted considerable attention. However, when handling sensitive data, such
as financial and medical data, a privacy issue emerges, because the cloud
server can access clients' raw data. A common method of handling sensitive data
in the cloud uses homomorphic encryption, which allows computation over
encrypted data without decryption. Previous research usually adopted a
low-degree polynomial mapping function, such as the square function, for data
classification. However, this technique results in low classification accuracy.
In this study, we seek to improve the classification accuracy for inference
processing in a convolutional neural network (CNN) while using homomorphic
encryption. We adopt an activation function that approximates Google's Swish
activation function while using a fourth-order polynomial. We also adopt batch
normalization to normalize the inputs for the Swish function to fit the input
range to minimize the error. We implemented CNN inference labeling over
homomorphic encryption using the Microsoft's Simple Encrypted Arithmetic
Library for the Cheon-Kim-Kim-Song (CKKS) scheme. The experimental evaluations
confirmed classification accuracies of 99.22% and 80.48% for MNIST and
CIFAR-10, respectively, which entails 0.04% and 4.11% improvements,
respectively, over previous methods. | [
"cs.LG",
"cs.CR",
"stat.ML"
] |
Binary segmentation of volumetric images of porous media is a crucial step
towards gaining a deeper understanding of the factors governing biogeochemical
processes at minute scales. Contemporary work primarily revolves around
primitive techniques based on global or local adaptive thresholding that have
known common drawbacks in image segmentation. Moreover, absence of a unified
benchmark prohibits quantitative evaluation, which further clouds the impact of
existing methodologies. In this study, we tackle the issue on both fronts.
Firstly, by drawing parallels with natural image segmentation, we propose a
novel, and automatic segmentation technique, 3D Quantum Cuts (QCuts-3D)
grounded on a state-of-the-art spectral clustering technique. Secondly, we
curate and present a publicly available dataset of 68 multiphase volumetric
images of porous media with diverse solid geometries, along with voxel-wise
ground truth annotations for each constituting phase. We provide comparative
evaluations between QCuts-3D and the current state-of-the-art over this dataset
across a variety of evaluation metrics. The proposed systematic approach
achieves a 26% increase in AUROC while achieving a substantial reduction of the
computational complexity of the state-of-the-art competitors. Moreover,
statistical analysis reveals that the proposed method exhibits significant
robustness against the compositional variations of porous media. | [
"cs.CV"
] |
Predicting motion of surrounding agents is critical to real-world
applications of tactical path planning for autonomous driving. Due to the
complex temporal dependencies and social interactions of agents, on-line
trajectory prediction is a challenging task. With the development of attention
mechanism in recent years, transformer model has been applied in natural
language sequence processing first and then image processing. In this paper, we
present a Spatial-Channel Transformer Network for trajectory prediction with
attention functions. Instead of RNN models, we employ transformer model to
capture the spatial-temporal features of agents. A channel-wise module is
inserted to measure the social interaction between agents. We find that the
Spatial-Channel Transformer Network achieves promising results on real-world
trajectory prediction datasets on the traffic scenes. | [
"cs.CV"
] |
First-order stochastic optimization methods are currently the most widely
used class of methods for training deep neural networks. However, the choice of
the optimizer has become an ad-hoc rule that can significantly affect the
performance. For instance, SGD with momentum (SGD+M) is typically used in
computer vision (CV) and Adam is used for training transformer models for
Natural Language Processing (NLP). Using the wrong method can lead to
significant performance degradation. Inspired by the dual averaging algorithm,
we propose Modernized Dual Averaging (MDA), an optimizer that is able to
perform as well as SGD+M in CV and as Adam in NLP. Our method is not adaptive
and is significantly simpler than Adam. We show that MDA induces a decaying
uncentered $L_2$-regularization compared to vanilla SGD+M and hypothesize that
this may explain why it works on NLP problems where SGD+M fails. | [
"cs.LG",
"math.OC",
"stat.ML"
] |
Semi-supervised learning is sought for leveraging the unlabelled data when
labelled data is difficult or expensive to acquire. Deep generative models
(e.g., Variational Autoencoder (VAE)) and semisupervised Generative Adversarial
Networks (GANs) have recently shown promising performance in semi-supervised
classification for the excellent discriminative representing ability. However,
the latent code learned by the traditional VAE is not exclusive (repeatable)
for a specific input sample, which prevents it from excellent classification
performance. In particular, the learned latent representation depends on a
non-exclusive component which is stochastically sampled from the prior
distribution. Moreover, the semi-supervised GAN models generate data from
pre-defined distribution (e.g., Gaussian noises) which is independent of the
input data distribution and may obstruct the convergence and is difficult to
control the distribution of the generated data. To address the aforementioned
issues, we propose a novel Adversarial Variational Embedding (AVAE) framework
for robust and effective semi-supervised learning to leverage both the
advantage of GAN as a high quality generative model and VAE as a posterior
distribution learner. The proposed approach first produces an exclusive latent
code by the model which we call VAE++, and meanwhile, provides a meaningful
prior distribution for the generator of GAN. The proposed approach is evaluated
over four different real-world applications and we show that our method
outperforms the state-of-the-art models, which confirms that the combination of
VAE++ and GAN can provide significant improvements in semisupervised
classification. | [
"cs.LG",
"stat.ML"
] |
3D Point cloud registration is still a very challenging topic due to the
difficulty in finding the rigid transformation between two point clouds with
partial correspondences, and it's even harder in the absence of any initial
estimation information. In this paper, we present an end-to-end deep-learning
based approach to resolve the point cloud registration problem. Firstly, the
revised LPD-Net is introduced to extract features and aggregate them with the
graph network. Secondly, the self-attention mechanism is utilized to enhance
the structure information in the point cloud and the cross-attention mechanism
is designed to enhance the corresponding information between the two input
point clouds. Based on which, the virtual corresponding points can be generated
by a soft pointer based method, and finally, the point cloud registration
problem can be solved by implementing the SVD method. Comparison results in
ModelNet40 dataset validate that the proposed approach reaches the
state-of-the-art in point cloud registration tasks and experiment resutls in
KITTI dataset validate the effectiveness of the proposed approach in real
applications.Our source code is available at
\url{https://github.com/qiaozhijian/VCR-Net.git} | [
"cs.CV",
"cs.RO"
] |
Optimization algorithms for solving nonconvex inverse problem have attracted
significant interests recently. However, existing methods require the nonconvex
regularization to be smooth or simple to ensure convergence. In this paper, we
propose a novel gradient descent type algorithm, by leveraging the idea of
residual learning and Nesterov's smoothing technique, to solve inverse problems
consisting of general nonconvex and nonsmooth regularization with provable
convergence. Moreover, we develop a neural network architecture intimating this
algorithm to learn the nonlinear sparsity transformation adaptively from
training data, which also inherits the convergence to accommodate the general
nonconvex structure of this learned transformation. Numerical results
demonstrate that the proposed network outperforms the state-of-the-art methods
on a variety of different image reconstruction problems in terms of efficiency
and accuracy. | [
"cs.CV",
"math.OC"
] |
We revisit a pioneer unsupervised learning technique called archetypal
analysis, which is related to successful data analysis methods such as sparse
coding and non-negative matrix factorization. Since it was proposed, archetypal
analysis did not gain a lot of popularity even though it produces more
interpretable models than other alternatives. Because no efficient
implementation has ever been made publicly available, its application to
important scientific problems may have been severely limited. Our goal is to
bring back into favour archetypal analysis. We propose a fast optimization
scheme using an active-set strategy, and provide an efficient open-source
implementation interfaced with Matlab, R, and Python. Then, we demonstrate the
usefulness of archetypal analysis for computer vision tasks, such as codebook
learning, signal classification, and large image collection visualization. | [
"cs.CV",
"cs.LG",
"stat.ML"
] |
The main contribution of this paper is a simple semi-supervised pipeline that
only uses the original training set without collecting extra data. It is
challenging in 1) how to obtain more training data only from the training set
and 2) how to use the newly generated data. In this work, the generative
adversarial network (GAN) is used to generate unlabeled samples. We propose the
label smoothing regularization for outliers (LSRO). This method assigns a
uniform label distribution to the unlabeled images, which regularizes the
supervised model and improves the baseline. We verify the proposed method on a
practical problem: person re-identification (re-ID). This task aims to retrieve
a query person from other cameras. We adopt the deep convolutional generative
adversarial network (DCGAN) for sample generation, and a baseline convolutional
neural network (CNN) for representation learning. Experiments show that adding
the GAN-generated data effectively improves the discriminative ability of
learned CNN embeddings. On three large-scale datasets, Market-1501, CUHK03 and
DukeMTMC-reID, we obtain +4.37%, +1.6% and +2.46% improvement in rank-1
precision over the baseline CNN, respectively. We additionally apply the
proposed method to fine-grained bird recognition and achieve a +0.6%
improvement over a strong baseline. The code is available at
https://github.com/layumi/Person-reID_GAN. | [
"cs.CV"
] |
This paper introduces a novel and distributed method for detecting inter-map
loop closure outliers in simultaneous localization and mapping (SLAM). The
proposed algorithm does not rely on a good initialization and can handle more
than two maps at a time. In multi-robot SLAM applications, maps made by
different agents have nonidentical spatial frames of reference which makes
initialization very difficult in the presence of outliers. This paper presents
a probabilistic approach for detecting incorrect orientation measurements prior
to pose graph optimization by checking the geometric consistency of rotation
measurements. Expectation-Maximization is used to fine-tune the model
parameters. As ancillary contributions, a new approximate discrete inference
procedure is presented which uses evidence on loops in a graph and is based on
optimization (Alternate Direction Method of Multipliers). This method yields
superior results compared to Belief Propagation and has convergence guarantees.
Simulation and experimental results are presented that evaluate the performance
of the outlier detection method and the inference algorithm on synthetic and
real-world data. | [
"cs.CV"
] |
Diabetic retinopathy (DR) is a common retinal disease that leads to
blindness. For diagnosis purposes, DR image grading aims to provide automatic
DR grade classification, which is not addressed in conventional research
methods of binary DR image classification. Small objects in the eye images,
like lesions and microaneurysms, are essential to DR grading in medical
imaging, but they could easily be influenced by other objects. To address these
challenges, we propose a new deep learning architecture, called BiRA-Net, which
combines the attention model for feature extraction and bilinear model for
fine-grained classification. Furthermore, in considering the distance between
different grades of different DR categories, we propose a new loss function,
called grading loss, which leads to improved training convergence of the
proposed approach. Experimental results are provided to demonstrate the
superior performance of the proposed approach. | [
"cs.CV",
"cs.LG",
"eess.IV"
] |
We investigate the problem of training neural networks from incomplete images
without replacing missing values. For this purpose, we first represent an image
as a graph, in which missing pixels are entirely ignored. The graph image
representation is processed using a spatial graph convolutional network (SGCN)
-- a type of graph convolutional networks, which is a proper generalization of
classical CNNs operating on images. On one hand, our approach avoids the
problem of missing data imputation while, on the other hand, there is a natural
correspondence between CNNs and SGCN. Experiments confirm that our approach
performs better than analogical CNNs with the imputation of missing values on
typical classification and reconstruction tasks. | [
"cs.CV",
"cs.LG"
] |
In recent years, many deep learning models have been adopted in autonomous
driving. At the same time, these models introduce new vulnerabilities that may
compromise the safety of autonomous vehicles. Specifically, recent studies have
demonstrated that adversarial attacks can cause a significant decline in
detection precision of deep learning-based 3D object detection models. Although
driving safety is the ultimate concern for autonomous driving, there is no
comprehensive study on the linkage between the performance of deep learning
models and the driving safety of autonomous vehicles under adversarial attacks.
In this paper, we investigate the impact of two primary types of adversarial
attacks, perturbation attacks and patch attacks, on the driving safety of
vision-based autonomous vehicles rather than the detection precision of deep
learning models. In particular, we consider two state-of-the-art models in
vision-based 3D object detection, Stereo R-CNN and DSGN. To evaluate driving
safety, we propose an end-to-end evaluation framework with a set of driving
safety performance metrics. By analyzing the results of our extensive
evaluation experiments, we find that (1) the attack's impact on the driving
safety of autonomous vehicles and the attack's impact on the precision of 3D
object detectors are decoupled, and (2) the DSGN model demonstrates stronger
robustness to adversarial attacks than the Stereo R-CNN model. In addition, we
further investigate the causes behind the two findings with an ablation study.
The findings of this paper provide a new perspective to evaluate adversarial
attacks and guide the selection of deep learning models in autonomous driving. | [
"cs.CV",
"cs.CR",
"cs.LG"
] |
Current techniques in machine learning are so far are unable to learn
classifiers that are robust to adversarial perturbations. However, they are
able to learn non-robust classifiers with very high accuracy, even in the
presence of random perturbations. Towards explaining this gap, we highlight the
hypothesis that $\textit{robust classification may require more complex
classifiers (i.e. more capacity) than standard classification.}$
In this note, we show that this hypothesis is indeed possible, by giving
several theoretical examples of classification tasks and sets of "simple"
classifiers for which: (1) There exists a simple classifier with high standard
accuracy, and also high accuracy under random $\ell_\infty$ noise. (2) Any
simple classifier is not robust: it must have high adversarial loss with
$\ell_\infty$ perturbations. (3) Robust classification is possible, but only
with more complex classifiers (exponentially more complex, in some examples).
Moreover, $\textit{there is a quantitative trade-off between robustness and
standard accuracy among simple classifiers.}$ This suggests an alternate
explanation of this phenomenon, which appears in practice: the tradeoff may
occur not because the classification task inherently requires such a tradeoff
(as in [Tsipras-Santurkar-Engstrom-Turner-Madry `18]), but because the
structure of our current classifiers imposes such a tradeoff. | [
"cs.LG",
"cs.CC",
"stat.ML"
] |
We present ktrain, a low-code Python library that makes machine learning more
accessible and easier to apply. As a wrapper to TensorFlow and many other
libraries (e.g., transformers, scikit-learn, stellargraph), it is designed to
make sophisticated, state-of-the-art machine learning models simple to build,
train, inspect, and apply by both beginners and experienced practitioners.
Featuring modules that support text data (e.g., text classification, sequence
tagging, open-domain question-answering), vision data (e.g., image
classification), graph data (e.g., node classification, link prediction), and
tabular data, ktrain presents a simple unified interface enabling one to
quickly solve a wide range of tasks in as little as three or four "commands" or
lines of code. | [
"cs.LG",
"cs.CL",
"cs.CV",
"cs.SI"
] |
We propose a method which can detect events in videos by modeling the change
in appearance of the event participants over time. This method makes it
possible to detect events which are characterized not by motion, but by the
changing state of the people or objects involved. This is accomplished by using
object detectors as output models for the states of a hidden Markov model
(HMM). The method allows an HMM to model the sequence of poses of the event
participants over time, and is effective for poses of humans and inanimate
objects. The ability to use existing object-detection methods as part of an
event model makes it possible to leverage ongoing work in the object-detection
community. A novel training method uses an EM loop to simultaneously learn the
temporal structure and object models automatically, without the need to specify
either the individual poses to be modeled or the frames in which they occur.
The E-step estimates the latent assignment of video frames to HMM states, while
the M-step estimates both the HMM transition probabilities and state output
models, including the object detectors, which are trained on the weighted
subset of frames assigned to their state. A new dataset was gathered because
little work has been done on events characterized by changing object pose, and
suitable datasets are not available. Our method produced results superior to
that of comparison systems on this dataset. | [
"cs.CV"
] |
Depth estimation is a fundamental issue in 4-D light field processing and
analysis. Although recent supervised learning-based light field depth
estimation methods have significantly improved the accuracy and efficiency of
traditional optimization-based ones, these methods rely on the training over
light field data with ground-truth depth maps which are challenging to obtain
or even unavailable for real-world light field data. Besides, due to the
inevitable gap (or domain difference) between real-world and synthetic data,
they may suffer from serious performance degradation when generalizing the
models trained with synthetic data to real-world data. By contrast, we propose
an unsupervised learning-based method, which does not require ground-truth
depth as supervision during training. Specifically, based on the basic
knowledge of the unique geometry structure of light field data, we present an
occlusion-aware strategy to improve the accuracy on occlusion areas, in which
we explore the angular coherence among subsets of the light field views to
estimate initial depth maps, and utilize a constrained unsupervised loss to
learn their corresponding reliability for final depth prediction. Additionally,
we adopt a multi-scale network with a weighted smoothness loss to handle the
textureless areas. Experimental results on synthetic data show that our method
can significantly shrink the performance gap between the previous unsupervised
method and supervised ones, and produce depth maps with comparable accuracy to
traditional methods with obviously reduced computational cost. Moreover,
experiments on real-world datasets show that our method can avoid the domain
shift problem presented in supervised methods, demonstrating the great
potential of our method. | [
"cs.CV"
] |
While most image captioning aims to generate objective descriptions of
images, the last few years have seen work on generating visually grounded image
captions which have a specific style (e.g., incorporating positive or negative
sentiment). However, because the stylistic component is typically the last part
of training, current models usually pay more attention to the style at the
expense of accurate content description. In addition, there is a lack of
variability in terms of the stylistic aspects. To address these issues, we
propose an image captioning model called ATTEND-GAN which has two core
components: first, an attention-based caption generator to strongly correlate
different parts of an image with different parts of a caption; and second, an
adversarial training mechanism to assist the caption generator to add diverse
stylistic components to the generated captions. Because of these components,
ATTEND-GAN can generate correlated captions as well as more human-like
variability of stylistic patterns. Our system outperforms the state-of-the-art
as well as a collection of our baseline models. A linguistic analysis of the
generated captions demonstrates that captions generated using ATTEND-GAN have a
wider range of stylistic adjectives and adjective-noun pairs. | [
"cs.CV",
"cs.CL"
] |
Temporal prediction is critical for making intelligent and robust decisions
in complex dynamic environments. Motion prediction needs to model the
inherently uncertain future which often contains multiple potential outcomes,
due to multi-agent interactions and the latent goals of others. Towards these
goals, we introduce a probabilistic framework that efficiently learns latent
variables to jointly model the multi-step future motions of agents in a scene.
Our framework is data-driven and learns semantically meaningful latent
variables to represent the multimodal future, without requiring explicit
labels. Using a dynamic attention-based state encoder, we learn to encode the
past as well as the future interactions among agents, efficiently scaling to
any number of agents. Finally, our model can be used for planning via computing
a conditional probability density over the trajectories of other agents given a
hypothetical rollout of the 'self' agent. We demonstrate our algorithms by
predicting vehicle trajectories of both simulated and real data, demonstrating
the state-of-the-art results on several vehicle trajectory datasets. | [
"cs.LG",
"cs.CV",
"cs.MA",
"cs.RO",
"stat.ML"
] |
Deep learning models have been criticized for their lack of easy
interpretation, which undermines confidence in their use for important
applications. Nevertheless, they are consistently utilized in many
applications, consequential to humans' lives, mostly because of their better
performance. Therefore, there is a great need for computational methods that
can explain, audit, and debug such models. Here, we use flip points to
accomplish these goals for deep learning models with continuous output scores
(e.g., computed by softmax), used in social applications. A flip point is any
point that lies on the boundary between two output classes: e.g. for a model
with a binary yes/no output, a flip point is any input that generates equal
scores for "yes" and "no". The flip point closest to a given input is of
particular importance because it reveals the least changes in the input that
would change a model's classification, and we show that it is the solution to a
well-posed optimization problem. Flip points also enable us to systematically
study the decision boundaries of a deep learning classifier. The resulting
insight into the decision boundaries of a deep model can clearly explain the
model's output on the individual-level, via an explanation report that is
understandable by non-experts. We also develop a procedure to understand and
audit model behavior towards groups of people. Flip points can also be used to
alter the decision boundaries in order to improve undesirable behaviors. We
demonstrate our methods by investigating several models trained on standard
datasets used in social applications of machine learning. We also identify the
features that are most responsible for particular classifications and
misclassifications. | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
Learning to generate 3D point clouds without 3D supervision is an important
but challenging problem. Current solutions leverage various differentiable
renderers to project the generated 3D point clouds onto a 2D image plane, and
train deep neural networks using the per-pixel difference with 2D ground truth
images. However, these solutions are still struggling to fully recover fine
structures of 3D shapes, such as thin tubes or planes. To resolve this issue,
we propose an unsupervised approach for 3D point cloud generation with fine
structures. Specifically, we cast 3D point cloud learning as a 2D projection
matching problem. Rather than using entire 2D silhouette images as a regular
pixel supervision, we introduce structure adaptive sampling to randomly sample
2D points within the silhouettes as an irregular point supervision, which
alleviates the consistency issue of sampling from different view angles. Our
method pushes the neural network to generate a 3D point cloud whose 2D
projections match the irregular point supervision from different view angles.
Our 2D projection matching approach enables the neural network to learn more
accurate structure information than using the per-pixel difference, especially
for fine and thin 3D structures. Our method can recover fine 3D structures from
2D silhouette images at different resolutions, and is robust to different
sampling methods and point number in irregular point supervision. Our method
outperforms others under widely used benchmarks. Our code, data and models are
available at https://github.com/chenchao15/2D\_projection\_matching. | [
"cs.CV"
] |
This paper investigates how end-to-end driving models can be improved to
drive more accurately and human-like. To tackle the first issue we exploit
semantic and visual maps from HERE Technologies and augment the existing
Drive360 dataset with such. The maps are used in an attention mechanism that
promotes segmentation confidence masks, thus focusing the network on semantic
classes in the image that are important for the current driving situation.
Human-like driving is achieved using adversarial learning, by not only
minimizing the imitation loss with respect to the human driver but by further
defining a discriminator, that forces the driving model to produce action
sequences that are human-like. Our models are trained and evaluated on the
Drive360 + HERE dataset, which features 60 hours and 3000 km of real-world
driving data. Extensive experiments show that our driving models are more
accurate and behave more human-like than previous methods. | [
"cs.CV",
"cs.AI",
"cs.RO"
] |
Forecasting multivariate time series is challenging as the variables are
intertwined in time and space, like in the case of traffic signals. Defining
signals on graphs relaxes such complexities by representing the evolution of
signals over a space using relevant graph kernels such as the heat diffusion
kernel. However, this kernel alone does not fully capture the actual dynamics
of the data as it only relies on the graph structure. The gap can be filled by
combining the graph kernel representation with data-driven models that utilize
historical data. This paper proposes a traffic propagation model that merges
multiple heat diffusion kernels into a data-driven prediction model to forecast
traffic signals. We optimize the model parameters using Bayesian inference to
minimize the prediction errors and, consequently, determine the mixing ratio of
the two approaches. Such mixing ratio strongly depends on training data size
and data anomalies, which typically correspond to the peak hours for traffic
data. The proposed model demonstrates prediction accuracy comparable to that of
the state-of-the-art deep neural networks with lower computational effort. It
particularly shows excellent performance for long-term prediction since it
inherits the data-driven models' periodicity modeling. | [
"cs.LG"
] |
Generative adversarial networks has emerged as a defacto standard for image
translation problems. To successfully drive such models, one has to rely on
additional networks e.g., discriminators and/or perceptual networks. Training
these networks with pixel based losses alone are generally not sufficient to
learn the target distribution. In this paper, we propose a novel method of
computing the loss directly between the source and target images that enable
proper distillation of shape/content and colour/style. We show that this is
useful in typical image-to-image translations allowing us to successfully drive
the generator without relying on additional networks. We demonstrate this on
many difficult image translation problems such as image-to-image domain
mapping, single image super-resolution and photo realistic makeup transfer. Our
extensive evaluation shows the effectiveness of the proposed formulation and
its ability to synthesize realistic images. [Code release:
https://github.com/ssarfraz/SPL] | [
"cs.CV",
"cs.LG",
"eess.IV"
] |
We present a method for simultaneously learning, in an unsupervised manner,
(i) a conditional image generator, (ii) foreground extraction and segmentation,
(iii) clustering into a two-level class hierarchy, and (iv) object removal and
background completion, all done without any use of annotation. The method
combines a Generative Adversarial Network and a Variational Auto-Encoder, with
multiple encoders, generators and discriminators, and benefits from solving all
tasks at once. The input to the training scheme is a varied collection of
unlabeled images from the same domain, as well as a set of background images
without a foreground object. In addition, the image generator can mix the
background from one image, with a foreground that is conditioned either on that
of a second image or on the index of a desired cluster. The method obtains
state of the art results in comparison to the literature methods, when compared
to the current state of the art in each of the tasks. | [
"cs.CV"
] |
In recent studies, neural message passing has proved to be an effective way
to design graph neural networks (GNNs), which have achieved state-of-the-art
performance in many graph-based tasks. However, current neural-message passing
architectures typically need to perform an expensive recursive neighborhood
expansion in multiple rounds and consequently suffer from a scalability issue.
Moreover, most existing neural-message passing schemes are inflexible since
they are restricted to fixed-hop neighborhoods and insensitive to the actual
demands of different nodes. We circumvent these limitations by a novel
feature-message passing framework, called Graph Multi-layer Perceptron (GMLP),
which separates the neural update from the message passing. With such
separation, GMLP significantly improves the scalability and efficiency by
performing the message passing procedure in a pre-compute manner, and is
flexible and adaptive in leveraging node feature messages over various levels
of localities. We further derive novel variants of scalable GNNs under this
framework to achieve the best of both worlds in terms of performance and
efficiency. We conduct extensive evaluations on 11 benchmark datasets,
including large-scale datasets like ogbn-products and an industrial dataset,
demonstrating that GMLP achieves not only the state-of-art performance, but
also high training scalability and efficiency. | [
"cs.LG"
] |
In this paper, we present a data augmentation method that generates synthetic
medical images using Generative Adversarial Networks (GANs). We propose a
training scheme that first uses classical data augmentation to enlarge the
training set and then further enlarges the data size and its diversity by
applying GAN techniques for synthetic data augmentation. Our method is
demonstrated on a limited dataset of computed tomography (CT) images of 182
liver lesions (53 cysts, 64 metastases and 65 hemangiomas). The classification
performance using only classic data augmentation yielded 78.6% sensitivity and
88.4% specificity. By adding the synthetic data augmentation the results
significantly increased to 85.7% sensitivity and 92.4% specificity. | [
"cs.CV"
] |
With the increase in available large clinical and experimental datasets,
there has been substantial amount of work being done on addressing the
challenges in the area of biomedical image analysis. Image segmentation, which
is crucial for any quantitative analysis, has especially attracted attention.
Recent hardware advancement has led to the success of deep learning approaches.
However, although deep learning models are being trained on large datasets,
existing methods do not use the information from different learning epochs
effectively. In this work, we leverage the information of each training epoch
to prune the prediction maps of the subsequent epochs. We propose a novel
architecture called feedback attention network (FANet) that unifies the
previous epoch mask with the feature map of the current training epoch. The
previous epoch mask is then used to provide a hard attention to the learnt
feature maps at different convolutional layers. The network also allows to
rectify the predictions in an iterative fashion during the test time. We show
that our proposed feedback attention model provides a substantial improvement
on most segmentation metrics tested on seven publicly available biomedical
imaging datasets demonstrating the effectiveness of the proposed FANet. | [
"cs.CV",
"eess.IV"
] |
As billions of personal data being shared through social media and network,
the data privacy and security have drawn an increasing attention. Several
attempts have been made to alleviate the leakage of identity information from
face photos, with the aid of, e.g., image obfuscation techniques. However, most
of the present results are either perceptually unsatisfactory or ineffective
against face recognition systems. Our goal in this paper is to develop a
technique that can encrypt the personal photos such that they can protect users
from unauthorized face recognition systems but remain visually identical to the
original version for human beings. To achieve this, we propose a targeted
identity-protection iterative method (TIP-IM) to generate adversarial identity
masks which can be overlaid on facial images, such that the original identities
can be concealed without sacrificing the visual quality. Extensive experiments
demonstrate that TIP-IM provides 95\%+ protection success rate against various
state-of-the-art face recognition models under practical test scenarios.
Besides, we also show the practical and effective applicability of our method
on a commercial API service. | [
"cs.LG",
"cs.CR",
"cs.CV",
"stat.ML"
] |
Safe learning and optimization deals with learning and optimization problems
that avoid, as much as possible, the evaluation of non-safe input points, which
are solutions, policies, or strategies that cause an irrecoverable loss (e.g.,
breakage of a machine or equipment, or life threat). Although a comprehensive
survey of safe reinforcement learning algorithms was published in 2015, a
number of new algorithms have been proposed thereafter, and related works in
active learning and in optimization were not considered. This paper reviews
those algorithms from a number of domains including reinforcement learning,
Gaussian process regression and classification, evolutionary algorithms, and
active learning. We provide the fundamental concepts on which the reviewed
algorithms are based and a characterization of the individual algorithms. We
conclude by explaining how the algorithms are connected and suggestions for
future research. | [
"cs.LG",
"cs.NE",
"math.OC",
"I.2.8"
] |
Message passing neural networks have become a method of choice for learning
on graphs, in particular the prediction of chemical properties and the
acceleration of molecular dynamics studies. While they readily scale to large
training data sets, previous approaches have proven to be less data efficient
than kernel methods. We identify limitations of invariant representations as a
major reason and extend the message passing formulation to rotationally
equivariant representations. On this basis, we propose the polarizable atom
interaction neural network (PaiNN) and improve on common molecule benchmarks
over previous networks, while reducing model size and inference time. We
leverage the equivariant atomwise representations obtained by PaiNN for the
prediction of tensorial properties. Finally, we apply this to the simulation of
molecular spectra, achieving speedups of 4-5 orders of magnitude compared to
the electronic structure reference. | [
"cs.LG",
"physics.chem-ph"
] |
In deep learning-based object detection on remote sensing domain, nuisance
factors, which affect observed variables while not affecting predictor
variables, often matters because they cause domain changes. Previously,
nuisance disentangled feature transformation (NDFT) was proposed to build
domain-invariant feature extractor with with knowledge of nuisance factors.
However, NDFT requires enormous time in a training phase, so it has been
impractical. In this paper, we introduce our proposed method, A-NDFT, which is
an improvement to NDFT. A-NDFT utilizes two acceleration techniques, feature
replay and slow learner. Consequently, on a large-scale UAVDT benchmark, it is
shown that our framework can reduce the training time of NDFT from 31 hours to
3 hours while still maintaining the performance. The code will be made publicly
available online. | [
"cs.CV",
"cs.LG"
] |
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