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We introduce a method for 3D object detection using a single monocular image.
Starting from a synthetic dataset, we pre-train an RGB-to-Depth Auto-Encoder
(AE). The embedding learnt from this AE is then used to train a 3D Object
Detector (3DOD) CNN which is used to regress the parameters of 3D object poses
after the encoder from the AE generates a latent embedding from the RGB image.
We show that we can pre-train the AE using paired RGB and depth images from
simulation data once and subsequently only train the 3DOD network using real
data, comprising of RGB images and 3D object pose labels (without the
requirement of dense depth). Our 3DOD network utilizes a particular
`cubification' of 3D space around the camera, where each cuboid is tasked with
predicting N object poses, along with their class and confidence values. The AE
pre-training and this method of dividing the 3D space around the camera into
cuboids give our method its name - CubifAE-3D. We demonstrate results for
monocular 3D object detection in the Autonomous Vehicle (AV) use-case with the
Virtual KITTI 2 and the KITTI datasets. | [
"cs.CV",
"cs.LG"
] |
While much attention has been given to the problem of estimating the effect
of discrete interventions from observational data, relatively little work has
been done in the setting of continuous-valued interventions, such as treatments
associated with a dosage parameter. In this paper, we tackle this problem by
building on a modification of the generative adversarial networks (GANs)
framework. Our model, SCIGAN, is flexible and capable of simultaneously
estimating counterfactual outcomes for several different continuous
interventions. The key idea is to use a significantly modified GAN model to
learn to generate counterfactual outcomes, which can then be used to learn an
inference model, using standard supervised methods, capable of estimating these
counterfactuals for a new sample. To address the challenges presented by
shifting to continuous interventions, we propose a novel architecture for our
discriminator - we build a hierarchical discriminator that leverages the
structure of the continuous intervention setting. Moreover, we provide
theoretical results to support our use of the GAN framework and of the
hierarchical discriminator. In the experiments section, we introduce a new
semi-synthetic data simulation for use in the continuous intervention setting
and demonstrate improvements over the existing benchmark models. | [
"cs.LG",
"stat.ML"
] |
Facial analysis models are increasingly used in applications that have
serious impacts on people's lives, ranging from authentication to surveillance
tracking. It is therefore critical to develop techniques that can reveal
unintended biases in facial classifiers to help guide the ethical use of facial
analysis technology. This work proposes a framework called \textit{image
counterfactual sensitivity analysis}, which we explore as a proof-of-concept in
analyzing a smiling attribute classifier trained on faces of celebrities. The
framework utilizes counterfactuals to examine how a classifier's prediction
changes if a face characteristic slightly changes. We leverage recent advances
in generative adversarial networks to build a realistic generative model of
face images that affords controlled manipulation of specific image
characteristics. We then introduce a set of metrics that measure the effect of
manipulating a specific property on the output of the trained classifier.
Empirically, we find several different factors of variation that affect the
predictions of the smiling classifier. This proof-of-concept demonstrates
potential ways generative models can be leveraged for fine-grained analysis of
bias and fairness. | [
"cs.CV",
"cs.LG",
"stat.ML"
] |
Autonomous vehicles were experiencing rapid development in the past few
years. However, achieving full autonomy is not a trivial task, due to the
nature of the complex and dynamic driving environment. Therefore, autonomous
vehicles are equipped with a suite of different sensors to ensure robust,
accurate environmental perception. In particular, the camera-LiDAR fusion is
becoming an emerging research theme. However, so far there has been no critical
review that focuses on deep-learning-based camera-LiDAR fusion methods. To
bridge this gap and motivate future research, this paper devotes to review
recent deep-learning-based data fusion approaches that leverage both image and
point cloud. This review gives a brief overview of deep learning on image and
point cloud data processing. Followed by in-depth reviews of camera-LiDAR
fusion methods in depth completion, object detection, semantic segmentation,
tracking and online cross-sensor calibration, which are organized based on
their respective fusion levels. Furthermore, we compare these methods on
publicly available datasets. Finally, we identified gaps and over-looked
challenges between current academic researches and real-world applications.
Based on these observations, we provide our insights and point out promising
research directions. | [
"cs.CV",
"cs.LG",
"cs.RO"
] |
Our global population contributes visual content on platforms like Instagram,
attempting to express themselves and engage their audiences, at an
unprecedented and increasing rate. In this paper, we revisit the popularity
prediction on Instagram. We present a robust, efficient, and explainable
baseline for population-based popularity prediction, achieving strong ranking
performance. We employ the latest methods in computer vision to maximize the
information extracted from the visual modality. We use transfer learning to
extract visual semantics such as concepts, scenes, and objects, allowing a new
level of scrutiny in an extensive, explainable ablation study. We inform
feature selection towards a robust and scalable model, but also illustrate
feature interactions, offering new directions for further inquiry in
computational social science. Our strongest models inform a lower limit to
population-based predictability of popularity on Instagram. The models are
immediately applicable to social media monitoring and influencer
identification. | [
"cs.CV",
"cs.SI"
] |
Every living organism struggles against disruptive environmental forces to
carve out and maintain an orderly niche. We propose that such a struggle to
achieve and preserve order might offer a principle for the emergence of useful
behaviors in artificial agents. We formalize this idea into an unsupervised
reinforcement learning method called surprise minimizing reinforcement learning
(SMiRL). SMiRL alternates between learning a density model to evaluate the
surprise of a stimulus, and improving the policy to seek more predictable
stimuli. The policy seeks out stable and repeatable situations that counteract
the environment's prevailing sources of entropy. This might include avoiding
other hostile agents, or finding a stable, balanced pose for a bipedal robot in
the face of disturbance forces. We demonstrate that our surprise minimizing
agents can successfully play Tetris, Doom, control a humanoid to avoid falls,
and navigate to escape enemies in a maze without any task-specific reward
supervision. We further show that SMiRL can be used together with standard task
rewards to accelerate reward-driven learning. | [
"cs.LG",
"cs.AI",
"stat.ML",
"G.3"
] |
We formalize concepts around geometric occlusion in 2D images (i.e., ignoring
semantics), and propose a novel unified formulation of both occlusion
boundaries and occlusion orientations via a pixel-pair occlusion relation. The
former provides a way to generate large-scale accurate occlusion datasets
while, based on the latter, we propose a novel method for task-independent
pixel-level occlusion relationship estimation from single images. Experiments
on a variety of datasets demonstrate that our method outperforms existing ones
on this task. To further illustrate the value of our formulation, we also
propose a new depth map refinement method that consistently improve the
performance of state-of-the-art monocular depth estimation methods. Our code
and data are available at http://imagine.enpc.fr/~qiux/P2ORM/. | [
"cs.CV"
] |
This paper addresses the importance of full-image supervision for monocular
depth estimation. We propose a semi-supervised architecture, which combines
both unsupervised framework of using image consistency and supervised framework
of dense depth completion. The latter provides full-image depth as supervision
for the former. Ego-motion from navigation system is also embedded into the
unsupervised framework as output supervision of an inner temporal transform
network, making monocular depth estimation better. In the evaluation, we show
that our proposed model outperforms other approaches on depth estimation. | [
"cs.CV"
] |
Knowledge distillation (KD), as an efficient and effective model compression
technique, has been receiving considerable attention in deep learning. The key
to its success is to transfer knowledge from a large teacher network to a small
student one. However, most of the existing knowledge distillation methods
consider only one type of knowledge learned from either instance features or
instance relations via a specific distillation strategy in teacher-student
learning. There are few works that explore the idea of transferring different
types of knowledge with different distillation strategies in a unified
framework. Moreover, the frequently used offline distillation suffers from a
limited learning capacity due to the fixed teacher-student architecture. In
this paper we propose a collaborative teacher-student learning via multiple
knowledge transfer (CTSL-MKT) that prompts both self-learning and collaborative
learning. It allows multiple students learn knowledge from both individual
instances and instance relations in a collaborative way. While learning from
themselves with self-distillation, they can also guide each other via online
distillation. The experiments and ablation studies on four image datasets
demonstrate that the proposed CTSL-MKT significantly outperforms the
state-of-the-art KD methods. | [
"cs.LG"
] |
Over the recent years, Graph Neural Networks have become increasingly popular
in network analytic and beyond. With that, their architecture noticeable
diverges from the classical multi-layered hierarchical organization of the
traditional neural networks. At the same time, many conventional approaches in
network science efficiently utilize the hierarchical approaches to account for
the hierarchical organization of the networks, and recent works emphasize their
critical importance. This paper aims to connect the dots between the
traditional Neural Network and the Graph Neural Network architectures as well
as the network science approaches, harnessing the power of the hierarchical
network organization. A Hierarchical Graph Neural Network architecture is
proposed, supplementing the original input network layer with the hierarchy of
auxiliary network layers and organizing the computational scheme updating the
node features through both - horizontal network connections within each layer
as well as the vertical connection between the layers. It enables simultaneous
learning of the individual node features along with the aggregated network
features at variable resolution and uses them to improve the convergence and
stability of the individual node feature learning. The proposed Hierarchical
Graph Neural network architecture is successfully evaluated on the network
embedding and modeling as well as network classification, node labeling, and
community tasks and demonstrates increased efficiency in those. | [
"cs.LG",
"cs.AI",
"math.CO",
"physics.data-an",
"68T07, 05C85"
] |
This paper presents a reinforcement learning approach to synthesizing
task-driven control policies for robotic systems equipped with rich sensory
modalities (e.g., vision or depth). Standard reinforcement learning algorithms
typically produce policies that tightly couple control actions to the entirety
of the system's state and rich sensor observations. As a consequence, the
resulting policies can often be sensitive to changes in task-irrelevant
portions of the state or observations (e.g., changing background colors). In
contrast, the approach we present here learns to create a task-driven
representation that is used to compute control actions. Formally, this is
achieved by deriving a policy gradient-style algorithm that creates an
information bottleneck between the states and the task-driven representation;
this constrains actions to only depend on task-relevant information. We
demonstrate our approach in a thorough set of simulation results on multiple
examples including a grasping task that utilizes depth images and a
ball-catching task that utilizes RGB images. Comparisons with a standard policy
gradient approach demonstrate that the task-driven policies produced by our
algorithm are often significantly more robust to sensor noise and
task-irrelevant changes in the environment. | [
"cs.LG",
"cs.RO",
"math.OC",
"stat.ML"
] |
For relocalization in large-scale point clouds, we propose the first approach
that unifies global place recognition and local 6DoF pose refinement. To this
end, we design a Siamese network that jointly learns 3D local feature detection
and description directly from raw 3D points. It integrates FlexConv and
Squeeze-and-Excitation (SE) to assure that the learned local descriptor
captures multi-level geometric information and channel-wise relations. For
detecting 3D keypoints we predict the discriminativeness of the local
descriptors in an unsupervised manner. We generate the global descriptor by
directly aggregating the learned local descriptors with an effective attention
mechanism. In this way, local and global 3D descriptors are inferred in one
single forward pass. Experiments on various benchmarks demonstrate that our
method achieves competitive results for both global point cloud retrieval and
local point cloud registration in comparison to state-of-the-art approaches. To
validate the generalizability and robustness of our 3D keypoints, we
demonstrate that our method also performs favorably without fine-tuning on the
registration of point clouds that were generated by a visual SLAM system. Code
and related materials are available at
https://vision.in.tum.de/research/vslam/dh3d. | [
"cs.CV"
] |
Point-cloud registration (PCR) is an important task in various applications
such as robotic manipulation, augmented and virtual reality, SLAM, etc. PCR is
an optimization problem involving minimization over two different types of
interdependent variables: transformation parameters and point-to-point
correspondences. Recent developments in deep-learning have produced
computationally fast approaches for PCR. The loss functions that are optimized
in these networks are based on the error in the transformation parameters. We
hypothesize that these methods would perform significantly better if they
calculated their loss function using correspondence error instead of only using
error in transformation parameters. We define correspondence error as a metric
based on incorrectly matched point pairs. We provide a fundamental explanation
for why this is the case and test our hypothesis by modifying existing methods
to use correspondence-based loss instead of transformation-based loss. These
experiments show that the modified networks converge faster and register more
accurately even at larger misalignment when compared to the original networks. | [
"cs.CV"
] |
Spherical data is distributed on the sphere. The data appears in various
fields such as meteorology, biology, and natural language processing. However,
a method for analysis of spherical data does not develop enough yet. One of the
important issues is an estimation of the number of clusters in spherical data.
To address the issue, I propose a new method called the Spherical X-means
(SX-means) that can estimate the number of clusters on d-dimensional sphere.
The SX-means is the model-based method assuming that the data is generated from
a mixture of von Mises-Fisher distributions. The present paper explains the
proposed method and shows its performance of estimation of the number of
clusters. | [
"cs.LG",
"stat.ML"
] |
3D urban reconstruction of buildings from remotely sensed imagery has drawn
significant attention during the past two decades. While aerial imagery and
LiDAR provide higher resolution, satellite imagery is cheaper and more
efficient to acquire for large scale need. However, the high, orbital altitude
of satellite observation brings intrinsic challenges, like unpredictable
atmospheric effect, multi view angles, significant radiometric differences due
to the necessary multiple views, diverse land covers and urban structures in a
scene, small base-height ratio or narrow field of view, all of which may
degrade 3D reconstruction quality. To address these major challenges, we
present a reliable and effective approach for building model reconstruction
from the point clouds generated from multi-view satellite images. We utilize
multiple types of primitive shapes to fit the input point cloud. Specifically,
a deep-learning approach is adopted to distinguish the shape of building roofs
in complex and yet noisy scenes. For points that belong to the same roof shape,
a multi-cue, hierarchical RANSAC approach is proposed for efficient and
reliable segmenting and reconstructing the building point cloud. Experimental
results over four selected urban areas (0.34 to 2.04 sq km in size) demonstrate
the proposed method can generate detailed roof structures under noisy data
environments. The average successful rate for building shape recognition is
83.0%, while the overall completeness and correctness are over 70% with
reference to ground truth created from airborne lidar. As the first effort to
address the public need of large scale city model generation, the development
is deployed as open source software. | [
"cs.CV",
"cs.LG",
"eess.IV"
] |
Image segmentation refers to the process to divide an image into
nonoverlapping meaningful regions according to human perception, which has
become a classic topic since the early ages of computer vision. A lot of
research has been conducted and has resulted in many applications. However,
while many segmentation algorithms exist, yet there are only a few sparse and
outdated summarizations available, an overview of the recent achievements and
issues is lacking. We aim to provide a comprehensive review of the recent
progress in this field. Covering 180 publications, we give an overview of broad
areas of segmentation topics including not only the classic bottom-up
approaches, but also the recent development in superpixel, interactive methods,
object proposals, semantic image parsing and image cosegmentation. In addition,
we also review the existing influential datasets and evaluation metrics.
Finally, we suggest some design flavors and research directions for future
research in image segmentation. | [
"cs.CV"
] |
In this paper we propose a novel method for infrared and visible image fusion
where we develop nest connection-based network and spatial/channel attention
models. The nest connection-based network can preserve significant amounts of
information from input data in a multi-scale perspective. The approach
comprises three key elements: encoder, fusion strategy and decoder
respectively. In our proposed fusion strategy, spatial attention models and
channel attention models are developed that describe the importance of each
spatial position and of each channel with deep features. Firstly, the source
images are fed into the encoder to extract multi-scale deep features. The novel
fusion strategy is then developed to fuse these features for each scale.
Finally, the fused image is reconstructed by the nest connection-based decoder.
Experiments are performed on publicly available datasets. These exhibit that
our proposed approach has better fusion performance than other state-of-the-art
methods. This claim is justified through both subjective and objective
evaluation. The code of our fusion method is available at
https://github.com/hli1221/imagefusion-nestfuse | [
"cs.CV"
] |
Diabetic retinopathy (DR) is a primary cause of blindness in working-age
people worldwide. About 3 to 4 million people with diabetes become blind
because of DR every year. Diagnosis of DR through color fundus images is a
common approach to mitigate such problem. However, DR diagnosis is a difficult
and time consuming task, which requires experienced clinicians to identify the
presence and significance of many small features on high resolution images.
Convolutional Neural Network (CNN) has proved to be a promising approach for
automatic biomedical image analysis recently. In this work, we investigate
lesion detection on DR fundus images with CNN-based object detection methods.
Lesion detection on fundus images faces two unique challenges. The first one is
that our dataset is not fully labeled, i.e., only a subset of all lesion
instances are marked. Not only will these unlabeled lesion instances not
contribute to the training of the model, but also they will be mistakenly
counted as false negatives, leading the model move to the opposite direction.
The second challenge is that the lesion instances are usually very small,
making them difficult to be found by normal object detectors. To address the
first challenge, we introduce an iterative training algorithm for the
semi-supervised method of pseudo-labeling, in which a considerable number of
unlabeled lesion instances can be discovered to boost the performance of the
lesion detector. For the small size targets problem, we extend both the input
size and the depth of feature pyramid network (FPN) to produce a large CNN
feature map, which can preserve the detail of small lesions and thus enhance
the effectiveness of the lesion detector. The experimental results show that
our proposed methods significantly outperform the baselines. | [
"cs.CV",
"cs.LG",
"eess.IV"
] |
This paper considers a canonical clustering problem where one receives
unlabeled samples drawn from a balanced mixture of two elliptical distributions
and aims for a classifier to estimate the labels. Many popular methods
including PCA and k-means require individual components of the mixture to be
somewhat spherical, and perform poorly when they are stretched. To overcome
this issue, we propose a non-convex program seeking for an affine transform to
turn the data into a one-dimensional point cloud concentrating around -1 and 1,
after which clustering becomes easy. Our theoretical contributions are
two-fold: (1) we show that the non-convex loss function exhibits desirable
landscape properties as long as the sample size exceeds some constant multiple
of the dimension, and (2) we leverage this to prove that an efficient
first-order algorithm achieves near-optimal statistical precision even without
good initialization. We also propose a general methodology for multi-class
clustering tasks with flexible choices of feature transforms and loss
objectives. | [
"stat.ML",
"cs.LG",
"math.OC",
"math.ST",
"stat.ME",
"stat.TH",
"62H30"
] |
Consider an imitation learning problem that the imitator and the expert have
different dynamics models. Most of the current imitation learning methods fail
because they focus on imitating actions. We propose a novel state
alignment-based imitation learning method to train the imitator to follow the
state sequences in expert demonstrations as much as possible. The state
alignment comes from both local and global perspectives and we combine them
into a reinforcement learning framework by a regularized policy update
objective. We show the superiority of our method on standard imitation learning
settings and imitation learning settings where the expert and imitator have
different dynamics models. | [
"cs.LG",
"stat.ML"
] |
We introduce the SE(3)-Transformer, a variant of the self-attention module
for 3D point clouds and graphs, which is equivariant under continuous 3D
roto-translations. Equivariance is important to ensure stable and predictable
performance in the presence of nuisance transformations of the data input. A
positive corollary of equivariance is increased weight-tying within the model.
The SE(3)-Transformer leverages the benefits of self-attention to operate on
large point clouds and graphs with varying number of points, while guaranteeing
SE(3)-equivariance for robustness. We evaluate our model on a toy N-body
particle simulation dataset, showcasing the robustness of the predictions under
rotations of the input. We further achieve competitive performance on two
real-world datasets, ScanObjectNN and QM9. In all cases, our model outperforms
a strong, non-equivariant attention baseline and an equivariant model without
attention. | [
"cs.LG",
"stat.ML"
] |
In this paper we introduce the first reinforcement learning (RL) based
robotic navigation method which utilizes ultrasound (US) images as an input.
Our approach combines state-of-the-art RL techniques, specifically deep
Q-networks (DQN) with memory buffers and a binary classifier for deciding when
to terminate the task.
Our method is trained and evaluated on an in-house collected data-set of 34
volunteers and when compared to pure RL and supervised learning (SL)
techniques, it performs substantially better, which highlights the suitability
of RL navigation for US-guided procedures. When testing our proposed model, we
obtained a 82.91% chance of navigating correctly to the sacrum from 165
different starting positions on 5 different unseen simulated environments. | [
"cs.LG",
"cs.RO",
"stat.ML"
] |
Few sample learning (FSL) is significant and challenging in the field of
machine learning. The capability of learning and generalizing from very few
samples successfully is a noticeable demarcation separating artificial
intelligence and human intelligence since humans can readily establish their
cognition to novelty from just a single or a handful of examples whereas
machine learning algorithms typically entail hundreds or thousands of
supervised samples to guarantee generalization ability. Despite the long
history dated back to the early 2000s and the widespread attention in recent
years with booming deep learning technologies, little surveys or reviews for
FSL are available until now. In this context, we extensively review 300+ papers
of FSL spanning from the 2000s to 2019 and provide a timely and comprehensive
survey for FSL. In this survey, we review the evolution history as well as the
current progress on FSL, categorize FSL approaches into the generative model
based and discriminative model based kinds in principle, and emphasize
particularly on the meta learning based FSL approaches. We also summarize
several recently emerging extensional topics of FSL and review the latest
advances on these topics. Furthermore, we highlight the important FSL
applications covering many research hotspots in computer vision, natural
language processing, audio and speech, reinforcement learning and robotic, data
analysis, etc. Finally, we conclude the survey with a discussion on promising
trends in the hope of providing guidance and insights to follow-up researches. | [
"cs.LG",
"cs.CV",
"stat.ML"
] |
Current end-to-end deep Reinforcement Learning (RL) approaches require
jointly learning perception, decision-making and low-level control from very
sparse reward signals and high-dimensional inputs, with little capability of
incorporating prior knowledge. This results in prohibitively long training
times for use on real-world robotic tasks. Existing algorithms capable of
extracting task-level representations from high-dimensional inputs, e.g. object
detection, often produce outputs of varying lengths, restricting their use in
RL methods due to the need for neural networks to have fixed length inputs. In
this work, we propose a framework that combines deep sets encoding, which
allows for variable-length abstract representations, with modular RL that
utilizes these representations, decoupling high-level decision making from
low-level control. We successfully demonstrate our approach on the robot
manipulation task of object sorting, showing that this method can learn
effective policies within mere minutes of highly simplified simulation. The
learned policies can be directly deployed on a robot without further training,
and generalize to variations of the task unseen during training. | [
"cs.LG",
"stat.ML"
] |
During software front-end development, the work to convert Graphical User
Interface(GUI) image to the corresponding front-end code is an inevitable
tedious work. There have been some attempts to make this work to be automatic.
However, the GUI code generated by these models is not accurate due to the lack
of attention mechanism guidance. To solve this problem, we propose PixCoder
based on an artificially supervised attention mechanism. The approach is to
train a neural network to predict the style sheets in the input GUI image and
then output a vector. PixCoder generate the GUI code targeting specific
platform according to the output vector. The experimental results have shown
the accuracy of the GUI code generated by PixCoder is over 95%. | [
"cs.LG",
"cs.CV"
] |
Vision-and-language (V\&L) reasoning necessitates perception of visual
concepts such as objects and actions, understanding semantics and language
grounding, and reasoning about the interplay between the two modalities. One
crucial aspect of visual reasoning is spatial understanding, which involves
understanding relative locations of objects, i.e.\ implicitly learning the
geometry of the scene. In this work, we evaluate the faithfulness of V\&L
models to such geometric understanding, by formulating the prediction of
pair-wise relative locations of objects as a classification as well as a
regression task. Our findings suggest that state-of-the-art transformer-based
V\&L models lack sufficient abilities to excel at this task. Motivated by this,
we design two objectives as proxies for 3D spatial reasoning (SR) -- object
centroid estimation, and relative position estimation, and train V\&L with weak
supervision from off-the-shelf depth estimators. This leads to considerable
improvements in accuracy for the "GQA" visual question answering challenge (in
fully supervised, few-shot, and O.O.D settings) as well as improvements in
relative spatial reasoning. Code and data will be released
\href{https://github.com/pratyay-banerjee/weak_sup_vqa}{here}. | [
"cs.CV",
"cs.CL",
"cs.LG"
] |
Reinforcement Learning has yielded promising results for Neural Architecture
Search (NAS). In this paper, we demonstrate how its performance can be improved
by using a simplified Transformer block to model the policy network. The
simplified Transformer uses a 2-stream attention-based mechanism to model
hyper-parameter dependencies while avoiding layer normalization and position
encoding. We posit that this parsimonious design balances model complexity
against expressiveness, making it suitable for discovering optimal
architectures in high-dimensional search spaces with limited exploration
budgets. We demonstrate how the algorithm's performance can be further improved
by a) using an actor-critic style algorithm instead of plain vanilla policy
gradient and b) ensembling Transformer blocks with shared parameters, each
block conditioned on a different auto-regressive factorization order. Our
algorithm works well as both a NAS and generic hyper-parameter optimization
(HPO) algorithm: it outperformed most algorithms on NAS-Bench-101, a public
data-set for benchmarking NAS algorithms. In particular, it outperformed RL
based methods that use alternate architectures to model the policy network,
underlining the value of using attention-based networks in this setting. As a
generic HPO algorithm, it outperformed Random Search in discovering more
accurate multi-layer perceptron model architectures across 2 regression tasks.
We have adhered to guidelines listed in Lindauer and Hutter while designing
experiments and reporting results. | [
"cs.LG",
"cs.NE",
"stat.ML"
] |
The coronavirus disease (COVID-19) has resulted in a pandemic crippling the a
breadth of services critical to daily life. Segmentation of lung infections in
computerized tomography (CT) slices could be be used to improve diagnosis and
understanding of COVID-19 in patients. Deep learning systems lack
interpretability because of their black box nature. Inspired by human
communication of complex ideas through language, we propose a symbolic
framework based on emergent languages for the segmentation of COVID-19
infections in CT scans of lungs. We model the cooperation between two
artificial agents - a Sender and a Receiver. These agents synergistically
cooperate using emergent symbolic language to solve the task of semantic
segmentation. Our game theoretic approach is to model the cooperation between
agents unlike Generative Adversarial Networks (GANs). The Sender retrieves
information from one of the higher layers of the deep network and generates a
symbolic sentence sampled from a categorical distribution of vocabularies. The
Receiver ingests the stream of symbols and cogenerates the segmentation mask. A
private emergent language is developed that forms the communication channel
used to describe the task of segmentation of COVID infections. We augment
existing state of the art semantic segmentation architectures with our symbolic
generator to form symbolic segmentation models. Our symbolic segmentation
framework achieves state of the art performance for segmentation of lung
infections caused by COVID-19. Our results show direct interpretation of
symbolic sentences to discriminate between normal and infected regions,
infection morphology and image characteristics. We show state of the art
results for segmentation of COVID-19 lung infections in CT. | [
"cs.CV",
"eess.IV"
] |
Random-walk based network embedding algorithms like node2vec and DeepWalk are
widely used to obtain Euclidean representation of the nodes in a network prior
to performing down-stream network inference tasks. Nevertheless, despite their
impressive empirical performance, there is a lack of theoretical results
explaining their behavior. In this paper we studied the node2vec and DeepWalk
algorithms through the perspective of matrix factorization. We analyze these
algorithms in the setting of community detection for stochastic blockmodel
graphs; in particular we established large-sample error bounds and prove
consistent community recovery of node2vec/DeepWalk embedding followed by
k-means clustering. Our theoretical results indicate a subtle interplay between
the sparsity of the observed networks, the window sizes of the random walks,
and the convergence rates of the node2vec/DeepWalk embedding toward the
embedding of the true but unknown edge probabilities matrix. More specifically,
as the network becomes sparser, our results suggest using larger window sizes,
or equivalently, taking longer random walks, in order to attain better
convergence rate for the resulting embeddings. The paper includes numerical
experiments corroborating these observations. | [
"stat.ML",
"cs.LG",
"cs.SI"
] |
Koopman spectral analysis has attracted attention for nonlinear dynamical
systems since we can analyze nonlinear dynamics with a linear regime by
embedding data into a Koopman space by a nonlinear function. For the analysis,
we need to find appropriate embedding functions. Although several neural
network-based methods have been proposed for learning embedding functions,
existing methods require long time-series for training neural networks. This
limitation prohibits performing Koopman spectral analysis in applications where
only short time-series are available. In this paper, we propose a meta-learning
method for estimating embedding functions from unseen short time-series by
exploiting knowledge learned from related but different time-series. With the
proposed method, a representation of a given short time-series is obtained by a
bidirectional LSTM for extracting its properties. The embedding function of the
short time-series is modeled by a neural network that depends on the
time-series representation. By sharing the LSTM and neural networks across
multiple time-series, we can learn common knowledge from different time-series
while modeling time-series-specific embedding functions with the time-series
representation. Our model is trained such that the expected test prediction
error is minimized with the episodic training framework. We experimentally
demonstrate that the proposed method achieves better performance in terms of
eigenvalue estimation and future prediction than existing methods. | [
"stat.ML",
"cs.LG",
"math.DS"
] |
We present Vax-a-Net; a technique for immunizing convolutional neural
networks (CNNs) against adversarial patch attacks (APAs). APAs insert visually
overt, local regions (patches) into an image to induce misclassification. We
introduce a conditional Generative Adversarial Network (GAN) architecture that
simultaneously learns to synthesise patches for use in APAs, whilst exploiting
those attacks to adapt a pre-trained target CNN to reduce its susceptibility to
them. This approach enables resilience against APAs to be conferred to
pre-trained models, which would be impractical with conventional adversarial
training due to the slow convergence of APA methods. We demonstrate
transferability of this protection to defend against existing APAs, and show
its efficacy across several contemporary CNN architectures. | [
"cs.CV"
] |
Convolutional Neural Networks (CNNs) are being increasingly used to address
the problem of iris presentation attack detection. In this work, we propose
attention-guided iris presentation attack detection (AG-PAD) to augment CNNs
with attention mechanisms. Two types of attention modules are independently
appended on top of the last convolutional layer of the backbone network.
Specifically, the channel attention module is used to model the inter-channel
relationship between features, while the position attention module is used to
model inter-spatial relationship between features. An element-wise sum is
employed to fuse these two attention modules. Further, a novel hierarchical
attention mechanism is introduced. Experiments involving both a JHU-APL
proprietary dataset and the benchmark LivDet-Iris-2017 dataset suggest that the
proposed method achieves promising results. To the best of our knowledge, this
is the first work that exploits the use of attention mechanisms in iris
presentation attack detection. | [
"cs.CV"
] |
In this paper, we tackle an open research question in transfer learning,
which is selecting a model initialization to achieve high performance on a new
task, given several pre-trained models. We propose a new highly efficient and
accurate approach based on duality diagram similarity (DDS) between deep neural
networks (DNNs). DDS is a generic framework to represent and compare data of
different feature dimensions. We validate our approach on the Taskonomy dataset
by measuring the correspondence between actual transfer learning performance
rankings on 17 taskonomy tasks and predicted rankings. Computing DDS based
ranking for $17\times17$ transfers requires less than 2 minutes and shows a
high correlation ($0.86$) with actual transfer learning rankings, outperforming
state-of-the-art methods by a large margin ($10\%$) on the Taskonomy benchmark.
We also demonstrate the robustness of our model selection approach to a new
task, namely Pascal VOC semantic segmentation. Additionally, we show that our
method can be applied to select the best layer locations within a DNN for
transfer learning on 2D, 3D and semantic tasks on NYUv2 and Pascal VOC
datasets. | [
"cs.CV",
"cs.LG"
] |
In this paper, we examine the visual variability of objects across different
ad categories, i.e. what causes an advertisement to be visually persuasive. We
focus on modeling and generating faces which appear to come from different
types of ads. For example, if faces in beauty ads tend to be women wearing
lipstick, a generative model should portray this distinct visual appearance.
Training generative models which capture such category-specific differences is
challenging because of the highly diverse appearance of faces in ads and the
relatively limited amount of available training data. To address these
problems, we propose a conditional variational autoencoder which makes use of
predicted semantic attributes and facial expressions as a supervisory signal
when training. We show how our model can be used to produce visually distinct
faces which appear to be from a fixed ad topic category. Our human studies and
quantitative and qualitative experiments confirm that our method greatly
outperforms a variety of baselines, including two variations of a
state-of-the-art generative adversarial network, for transforming faces to be
more ad-category appropriate. Finally, we show preliminary generation results
for other types of objects, conditioned on an ad topic. | [
"cs.CV"
] |
We study Thompson sampling (TS) in online decision-making problems where the
uncertain environment is sampled from a mixture distribution. This is relevant
to multi-task settings, where a learning agent is faced with different classes
of problems. We incorporate this structure in a natural way by initializing TS
with a mixture prior -- dubbed MixTS -- and develop a novel, general technique
for analyzing the regret of TS with such priors. We apply this technique to
derive Bayes regret bounds for MixTS in both linear bandits and tabular Markov
decision processes (MDPs). Our regret bounds reflect the structure of the
problem and depend on the number of components and confidence width of each
component of the prior. Finally, we demonstrate the empirical effectiveness of
MixTS in both synthetic and real-world experiments. | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
In recent years, deep neural network approaches have naturally extended to
the video domain, in their simplest case by aggregating per-frame
classifications as a baseline for action recognition. A majority of the work in
this area extends from the imaging domain, leading to visual-feature heavy
approaches on temporal data. To address this issue we introduce "Let's Dance",
a 1000 video dataset (and growing) comprised of 10 visually overlapping dance
categories that require motion for their classification. We stress the
important of human motion as a key distinguisher in our work given that, as we
show in this work, visual information is not sufficient to classify
motion-heavy categories. We compare our datasets' performance using imaging
techniques with UCF-101 and demonstrate this inherent difficulty. We present a
comparison of numerous state-of-the-art techniques on our dataset using three
different representations (video, optical flow and multi-person pose data) in
order to analyze these approaches. We discuss the motion parameterization of
each of them and their value in learning to categorize online dance videos.
Lastly, we release this dataset (and its three representations) for the
research community to use. | [
"cs.CV",
"I.4; I.5; I.5.1"
] |
Class imbalance is a common problem in supervised learning and impedes the
predictive performance of classification models. Popular countermeasures
include oversampling the minority class. Standard methods like SMOTE rely on
finding nearest neighbours and linear interpolations which are problematic in
case of high-dimensional, complex data distributions. Generative Adversarial
Networks (GANs) have been proposed as an alternative method for generating
artificial minority examples as they can model complex distributions. However,
prior research on GAN-based oversampling does not incorporate recent
advancements from the literature on generating realistic tabular data with
GANs. Previous studies also focus on numerical variables whereas categorical
features are common in many business applications of classification methods
such as credit scoring. The paper propoes an oversampling method based on a
conditional Wasserstein GAN that can effectively model tabular datasets with
numerical and categorical variables and pays special attention to the
down-stream classification task through an auxiliary classifier loss. We
benchmark our method against standard oversampling methods and the imbalanced
baseline on seven real-world datasets. Empirical results evidence the
competitiveness of GAN-based oversampling. | [
"cs.LG"
] |
Until now, all single level segmentation algorithms except CNN-based ones
lead to over segmentation. And CNN-based segmentation algorithms have their own
problems. To avoid over segmentation, multiple thresholds of criteria are
adopted in region merging process to produce hierarchical segmentation results.
However, there still has extreme over segmentation in the low level of the
hierarchy, and outstanding tiny objects are merged to their large adjacencies
in the high level of the hierarchy. This paper proposes a region-merging-based
image segmentation method that we call it Dam Burst. As a single level
segmentation algorithm, this method avoids over segmentation and retains
details by the same time. It is named because of that it simulates a flooding
from underground destroys dams between water-pools. We treat edge detection
results as strengthening structure of a dam if it is on the dam. To simulate a
flooding from underground, regions are merged by ascending order of the average
gra-dient inside the region. | [
"cs.CV"
] |
Recent work proposed $\delta$-relevant inputs (or sets) as a probabilistic
explanation for the predictions made by a classifier on a given input.
$\delta$-relevant sets are significant because they serve to relate
(model-agnostic) Anchors with (model-accurate) PI- explanations, among other
explanation approaches. Unfortunately, the computation of smallest size
$\delta$-relevant sets is complete for ${NP}^{PP}$, rendering their computation
largely infeasible in practice. This paper investigates solutions for tackling
the practical limitations of $\delta$-relevant sets. First, the paper
alternatively considers the computation of subset-minimal sets. Second, the
paper studies concrete families of classifiers, including decision trees among
others. For these cases, the paper shows that the computation of subset-minimal
$\delta$-relevant sets is in NP, and can be solved with a polynomial number of
calls to an NP oracle. The experimental evaluation compares the proposed
approach with heuristic explainers for the concrete case of the classifiers
studied in the paper, and confirms the advantage of the proposed solution over
the state of the art. | [
"cs.LG",
"cs.AI"
] |
As an important component of autonomous systems, autonomous car perception
has had a big leap with recent advances in parallel computing architectures.
With the use of tiny but full-feature embedded supercomputers, computer stereo
vision has been prevalently applied in autonomous cars for depth perception.
The two key aspects of computer stereo vision are speed and accuracy. They are
both desirable but conflicting properties, as the algorithms with better
disparity accuracy usually have higher computational complexity. Therefore, the
main aim of developing a computer stereo vision algorithm for resource-limited
hardware is to improve the trade-off between speed and accuracy. In this
chapter, we introduce both the hardware and software aspects of computer stereo
vision for autonomous car systems. Then, we discuss four autonomous car
perception tasks, including 1) visual feature detection, description and
matching, 2) 3D information acquisition, 3) object detection/recognition and 4)
semantic image segmentation. The principles of computer stereo vision and
parallel computing on multi-threading CPU and GPU architectures are then
detailed. | [
"cs.CV"
] |
Road transportation is of critical importance for a nation, having profound
effects in the economy, the health and life style of its people. With the
growth of cities and populations come bigger demands for mobility and safety,
creating new problems and magnifying those of the past. New tools are needed to
face the challenge, to keep roads in good conditions, their users safe, and
minimize the impact on the environment.
This dissertation is concerned with road quality assessment and aggressive
driving, two important problems in road transportation, approached in the
context of Intelligent Transportation Systems by using Machine Learning
techniques to analyze acceleration time series acquired with smartphone-based
opportunistic sensing to automatically detect, classify, and characterize
events of interest.
Two aspects of road quality assessment are addressed: the detection and the
characterization of road anomalies. For the first, the most widely cited works
in the literature are compared and proposals capable of equal or better
performance are presented, removing the reliance on threshold values and
reducing the computational cost and dimensionality of previous proposals. For
the second, new approaches for the estimation of pothole depth and the
functional condition of speed reducers are showed. The new problem of pothole
depth ranking is introduced, using a learning-to-rank approach to sort
acceleration signals by the depth of the potholes that they reflect.
The classification of aggressive driving maneuvers is done with automatic
feature extraction, finding characteristically shaped subsequences in the
signals as more effective discriminants than conventional descriptors
calculated over time windows.
Finally, all the previously mentioned tasks are combined to produce a robust
road transport evaluation platform. | [
"cs.LG",
"eess.SP",
"stat.ML"
] |
In this report, we present a new reinforcement learning (RL) benchmark based
on the Sonic the Hedgehog (TM) video game franchise. This benchmark is intended
to measure the performance of transfer learning and few-shot learning
algorithms in the RL domain. We also present and evaluate some baseline
algorithms on the new benchmark. | [
"cs.LG",
"stat.ML"
] |
This work proposes the continuous conditional generative adversarial network
(CcGAN), the first generative model for image generation conditional on
continuous, scalar conditions (termed regression labels). Existing conditional
GANs (cGANs) are mainly designed for categorical conditions (eg, class labels);
conditioning on regression labels is mathematically distinct and raises two
fundamental problems:(P1) Since there may be very few (even zero) real images
for some regression labels, minimizing existing empirical versions of cGAN
losses (aka empirical cGAN losses) often fails in practice;(P2) Since
regression labels are scalar and infinitely many, conventional label input
methods are not applicable. The proposed CcGAN solves the above problems,
respectively, by (S1) reformulating existing empirical cGAN losses to be
appropriate for the continuous scenario; and (S2) proposing a naive label input
(NLI) method and an improved label input (ILI) method to incorporate regression
labels into the generator and the discriminator. The reformulation in (S1)
leads to two novel empirical discriminator losses, termed the hard vicinal
discriminator loss (HVDL) and the soft vicinal discriminator loss (SVDL)
respectively, and a novel empirical generator loss. The error bounds of a
discriminator trained with HVDL and SVDL are derived under mild assumptions in
this work. Two new benchmark datasets (RC-49 and Cell-200) and a novel
evaluation metric (Sliding Fr\'echet Inception Distance) are also proposed for
this continuous scenario. Our experiments on the Circular 2-D Gaussians, RC-49,
UTKFace, Cell-200, and Steering Angle datasets show that CcGAN is able to
generate diverse, high-quality samples from the image distribution conditional
on a given regression label. Moreover, in these experiments, CcGAN
substantially outperforms cGAN both visually and quantitatively. | [
"cs.CV",
"cs.LG",
"stat.ML"
] |
Bayesian optimization (BO) is a widely-used method for optimizing expensive
(to evaluate) problems. At the core of most BO methods is the modeling of the
objective function using a Gaussian Process (GP) whose covariance is selected
from a set of standard covariance functions. From a weight-space view, this
models the objective as a linear function in a feature space implied by the
given covariance K, with an arbitrary Gaussian weight prior ${\bf w} \sim
\mathcal{N} ({\bf 0}, {\bf I})$. In many practical applications there is data
available that has a similar (covariance) structure to the objective, but
which, having different form, cannot be used directly in standard transfer
learning. In this paper we show how such auxiliary data may be used to
construct a GP covariance corresponding to a more appropriate weight prior for
the objective function. Building on this, we show that we may accelerate BO by
modeling the objective function using this (learned) weight prior, which we
demonstrate on both test functions and a practical application to short-polymer
fibre manufacture. | [
"stat.ML",
"cs.LG"
] |
The use of machine learning in chemistry has become a common practice. At the
same time, despite the success of modern machine learning methods, the lack of
data limits their use. Using a transfer learning methodology can help solve
this problem. This methodology assumes that a model built on a sufficient
amount of data captures general features of the chemical compound structure on
which it was trained and that the further reuse of these features on a dataset
with a lack of data will greatly improve the quality of the new model. In this
paper, we develop this approach for small organic molecules, implementing
transfer learning with graph convolutional neural networks. The paper shows a
significant improvement in the performance of models for target properties with
a lack of data. The effects of the dataset composition on model quality and the
applicability domain of the resulting models are also considered. | [
"cs.LG"
] |
3D reconstruction from single view images is an ill-posed problem. Inferring
the hidden regions from self-occluded images is both challenging and ambiguous.
We propose a two-pronged approach to address these issues. To better
incorporate the data prior and generate meaningful reconstructions, we propose
3D-LMNet, a latent embedding matching approach for 3D reconstruction. We first
train a 3D point cloud auto-encoder and then learn a mapping from the 2D image
to the corresponding learnt embedding. To tackle the issue of uncertainty in
the reconstruction, we predict multiple reconstructions that are consistent
with the input view. This is achieved by learning a probablistic latent space
with a novel view-specific diversity loss. Thorough quantitative and
qualitative analysis is performed to highlight the significance of the proposed
approach. We outperform state-of-the-art approaches on the task of single-view
3D reconstruction on both real and synthetic datasets while generating multiple
plausible reconstructions, demonstrating the generalizability and utility of
our approach. | [
"cs.CV"
] |
In this paper, we present a hypergraph neural networks (HGNN) framework for
data representation learning, which can encode high-order data correlation in a
hypergraph structure. Confronting the challenges of learning representation for
complex data in real practice, we propose to incorporate such data structure in
a hypergraph, which is more flexible on data modeling, especially when dealing
with complex data. In this method, a hyperedge convolution operation is
designed to handle the data correlation during representation learning. In this
way, traditional hypergraph learning procedure can be conducted using hyperedge
convolution operations efficiently. HGNN is able to learn the hidden layer
representation considering the high-order data structure, which is a general
framework considering the complex data correlations. We have conducted
experiments on citation network classification and visual object recognition
tasks and compared HGNN with graph convolutional networks and other traditional
methods. Experimental results demonstrate that the proposed HGNN method
outperforms recent state-of-the-art methods. We can also reveal from the
results that the proposed HGNN is superior when dealing with multi-modal data
compared with existing methods. | [
"cs.LG",
"stat.ML"
] |
Validation accuracy is a necessary, but not sufficient, measure of a neural
network classifier's quality. High validation accuracy during development does
not guarantee that a model is free of serious flaws, such as vulnerability to
adversarial attacks or a tendency to misclassify (with high confidence) data it
was not trained on. The model may also be incomprehensible to a human or base
its decisions on unreasonable criteria. These problems, which are not unique to
classifiers, have been the focus of a substantial amount of recent research.
However, they are not prioritized during model development, which almost always
optimizes on validation accuracy to the exclusion of everything else. The
product of this approach is likely to fail in unexpected ways outside of the
training environment. We believe that, in addition to validation accuracy, the
model development process must give added weight to other performance metrics
such as explainability, resistance to adversarial attacks, and overconfidence
on out-of-distribution data. | [
"cs.LG"
] |
We consider an MRI reconstruction problem with input of k-space data at a
very low undersampled rate. This can practically benefit patient due to reduced
time of MRI scan, but it is also challenging since quality of reconstruction
may be compromised. Currently, deep learning based methods dominate MRI
reconstruction over traditional approaches such as Compressed Sensing, but they
rarely show satisfactory performance in the case of low undersampled k-space
data. One explanation is that these methods treat channel-wise features
equally, which results in degraded representation ability of the neural
network. To solve this problem, we propose a new model called MRI Cascaded
Channel-wise Attention Network (MICCAN), highlighted by three components: (i) a
variant of U-net with Channel-wise Attention (UCA) module, (ii) a long skip
connection and (iii) a combined loss. Our model is able to attend to salient
information by filtering irrelevant features and also concentrate on
high-frequency information by enforcing low-frequency information bypassed to
the final output. We conduct both quantitative evaluation and qualitative
analysis of our method on a cardiac dataset. The experiment shows that our
method achieves very promising results in terms of three common metrics on the
MRI reconstruction with low undersampled k-space data. | [
"cs.CV",
"cs.LG"
] |
Functional groups (FGs) are molecular substructures that are served as a
foundation for analyzing and predicting chemical properties of molecules.
Automatic discovery of FGs will impact various fields of research, including
medicinal chemistry and material sciences, by reducing the amount of lab
experiments required for discovery or synthesis of new molecules. In this
paper, we investigate methods based on graph convolutional neural networks
(GCNNs) for localizing FGs that contribute to specific chemical properties of
interest. In our framework, molecules are modeled as undirected relational
graphs with atoms as nodes and bonds as edges. Using this relational graph
structure, we trained GCNNs in a supervised way on experimentally-validated
molecular training sets to predict specific chemical properties, e.g.,
toxicity. Upon learning a GCNN, we analyzed its activation patterns to
automatically identify FGs using four different explainability methods that we
have developed: gradient-based saliency maps, Class Activation Mapping (CAM),
gradient-weighted CAM (Grad-CAM), and Excitation Back-Propagation. Although
these methods are originally derived for convolutional neural networks (CNNs),
we adapt them to develop the corresponding suitable versions for GCNNs. We
evaluated the contrastive power of these methods with respect to the
specificity of the identified molecular substructures and their relevance for
chemical functions. Grad-CAM had the highest contrastive power and generated
qualitatively the best FGs. This work paves the way for automatic analysis and
design of new molecules. | [
"cs.LG",
"stat.ML"
] |
To mitigate the issue of minimal intrinsic features for pure data-driven
methods, in this paper, we propose a novel model-driven deep network for
infrared small target detection, which combines discriminative networks and
conventional model-driven methods to make use of both labeled data and the
domain knowledge. By designing a feature map cyclic shift scheme, we modularize
a conventional local contrast measure method as a depth-wise parameterless
nonlinear feature refinement layer in an end-to-end network, which encodes
relatively long-range contextual interactions with clear physical
interpretability. To highlight and preserve the small target features, we also
exploit a bottom-up attentional modulation integrating the smaller scale subtle
details of low-level features into high-level features of deeper layers. We
conduct detailed ablation studies with varying network depths to empirically
verify the effectiveness and efficiency of the design of each component in our
network architecture. We also compare the performance of our network against
other model-driven methods and deep networks on the open SIRST dataset as well.
The results suggest that our network yields a performance boost over its
competitors. Our code, trained models, and results are available online. | [
"cs.CV"
] |
We present the task of Spatio-Temporal Video Question Answering, which
requires intelligent systems to simultaneously retrieve relevant moments and
detect referenced visual concepts (people and objects) to answer natural
language questions about videos. We first augment the TVQA dataset with 310.8K
bounding boxes, linking depicted objects to visual concepts in questions and
answers. We name this augmented version as TVQA+. We then propose
Spatio-Temporal Answerer with Grounded Evidence (STAGE), a unified framework
that grounds evidence in both spatial and temporal domains to answer questions
about videos. Comprehensive experiments and analyses demonstrate the
effectiveness of our framework and how the rich annotations in our TVQA+
dataset can contribute to the question answering task. Moreover, by performing
this joint task, our model is able to produce insightful and interpretable
spatio-temporal attention visualizations. Dataset and code are publicly
available at: http: //tvqa.cs.unc.edu, https://github.com/jayleicn/TVQAplus | [
"cs.CV",
"cs.AI",
"cs.CL"
] |
This work aims to address the problem of low-shot object detection, where
only a few training samples are available for each category. Regarding the fact
that conventional fully supervised approaches usually suffer huge performance
drop with rare classes where data is insufficient, our study reveals that there
exists more serious misalignment between classification confidence and
localization accuracy on rarely labeled categories, and the prone to
overfitting class-specific parameters is the crucial cause of this issue. In
this paper, we propose a novel low-shot classification correction network
(LSCN) which can be adopted into any anchor-based detector to directly enhance
the detection accuracy on data-rare categories, without sacrificing the
performance on base categories. Specially, we sample false positive proposals
from a base detector to train a separate classification correction network.
During inference, the well-trained correction network removes false positives
from the base detector. The proposed correction network is data-efficient yet
highly effective with four carefully designed components, which are Unified
recognition, Global receptive field, Inter-class separation, and Confidence
calibration. Experiments show our proposed method can bring significant
performance gains to rarely labeled categories and outperforms previous work on
COCO and PASCAL VOC by a large margin. | [
"cs.CV"
] |
Deep learning usually requires large amounts of labeled training data, but
annotating data is costly and tedious. The framework of semi-supervised
learning provides the means to use both labeled data and arbitrary amounts of
unlabeled data for training. Recently, semi-supervised deep learning has been
intensively studied for standard CNN architectures. However, Fully
Convolutional Networks (FCNs) set the state-of-the-art for many image
segmentation tasks. To the best of our knowledge, there is no existing
semi-supervised learning method for such FCNs yet. We lift the concept of
auxiliary manifold embedding for semi-supervised learning to FCNs with the help
of Random Feature Embedding. In our experiments on the challenging task of MS
Lesion Segmentation, we leverage the proposed framework for the purpose of
domain adaptation and report substantial improvements over the baseline model. | [
"cs.CV"
] |
Recent advances in nonlinear Independent Component Analysis (ICA) provide a
principled framework for unsupervised feature learning and disentanglement. The
central idea in such works is that the latent components are assumed to be
independent conditional on some observed auxiliary variables, such as the
time-segment index. This requires manual segmentation of data into
non-stationary segments which is computationally expensive, inaccurate and
often impossible. These models are thus not fully unsupervised. We remedy these
limitations by combining nonlinear ICA with a Hidden Markov Model, resulting in
a model where a latent state acts in place of the observed segment-index. We
prove identifiability of the proposed model for a general mixing nonlinearity,
such as a neural network. We also show how maximum likelihood estimation of the
model can be done using the expectation-maximization algorithm. Thus, we
achieve a new nonlinear ICA framework which is unsupervised, more efficient, as
well as able to model underlying temporal dynamics. | [
"stat.ML",
"cs.LG"
] |
Model-based reinforcement learning (RL) has proven to be a data efficient
approach for learning control tasks but is difficult to utilize in domains with
complex observations such as images. In this paper, we present a method for
learning representations that are suitable for iterative model-based policy
improvement, even when the underlying dynamical system has complex dynamics and
image observations, in that these representations are optimized for inferring
simple dynamics and cost models given data from the current policy. This
enables a model-based RL method based on the linear-quadratic regulator (LQR)
to be used for systems with image observations. We evaluate our approach on a
range of robotics tasks, including manipulation with a real-world robotic arm
directly from images. We find that our method produces substantially better
final performance than other model-based RL methods while being significantly
more efficient than model-free RL. | [
"cs.LG",
"cs.RO",
"stat.ML"
] |
We develop a personalized real time risk scoring algorithm that provides
timely and granular assessments for the clinical acuity of ward patients based
on their (temporal) lab tests and vital signs. Heterogeneity of the patients
population is captured via a hierarchical latent class model. The proposed
algorithm aims to discover the number of latent classes in the patients
population, and train a mixture of Gaussian Process (GP) experts, where each
expert models the physiological data streams associated with a specific class.
Self-taught transfer learning is used to transfer the knowledge of latent
classes learned from the domain of clinically stable patients to the domain of
clinically deteriorating patients. For new patients, the posterior beliefs of
all GP experts about the patient's clinical status given her physiological data
stream are computed, and a personalized risk score is evaluated as a weighted
average of those beliefs, where the weights are learned from the patient's
hospital admission information. Experiments on a heterogeneous cohort of 6,313
patients admitted to Ronald Regan UCLA medical center show that our risk score
outperforms the currently deployed risk scores, such as MEWS and Rothman
scores. | [
"cs.LG",
"stat.ML"
] |
Semantic image synthesis aims at generating photorealistic images from
semantic layouts. Previous approaches with conditional generative adversarial
networks (GAN) show state-of-the-art performance on this task, which either
feed the semantic label maps as inputs to the generator, or use them to
modulate the activations in normalization layers via affine transformations. We
argue that convolutional kernels in the generator should be aware of the
distinct semantic labels at different locations when generating images. In
order to better exploit the semantic layout for the image generator, we propose
to predict convolutional kernels conditioned on the semantic label map to
generate the intermediate feature maps from the noise maps and eventually
generate the images. Moreover, we propose a feature pyramid semantics-embedding
discriminator, which is more effective in enhancing fine details and semantic
alignments between the generated images and the input semantic layouts than
previous multi-scale discriminators. We achieve state-of-the-art results on
both quantitative metrics and subjective evaluation on various semantic
segmentation datasets, demonstrating the effectiveness of our approach. | [
"cs.CV",
"cs.LG",
"eess.IV"
] |
We present Voxel Transformer (VoTr), a novel and effective voxel-based
Transformer backbone for 3D object detection from point clouds. Conventional 3D
convolutional backbones in voxel-based 3D detectors cannot efficiently capture
large context information, which is crucial for object recognition and
localization, owing to the limited receptive fields. In this paper, we resolve
the problem by introducing a Transformer-based architecture that enables
long-range relationships between voxels by self-attention. Given the fact that
non-empty voxels are naturally sparse but numerous, directly applying standard
Transformer on voxels is non-trivial. To this end, we propose the sparse voxel
module and the submanifold voxel module, which can operate on the empty and
non-empty voxel positions effectively. To further enlarge the attention range
while maintaining comparable computational overhead to the convolutional
counterparts, we propose two attention mechanisms for multi-head attention in
those two modules: Local Attention and Dilated Attention, and we further
propose Fast Voxel Query to accelerate the querying process in multi-head
attention. VoTr contains a series of sparse and submanifold voxel modules and
can be applied in most voxel-based detectors. Our proposed VoTr shows
consistent improvement over the convolutional baselines while maintaining
computational efficiency on the KITTI dataset and the Waymo Open dataset. | [
"cs.CV"
] |
Vehicle tracking is an essential task in the multi-object tracking (MOT)
field. A distinct characteristic in vehicle tracking is that the trajectories
of vehicles are fairly smooth in both the world coordinate and the image
coordinate. Hence, models that capture motion consistencies are of high
necessity. However, tracking with the standalone motion-based trackers is quite
challenging because targets could get lost easily due to limited information,
detection error and occlusion. Leveraging appearance information to assist
object re-identification could resolve this challenge to some extent. However,
doing so requires extra computation while appearance information is sensitive
to occlusion as well. In this paper, we try to explore the significance of
motion patterns for vehicle tracking without appearance information. We propose
a novel approach that tackles the association issue for long-term tracking with
the exclusive fully-exploited motion information. We address the tracklet
embedding issue with the proposed reconstruct-to-embed strategy based on deep
graph convolutional neural networks (GCN). Comprehensive experiments on the
KITTI-car tracking dataset and UA-Detrac dataset show that the proposed method,
though without appearance information, could achieve competitive performance
with the state-of-the-art (SOTA) trackers. The source code will be available at
https://github.com/GaoangW/LGMTracker. | [
"cs.CV"
] |
With the large-scale explosion of images and videos over the internet,
efficient hashing methods have been developed to facilitate memory and time
efficient retrieval of similar images. However, none of the existing works uses
hashing to address texture image retrieval mostly because of the lack of
sufficiently large texture image databases. Our work addresses this problem by
developing a novel deep learning architecture that generates binary hash codes
for input texture images. For this, we first pre-train a Texture Synthesis
Network (TSN) which takes a texture patch as input and outputs an enlarged view
of the texture by injecting newer texture content. Thus it signifies that the
TSN encodes the learnt texture specific information in its intermediate layers.
In the next stage, a second network gathers the multi-scale feature
representations from the TSN's intermediate layers using channel-wise
attention, combines them in a progressive manner to a dense continuous
representation which is finally converted into a binary hash code with the help
of individual and pairwise label information. The new enlarged texture patches
also help in data augmentation to alleviate the problem of insufficient texture
data and are used to train the second stage of the network. Experiments on
three public texture image retrieval datasets indicate the superiority of our
texture synthesis guided hashing approach over current state-of-the-art
methods. | [
"cs.CV"
] |
The performance of generative adversarial networks (GANs) heavily
deteriorates given a limited amount of training data. This is mainly because
the discriminator is memorizing the exact training set. To combat it, we
propose Differentiable Augmentation (DiffAugment), a simple method that
improves the data efficiency of GANs by imposing various types of
differentiable augmentations on both real and fake samples. Previous attempts
to directly augment the training data manipulate the distribution of real
images, yielding little benefit; DiffAugment enables us to adopt the
differentiable augmentation for the generated samples, effectively stabilizes
training, and leads to better convergence. Experiments demonstrate consistent
gains of our method over a variety of GAN architectures and loss functions for
both unconditional and class-conditional generation. With DiffAugment, we
achieve a state-of-the-art FID of 6.80 with an IS of 100.8 on ImageNet 128x128
and 2-4x reductions of FID given 1,000 images on FFHQ and LSUN. Furthermore,
with only 20% training data, we can match the top performance on CIFAR-10 and
CIFAR-100. Finally, our method can generate high-fidelity images using only 100
images without pre-training, while being on par with existing transfer learning
algorithms. Code is available at
https://github.com/mit-han-lab/data-efficient-gans. | [
"cs.CV",
"cs.GR",
"cs.LG"
] |
Time series classification problems have drawn increasing attention in the
machine learning and statistical community. Closely related is the field of
functional data analysis (FDA): it refers to the range of problems that deal
with the analysis of data that is continuously indexed over some domain. While
often employing different methods, both fields strive to answer similar
questions, a common example being classification or regression problems with
functional covariates. We study methods from functional data analysis, such as
functional generalized additive models, as well as functionality to concatenate
(functional-) feature extraction or basis representations with traditional
machine learning algorithms like support vector machines or classification
trees. In order to assess the methods and implementations, we run a benchmark
on a wide variety of representative (time series) data sets, with in-depth
analysis of empirical results, and strive to provide a reference ranking for
which method(s) to use for non-expert practitioners. Additionally, we provide a
software framework in R for functional data analysis for supervised learning,
including machine learning and more linear approaches from statistics. This
allows convenient access, and in connection with the machine-learning toolbox
mlr, those methods can now also be tuned and benchmarked. | [
"stat.ML",
"cs.LG"
] |
Automatic skin lesion segmentation on dermoscopic images is an essential step
in computer-aided diagnosis of melanoma. However, this task is challenging due
to significant variations of lesion appearances across different patients. This
challenge is further exacerbated when dealing with a large amount of image
data. In this paper, we extended our previous work by developing a deeper
network architecture with smaller kernels to enhance its discriminant capacity.
In addition, we explicitly included color information from multiple color
spaces to facilitate network training and thus to further improve the
segmentation performance. We extensively evaluated our method on the ISBI 2017
skin lesion segmentation challenge. By training with the 2000 challenge
training images, our method achieved an average Jaccard Index (JA) of 0.765 on
the 600 challenge testing images, which ranked itself in the first place in the
challenge | [
"cs.CV"
] |
Current object detection frameworks mainly rely on bounding box regression to
localize objects. Despite the remarkable progress in recent years, the
precision of bounding box regression remains unsatisfactory, hence limiting
performance in object detection. We observe that precise localization requires
careful placement of each side of the bounding box. However, the mainstream
approach, which focuses on predicting centers and sizes, is not the most
effective way to accomplish this task, especially when there exists
displacements with large variance between the anchors and the targets. In this
paper, we propose an alternative approach, named as Side-Aware Boundary
Localization (SABL), where each side of the bounding box is respectively
localized with a dedicated network branch. To tackle the difficulty of precise
localization in the presence of displacements with large variance, we further
propose a two-step localization scheme, which first predicts a range of
movement through bucket prediction and then pinpoints the precise position
within the predicted bucket. We test the proposed method on both two-stage and
single-stage detection frameworks. Replacing the standard bounding box
regression branch with the proposed design leads to significant improvements on
Faster R-CNN, RetinaNet, and Cascade R-CNN, by 3.0%, 1.7%, and 0.9%,
respectively. Code is available at https://github.com/open-mmlab/mmdetection. | [
"cs.CV"
] |
Despite deep neural networks have demonstrated extraordinary power in various
applications, their superior performances are at expense of high storage and
computational costs. Consequently, the acceleration and compression of neural
networks have attracted much attention recently. Knowledge Transfer (KT), which
aims at training a smaller student network by transferring knowledge from a
larger teacher model, is one of the popular solutions. In this paper, we
propose a novel knowledge transfer method by treating it as a distribution
matching problem. Particularly, we match the distributions of neuron
selectivity patterns between teacher and student networks. To achieve this
goal, we devise a new KT loss function by minimizing the Maximum Mean
Discrepancy (MMD) metric between these distributions. Combined with the
original loss function, our method can significantly improve the performance of
student networks. We validate the effectiveness of our method across several
datasets, and further combine it with other KT methods to explore the best
possible results. Last but not least, we fine-tune the model to other tasks
such as object detection. The results are also encouraging, which confirm the
transferability of the learned features. | [
"cs.CV",
"cs.LG",
"cs.NE"
] |
Accurate prediction of postoperative complications can inform shared
decisions between patients and surgeons regarding the appropriateness of
surgery, preoperative risk-reduction strategies, and postoperative resource
use. Traditional predictive analytic tools are hindered by suboptimal
performance and usability. We hypothesized that novel deep learning techniques
would outperform logistic regression models in predicting postoperative
complications. In a single-center longitudinal cohort of 43,943 adult patients
undergoing 52,529 major inpatient surgeries, deep learning yielded greater
discrimination than logistic regression for all nine complications. Predictive
performance was strongest when leveraging the full spectrum of preoperative and
intraoperative physiologic time-series electronic health record data. A single
multi-task deep learning model yielded greater performance than separate models
trained on individual complications. Integrated gradients interpretability
mechanisms demonstrated the substantial importance of missing data.
Interpretable, multi-task deep neural networks made accurate, patient-level
predictions that harbor the potential to augment surgical decision-making. | [
"cs.LG",
"stat.ML"
] |
We propose randomized least-squares value iteration (RLSVI) -- a new
reinforcement learning algorithm designed to explore and generalize efficiently
via linearly parameterized value functions. We explain why versions of
least-squares value iteration that use Boltzmann or epsilon-greedy exploration
can be highly inefficient, and we present computational results that
demonstrate dramatic efficiency gains enjoyed by RLSVI. Further, we establish
an upper bound on the expected regret of RLSVI that demonstrates
near-optimality in a tabula rasa learning context. More broadly, our results
suggest that randomized value functions offer a promising approach to tackling
a critical challenge in reinforcement learning: synthesizing efficient
exploration and effective generalization. | [
"stat.ML",
"cs.AI",
"cs.LG",
"cs.SY"
] |
The most common approaches to instance segmentation are complex and use
two-stage networks with object proposals, conditional random-fields, template
matching or recurrent neural networks. In this work we present TernausNetV2 - a
simple fully convolutional network that allows extracting objects from a
high-resolution satellite imagery on an instance level. The network has popular
encoder-decoder type of architecture with skip connections but has a few
essential modifications that allows using for semantic as well as for instance
segmentation tasks. This approach is universal and allows to extend any network
that has been successfully applied for semantic segmentation to perform
instance segmentation task. In addition, we generalize network encoder that was
pre-trained for RGB images to use additional input channels. It makes possible
to use transfer learning from visual to a wider spectral range. For
DeepGlobe-CVPR 2018 building detection sub-challenge, based on public
leaderboard score, our approach shows superior performance in comparison to
other methods. The source code corresponding pre-trained weights are publicly
available at https://github.com/ternaus/TernausNetV2 | [
"cs.CV"
] |
Scarcity of labeled data has motivated the development of semi-supervised
learning methods, which learn from large portions of unlabeled data alongside a
few labeled samples. Consistency Regularization between model's predictions
under different input perturbations, particularly has shown to provide
state-of-the art results in a semi-supervised framework. However, most of these
method have been limited to classification and segmentation applications. We
propose Transformation Consistency Regularization, which delves into a more
challenging setting of image-to-image translation, which remains unexplored by
semi-supervised algorithms. The method introduces a diverse set of geometric
transformations and enforces the model's predictions for unlabeled data to be
invariant to those transformations. We evaluate the efficacy of our algorithm
on three different applications: image colorization, denoising and
super-resolution. Our method is significantly data efficient, requiring only
around 10 - 20% of labeled samples to achieve similar image reconstructions to
its fully-supervised counterpart. Furthermore, we show the effectiveness of our
method in video processing applications, where knowledge from a few frames can
be leveraged to enhance the quality of the rest of the movie. | [
"cs.CV"
] |
Detection and classification of objects in overhead images are two important
and challenging problems in computer vision. Among various research areas in
this domain, the task of fine-grained classification of objects in overhead
images has become ubiquitous in diverse real-world applications, due to recent
advances in high-resolution satellite and airborne imaging systems. The small
inter-class variations and the large intra class variations caused by the fine
grained nature make it a challenging task, especially in low-resource cases. In
this paper, we introduce COFGA a new open dataset for the advancement of
fine-grained classification research. The 2,104 images in the dataset are
collected from an airborne imaging system at 5 15 cm ground sampling distance,
providing higher spatial resolution than most public overhead imagery datasets.
The 14,256 annotated objects in the dataset were classified into 2 classes, 15
subclasses, 14 unique features, and 8 perceived colors a total of 37 distinct
labels making it suitable to the task of fine-grained classification more than
any other publicly available overhead imagery dataset. We compare COFGA to
other overhead imagery datasets and then describe some distinguished fine-grain
classification approaches that were explored during an open data-science
competition we have conducted for this task. | [
"cs.CV"
] |
Learning to reliably perceive and understand the scene is an integral enabler
for robots to operate in the real-world. This problem is inherently challenging
due to the multitude of object types as well as appearance changes caused by
varying illumination and weather conditions. Leveraging complementary
modalities can enable learning of semantically richer representations that are
resilient to such perturbations. Despite the tremendous progress in recent
years, most multimodal convolutional neural network approaches directly
concatenate feature maps from individual modality streams rendering the model
incapable of focusing only on relevant complementary information for fusion. To
address this limitation, we propose a mutimodal semantic segmentation framework
that dynamically adapts the fusion of modality-specific features while being
sensitive to the object category, spatial location and scene context in a
self-supervised manner. Specifically, we propose an architecture consisting of
two modality-specific encoder streams that fuse intermediate encoder
representations into a single decoder using our proposed self-supervised model
adaptation fusion mechanism which optimally combines complementary features. As
intermediate representations are not aligned across modalities, we introduce an
attention scheme for better correlation. In addition, we propose a
computationally efficient unimodal segmentation architecture termed AdapNet++
that incorporates a new encoder with multiscale residual units and an efficient
atrous spatial pyramid pooling that has a larger effective receptive field with
more than 10x fewer parameters, complemented with a strong decoder with a
multi-resolution supervision scheme that recovers high-resolution details.
Comprehensive empirical evaluations on several benchmarks demonstrate that both
our unimodal and multimodal architectures achieve state-of-the-art performance. | [
"cs.CV"
] |
Benefiting from the capability of building inter-dependencies among channels
or spatial locations, attention mechanisms have been extensively studied and
broadly used in a variety of computer vision tasks recently. In this paper, we
investigate light-weight but effective attention mechanisms and present triplet
attention, a novel method for computing attention weights by capturing
cross-dimension interaction using a three-branch structure. For an input
tensor, triplet attention builds inter-dimensional dependencies by the rotation
operation followed by residual transformations and encodes inter-channel and
spatial information with negligible computational overhead. Our method is
simple as well as efficient and can be easily plugged into classic backbone
networks as an add-on module. We demonstrate the effectiveness of our method on
various challenging tasks including image classification on ImageNet-1k and
object detection on MSCOCO and PASCAL VOC datasets. Furthermore, we provide
extensive in-sight into the performance of triplet attention by visually
inspecting the GradCAM and GradCAM++ results. The empirical evaluation of our
method supports our intuition on the importance of capturing dependencies
across dimensions when computing attention weights. Code for this paper can be
publicly accessed at https://github.com/LandskapeAI/triplet-attention | [
"cs.CV"
] |
In this paper, we propose Push-SAGA, a decentralized stochastic first-order
method for finite-sum minimization over a directed network of nodes. Push-SAGA
combines node-level variance reduction to remove the uncertainty caused by
stochastic gradients, network-level gradient tracking to address the
distributed nature of the data, and push-sum consensus to tackle the challenge
of directed communication links. We show that Push-SAGA achieves linear
convergence to the exact solution for smooth and strongly convex problems and
is thus the first linearly-convergent stochastic algorithm over arbitrary
strongly connected directed graphs. We also characterize the regimes in which
Push-SAGA achieves a linear speed-up compared to its centralized counterpart
and achieves a network-independent convergence rate. We illustrate the behavior
and convergence properties of Push-SAGA with the help of numerical experiments
on strongly convex and non-convex problems. | [
"cs.LG",
"cs.DC",
"cs.MA",
"cs.SY",
"eess.SY",
"stat.ML"
] |
Multi-layered representation is believed to be the key ingredient of deep
neural networks especially in cognitive tasks like computer vision. While
non-differentiable models such as gradient boosting decision trees (GBDTs) are
the dominant methods for modeling discrete or tabular data, they are hard to
incorporate with such representation learning ability. In this work, we propose
the multi-layered GBDT forest (mGBDTs), with an explicit emphasis on exploring
the ability to learn hierarchical representations by stacking several layers of
regression GBDTs as its building block. The model can be jointly trained by a
variant of target propagation across layers, without the need to derive
back-propagation nor differentiability. Experiments and visualizations
confirmed the effectiveness of the model in terms of performance and
representation learning ability. | [
"cs.LG",
"stat.ML"
] |
A key limitation in using various modern methods of machine learning in
developing feedback control policies is the lack of appropriate methodologies
to analyze their long-term dynamics, in terms of making any sort of guarantees
(even statistically) about robustness. The central reasons for this are largely
due to the so-called curse of dimensionality, combined with the black-box
nature of the resulting control policies themselves. This paper aims at the
first of these issues. Although the full state space of a system may be quite
large in dimensionality, it is a common feature of most model-based control
methods that the resulting closed-loop systems demonstrate dominant dynamics
that are rapidly driven to some lower-dimensional sub-space within. In this
work we argue that the dimensionality of this subspace is captured by tools
from fractal geometry, namely various notions of a fractional dimension. We
then show that the dimensionality of trajectories induced by model free
reinforcement learning agents can be influenced adding a post processing
function to the agents reward signal. We verify that the dimensionality
reduction is robust to noise being added to the system and show that that the
modified agents are more actually more robust to noise and push disturbances in
general for the systems we examined. | [
"cs.LG",
"cs.RO"
] |
Segmentation of multiple surfaces in medical images is a challenging problem,
further complicated by the frequent presence of weak boundary and mutual
influence between adjacent objects. The traditional graph-based optimal surface
segmentation method has proven its effectiveness with its ability of capturing
various surface priors in a uniform graph model. However, its efficacy heavily
relies on handcrafted features that are used to define the surface cost for the
"goodness" of a surface. Recently, deep learning (DL) is emerging as powerful
tools for medical image segmentation thanks to its superior feature learning
capability. Unfortunately, due to the scarcity of training data in medical
imaging, it is nontrivial for DL networks to implicitly learn the global
structure of the target surfaces, including surface interactions. In this work,
we propose to parameterize the surface cost functions in the graph model and
leverage DL to learn those parameters. The multiple optimal surfaces are then
simultaneously detected by minimizing the total surface cost while explicitly
enforcing the mutual surface interaction constraints. The optimization problem
is solved by the primal-dual Internal Point Method, which can be implemented by
a layer of neural networks, enabling efficient end-to-end training of the whole
network. Experiments on Spectral Domain Optical Coherence Tomography (SD-OCT)
retinal layer segmentation and Intravascular Ultrasound (IVUS) vessel wall
segmentation demonstrated very promising results. All source code is public to
facilitate further research at this direction. | [
"cs.CV",
"cs.LG",
"I.2.1, I.4.6,"
] |
Datasets for autonomous cars are essential for the development and
benchmarking of perception systems. However, most existing datasets are
captured with camera and LiDAR sensors in good weather conditions. In this
paper, we present the RAdar Dataset In Adverse weaThEr (RADIATE), aiming to
facilitate research on object detection, tracking and scene understanding using
radar sensing for safe autonomous driving. RADIATE includes 3 hours of
annotated radar images with more than 200K labelled road actors in total, on
average about 4.6 instances per radar image. It covers 8 different categories
of actors in a variety of weather conditions (e.g., sun, night, rain, fog and
snow) and driving scenarios (e.g., parked, urban, motorway and suburban),
representing different levels of challenge. To the best of our knowledge, this
is the first public radar dataset which provides high-resolution radar images
on public roads with a large amount of road actors labelled. The data collected
in adverse weather, e.g., fog and snowfall, is unique. Some baseline results of
radar based object detection and recognition are given to show that the use of
radar data is promising for automotive applications in bad weather, where
vision and LiDAR can fail. RADIATE also has stereo images, 32-channel LiDAR and
GPS data, directed at other applications such as sensor fusion, localisation
and mapping. The public dataset can be accessed at
http://pro.hw.ac.uk/radiate/. | [
"cs.CV",
"cs.RO"
] |
The principle of optimism in the face of uncertainty is prevalent throughout
sequential decision making problems such as multi-armed bandits and
reinforcement learning (RL), often coming with strong theoretical guarantees.
However, it remains a challenge to scale these approaches to the deep RL
paradigm, which has achieved a great deal of attention in recent years. In this
paper, we introduce a tractable approach to optimism via noise augmented Markov
Decision Processes (MDPs), which we show can obtain a competitive regret bound:
$\tilde{\mathcal{O}}( |\mathcal{S}|H\sqrt{|\mathcal{S}||\mathcal{A}| T } )$
when augmenting using Gaussian noise, where $T$ is the total number of
environment steps. This tractability allows us to apply our approach to the
deep RL setting, where we rigorously evaluate the key factors for success of
optimistic model-based RL algorithms, bridging the gap between theory and
practice. | [
"cs.LG",
"stat.ML"
] |
A representation is supposed universal if it encodes any element of the
visual world (e.g., objects, scenes) in any configuration (e.g., scale,
context). While not expecting pure universal representations, the goal in the
literature is to improve the universality level, starting from a representation
with a certain level. To do so, the state-of-the-art consists in learning
CNN-based representations on a diversified training problem (e.g., ImageNet
modified by adding annotated data). While it effectively increases
universality, such approach still requires a large amount of efforts to satisfy
the needs in annotated data. In this work, we propose two methods to improve
universality, but pay special attention to limit the need of annotated data. We
also propose a unified framework of the methods based on the diversifying of
the training problem. Finally, to better match Atkinson's cognitive study about
universal human representations, we proposed to rely on the transfer-learning
scheme as well as a new metric to evaluate universality. This latter, aims us
to demonstrates the interest of our methods on 10 target-problems, relating to
the classification task and a variety of visual domains. | [
"cs.CV",
"cs.LG"
] |
LiDAR sensors can be used to obtain a wide range of measurement signals other
than a simple 3D point cloud, and those signals can be leveraged to improve
perception tasks like 3D object detection. A single laser pulse can be
partially reflected by multiple objects along its path, resulting in multiple
measurements called echoes. Multi-echo measurement can provide information
about object contours and semi-transparent surfaces which can be used to better
identify and locate objects. LiDAR can also measure surface reflectance
(intensity of laser pulse return), as well as ambient light of the scene
(sunlight reflected by objects). These signals are already available in
commercial LiDAR devices but have not been used in most LiDAR-based detection
models. We present a 3D object detection model which leverages the full
spectrum of measurement signals provided by LiDAR. First, we propose a
multi-signal fusion (MSF) module to combine (1) the reflectance and ambient
features extracted with a 2D CNN, and (2) point cloud features extracted using
a 3D graph neural network (GNN). Second, we propose a multi-echo aggregation
(MEA) module to combine the information encoded in different set of echo
points. Compared with traditional single echo point cloud methods, our proposed
Multi-Signal LiDAR Detector (MSLiD) extracts richer context information from a
wider range of sensing measurements and achieves more accurate 3D object
detection. Experiments show that by incorporating the multi-modality of LiDAR,
our method outperforms the state-of-the-art by up to 9.1%. | [
"cs.CV"
] |
Structure-based Deep Fusion models were recently shown to outperform several
physics- and machine learning-based protein-ligand binding affinity prediction
methods. As part of a multi-institutional COVID-19 pandemic response, over 500
million small molecules were computationally screened against four protein
structures from the novel coronavirus (SARS-CoV-2), which causes COVID-19.
Three enhancements to Deep Fusion were made in order to evaluate more than 5
billion docked poses on SARS-CoV-2 protein targets. First, the Deep Fusion
concept was refined by formulating the architecture as one, coherently
backpropagated model (Coherent Fusion) to improve binding-affinity prediction
accuracy. Secondly, the model was trained using a distributed, genetic
hyper-parameter optimization. Finally, a scalable, high-throughput screening
capability was developed to maximize the number of ligands evaluated and
expedite the path to experimental evaluation. In this work, we present both the
methods developed for machine learning-based high-throughput screening and
results from using our computational pipeline to find SARS-CoV-2 inhibitors. | [
"cs.LG",
"q-bio.BM"
] |
Healthcare sector is totally different from other industry. It is on high
priority sector and people expect highest level of care and services regardless
of cost. It did not achieve social expectation even though it consume huge
percentage of budget. Mostly the interpretations of medical data is being done
by medical expert. In terms of image interpretation by human expert, it is
quite limited due to its subjectivity, the complexity of the image, extensive
variations exist across different interpreters, and fatigue. After the success
of deep learning in other real world application, it is also providing exciting
solutions with good accuracy for medical imaging and is seen as a key method
for future applications in health secotr. In this chapter, we discussed state
of the art deep learning architecture and its optimization used for medical
image segmentation and classification. In the last section, we have discussed
the challenges deep learning based methods for medical imaging and open
research issue. | [
"cs.CV"
] |
Classic image scaling (e.g. bicubic) can be seen as one convolutional layer
and a single upscaling filter. Its implementation is ubiquitous in all display
devices and image processing software. In the last decade deep learning systems
have been introduced for the task of image super-resolution (SR), using several
convolutional layers and numerous filters. These methods have taken over the
benchmarks of image quality for upscaling tasks. Would it be possible to
replace classic upscalers with deep learning architectures on edge devices such
as display panels, tablets, laptop computers, etc.? On one hand, the current
trend in Edge-AI chips shows a promising future in this direction, with rapid
development of hardware that can run deep-learning tasks efficiently. On the
other hand, in image SR only few architectures have pushed the limit to extreme
small sizes that can actually run on edge devices at real-time. We explore
possible solutions to this problem with the aim to fill the gap between classic
upscalers and small deep learning configurations. As a transition from classic
to deep-learning upscaling we propose edge-SR (eSR), a set of one-layer
architectures that use interpretable mechanisms to upscale images. Certainly, a
one-layer architecture cannot reach the quality of deep learning systems.
Nevertheless, we find that for high speed requirements, eSR becomes better at
trading-off image quality and runtime performance. Filling the gap between
classic and deep-learning architectures for image upscaling is critical for
massive adoption of this technology. It is equally important to have an
interpretable system that can reveal the inner strategies to solve this problem
and guide us to future improvements and better understanding of larger
networks. | [
"cs.CV",
"cs.LG",
"eess.IV",
"eess.SP"
] |
Scoring functions (SFs), which measure the plausibility of triplets in
knowledge graph (KG), have become the crux of KG embedding. Lots of SFs, which
target at capturing different kinds of relations in KGs, have been designed by
humans in recent years. However, as relations can exhibit complex patterns that
are hard to infer before training, none of them can consistently perform better
than others on existing benchmark data sets. In this paper, inspired by the
recent success of automated machine learning (AutoML), we propose to
automatically design SFs (AutoSF) for distinct KGs by the AutoML techniques.
However, it is non-trivial to explore domain-specific information here to make
AutoSF efficient and effective. We firstly identify a unified representation
over popularly used SFs, which helps to set up a search space for AutoSF. Then,
we propose a greedy algorithm to search in such a space efficiently. The
algorithm is further sped up by a filter and a predictor, which can avoid
repeatedly training SFs with same expressive ability and help removing bad
candidates during the search before model training. Finally, we perform
extensive experiments on benchmark data sets. Results on link prediction and
triplets classification show that the searched SFs by AutoSF, are KG dependent,
new to the literature, and outperform the state-of-the-art SFs designed by
humans. | [
"cs.LG",
"stat.ML"
] |
Finetuning from a pretrained deep model is found to yield state-of-the-art
performance for many vision tasks. This paper investigates many factors that
influence the performance in finetuning for object detection. There is a
long-tailed distribution of sample numbers for classes in object detection. Our
analysis and empirical results show that classes with more samples have higher
impact on the feature learning. And it is better to make the sample number more
uniform across classes. Generic object detection can be considered as multiple
equally important tasks. Detection of each class is a task. These classes/tasks
have their individuality in discriminative visual appearance representation.
Taking this individuality into account, we cluster objects into visually
similar class groups and learn deep representations for these groups
separately. A hierarchical feature learning scheme is proposed. In this scheme,
the knowledge from the group with large number of classes is transferred for
learning features in its sub-groups. Finetuned on the GoogLeNet model,
experimental results show 4.7% absolute mAP improvement of our approach on the
ImageNet object detection dataset without increasing much computational cost at
the testing stage. | [
"cs.CV"
] |
Recently, graph neural networks (GNNs) have become an important and active
research direction in deep learning. It is worth noting that most of the
existing GNN-based methods learn graph representations within the Euclidean
vector space. Beyond the Euclidean space, learning representation and
embeddings in hyper-complex space have also shown to be a promising and
effective approach. To this end, we propose Quaternion Graph Neural Networks
(QGNN) to learn graph representations within the Quaternion space. As
demonstrated, the Quaternion space, a hyper-complex vector space, provides
highly meaningful computations and analogical calculus through Hamilton product
compared to the Euclidean and complex vector spaces. Our QGNN obtains
state-of-the-art results on a range of benchmark datasets for graph
classification and node classification. Besides, regarding knowledge graphs,
our QGNN-based embedding model achieves state-of-the-art results on three new
and challenging benchmark datasets for knowledge graph completion. Our code is
available at: \url{https://github.com/daiquocnguyen/QGNN}. | [
"cs.LG",
"stat.ML"
] |
Data efficiency and robustness to task-irrelevant perturbations are
long-standing challenges for deep reinforcement learning algorithms. Here we
introduce a modular approach to addressing these challenges in a continuous
control environment, without using hand-crafted or supervised information. Our
Curious Object-Based seaRch Agent (COBRA) uses task-free intrinsically
motivated exploration and unsupervised learning to build object-based models of
its environment and action space. Subsequently, it can learn a variety of tasks
through model-based search in very few steps and excel on structured hold-out
tests of policy robustness. | [
"cs.LG",
"cs.AI"
] |
Steel pipes are widely used in high-risk and high-pressure scenarios such as
oil, chemical, natural gas, shale gas, etc. If there is some defect in steel
pipes, it will lead to serious adverse consequences. Applying object detection
in the field of deep learning to pipe weld defect detection and identification
can effectively improve inspection efficiency and promote the development of
industrial automation. Most predecessors used traditional computer vision
methods applied to detect defects of steel pipe weld seams. However,
traditional computer vision methods rely on prior knowledge and can only detect
defects with a single feature, so it is difficult to complete the task of
multi-defect classification, while deep learning is end-to-end. In this paper,
the state-of-the-art single-stage object detection algorithm YOLOv5 is proposed
to be applied to the field of steel pipe weld defect detection, and compared
with the two-stage representative object detection algorithm Faster R-CNN. The
experimental results show that applying YOLOv5 to steel pipe weld defect
detection can greatly improve the accuracy, complete the multi-classification
task, and meet the criteria of real-time detection. | [
"cs.CV",
"cs.AI",
"68T07, 65D19",
"I.4.0; I.2.10"
] |
How much credit (or blame) should an action taken in a state get for a future
reward? This is the fundamental temporal credit assignment problem in
Reinforcement Learning (RL). One of the earliest and still most widely used
heuristics is to assign this credit based on a scalar coefficient $\lambda$
(treated as a hyperparameter) raised to the power of the time interval between
the state-action and the reward. In this empirical paper, we explore heuristics
based on more general pairwise weightings that are functions of the state in
which the action was taken, the state at the time of the reward, as well as the
time interval between the two. Of course it isn't clear what these pairwise
weight functions should be, and because they are too complex to be treated as
hyperparameters we develop a metagradient procedure for learning these weight
functions during the usual RL training of a policy. Our empirical work shows
that it is often possible to learn these pairwise weight functions during
learning of the policy to achieve better performance than competing approaches. | [
"cs.LG",
"cs.AI"
] |
The Wasserstein distance and its variations, e.g., the sliced-Wasserstein
(SW) distance, have recently drawn attention from the machine learning
community. The SW distance, specifically, was shown to have similar properties
to the Wasserstein distance, while being much simpler to compute, and is
therefore used in various applications including generative modeling and
general supervised/unsupervised learning. In this paper, we first clarify the
mathematical connection between the SW distance and the Radon transform. We
then utilize the generalized Radon transform to define a new family of
distances for probability measures, which we call generalized
sliced-Wasserstein (GSW) distances. We also show that, similar to the SW
distance, the GSW distance can be extended to a maximum GSW (max-GSW) distance.
We then provide the conditions under which GSW and max-GSW distances are indeed
distances. Finally, we compare the numerical performance of the proposed
distances on several generative modeling tasks, including SW flows and SW
auto-encoders. | [
"cs.LG",
"stat.ML"
] |
Manipulating data, such as weighting data examples or augmenting with new
instances, has been increasingly used to improve model training. Previous work
has studied various rule- or learning-based approaches designed for specific
types of data manipulation. In this work, we propose a new method that supports
learning different manipulation schemes with the same gradient-based algorithm.
Our approach builds upon a recent connection of supervised learning and
reinforcement learning (RL), and adapts an off-the-shelf reward learning
algorithm from RL for joint data manipulation learning and model training.
Different parameterization of the "data reward" function instantiates different
manipulation schemes. We showcase data augmentation that learns a text
transformation network, and data weighting that dynamically adapts the data
sample importance. Experiments show the resulting algorithms significantly
improve the image and text classification performance in low data regime and
class-imbalance problems. | [
"cs.LG",
"cs.CL",
"cs.CV",
"stat.ML"
] |
State-of-the-art transformer models use pairwise dot-product based
self-attention, which comes at a computational cost quadratic in the input
sequence length. In this paper, we investigate the global structure of
attention scores computed using this dot product mechanism on a typical
distribution of inputs, and study the principal components of their variation.
Through eigen analysis of full attention score matrices, as well as of their
individual rows, we find that most of the variation among attention scores lie
in a low-dimensional eigenspace. Moreover, we find significant overlap between
these eigenspaces for different layers and even different transformer models.
Based on this, we propose to compute scores only for a partial subset of token
pairs, and use them to estimate scores for the remaining pairs. Beyond
investigating the accuracy of reconstructing attention scores themselves, we
investigate training transformer models that employ these approximations, and
analyze the effect on overall accuracy. Our analysis and the proposed method
provide insights into how to balance the benefits of exact pair-wise attention
and its significant computational expense. | [
"cs.LG"
] |
Actor-critic algorithms are widely used in reinforcement learning, but are
challenging to mathematically analyze due to the online arrival of non-i.i.d.
data samples. The distribution of the data samples dynamically changes as the
model is updated, introducing a complex feedback loop between the data
distribution and the reinforcement learning algorithm. We prove that, under a
time rescaling, the online actor-critic algorithm with tabular parametrization
converges to an ordinary differential equations (ODEs) as the number of updates
becomes large. The proof first establishes the geometric ergodicity of the data
samples under a fixed actor policy. Then, using a Poisson equation, we prove
that the fluctuations of the data samples around a dynamic probability measure,
which is a function of the evolving actor model, vanish as the number of
updates become large. Once the ODE limit has been derived, we study its
convergence properties using a two time-scale analysis which asymptotically
de-couples the critic ODE from the actor ODE. The convergence of the critic to
the solution of the Bellman equation and the actor to the optimal policy are
proven. In addition, a convergence rate to this global minimum is also
established. Our convergence analysis holds under specific choices for the
learning rates and exploration rates in the actor-critic algorithm, which could
provide guidance for the implementation of actor-critic algorithms in practice. | [
"cs.LG",
"math.OC",
"stat.ML"
] |
Estimating geometric elements such as depth, camera motion, and optical flow
from images is an important part of the robot's visual perception. We use a
joint self-supervised method to estimate the three geometric elements. Depth
network, optical flow network and camera motion network are independent of each
other but are jointly optimized during training phase. Compared with
independent training, joint training can make full use of the geometric
relationship between geometric elements and provide dynamic and static
information of the scene. In this paper, we improve the joint self-supervision
method from three aspects: network structure, dynamic object segmentation, and
geometric constraints. In terms of network structure, we apply the attention
mechanism to the camera motion network, which helps to take advantage of the
similarity of camera movement between frames. And according to attention
mechanism in Transformer, we propose a plug-and-play convolutional attention
module. In terms of dynamic object, according to the different influences of
dynamic objects in the optical flow self-supervised framework and the
depth-pose self-supervised framework, we propose a threshold algorithm to
detect dynamic regions, and mask that in the loss function respectively. In
terms of geometric constraints, we use traditional methods to estimate the
fundamental matrix from the corresponding points to constrain the camera motion
network. We demonstrate the effectiveness of our method on the KITTI dataset.
Compared with other joint self-supervised methods, our method achieves
state-of-the-art performance in the estimation of pose and optical flow, and
the depth estimation has also achieved competitive results. Code will be
available https://github.com/jianfenglihg/Unsupervised_geometry. | [
"cs.CV",
"65Dxx"
] |
Nonnegative matrix factorization (NMF) has been successfully applied in
several data mining tasks. Recently, there is an increasing interest in the
acceleration of NMF, due to its high cost on large matrices. On the other hand,
the privacy issue of NMF over federated data is worthy of attention, since NMF
is prevalently applied in image and text analysis which may involve leveraging
privacy data (e.g, medical image and record) across several parties (e.g.,
hospitals). In this paper, we study the acceleration and security problems of
distributed NMF. Firstly, we propose a distributed sketched alternating
nonnegative least squares (DSANLS) framework for NMF, which utilizes a matrix
sketching technique to reduce the size of nonnegative least squares subproblems
with a convergence guarantee. For the second problem, we show that DSANLS with
modification can be adapted to the security setting, but only for one or
limited iterations. Consequently, we propose four efficient distributed NMF
methods in both synchronous and asynchronous settings with a security
guarantee. We conduct extensive experiments on several real datasets to show
the superiority of our proposed methods. The implementation of our methods is
available at https://github.com/qianyuqiu79/DSANLS. | [
"cs.LG",
"cs.CR",
"cs.DC",
"stat.ML"
] |
The central challenge in automated synthesis planning is to be able to
generate and predict outcomes of a diverse set of chemical reactions. In
particular, in many cases, the most likely synthesis pathway cannot be applied
due to additional constraints, which requires proposing alternative chemical
reactions. With this in mind, we present Molecule Edit Graph Attention Network
(MEGAN), an end-to-end encoder-decoder neural model. MEGAN is inspired by
models that express a chemical reaction as a sequence of graph edits, akin to
the arrow pushing formalism. We extend this model to retrosynthesis prediction
(predicting substrates given the product of a chemical reaction) and scale it
up to large datasets. We argue that representing the reaction as a sequence of
edits enables MEGAN to efficiently explore the space of plausible chemical
reactions, maintaining the flexibility of modeling the reaction in an
end-to-end fashion, and achieving state-of-the-art accuracy in standard
benchmarks. Code and trained models are made available online at
https://github.com/molecule-one/megan. | [
"cs.LG",
"physics.chem-ph",
"stat.ML"
] |
Single image dehazing is a challenging ill-posed problem due to the severe
information degeneration. However, existing deep learning based dehazing
methods only adopt clear images as positive samples to guide the training of
dehazing network while negative information is unexploited. Moreover, most of
them focus on strengthening the dehazing network with an increase of depth and
width, leading to a significant requirement of computation and memory. In this
paper, we propose a novel contrastive regularization (CR) built upon
contrastive learning to exploit both the information of hazy images and clear
images as negative and positive samples, respectively. CR ensures that the
restored image is pulled to closer to the clear image and pushed to far away
from the hazy image in the representation space. Furthermore, considering
trade-off between performance and memory storage, we develop a compact dehazing
network based on autoencoder-like (AE) framework. It involves an adaptive mixup
operation and a dynamic feature enhancement module, which can benefit from
preserving information flow adaptively and expanding the receptive field to
improve the network's transformation capability, respectively. We term our
dehazing network with autoencoder and contrastive regularization as AECR-Net.
The extensive experiments on synthetic and real-world datasets demonstrate that
our AECR-Net surpass the state-of-the-art approaches. The code is released in
https://github.com/GlassyWu/AECR-Net. | [
"cs.CV",
"cs.AI"
] |
Graph Neural Nets (GNNs) have received increasing attentions, partially due
to their superior performance in many node and graph classification tasks.
However, there is a lack of understanding on what they are learning and how
sophisticated the learned graph functions are. In this work, we propose a
dissection of GNNs on graph classification into two parts: 1) the graph
filtering, where graph-based neighbor aggregations are performed, and 2) the
set function, where a set of hidden node features are composed for prediction.
To study the importance of both parts, we propose to linearize them separately.
We first linearize the graph filtering function, resulting Graph Feature
Network (GFN), which is a simple lightweight neural net defined on a
\textit{set} of graph augmented features. Further linearization of GFN's set
function results in Graph Linear Network (GLN), which is a linear function.
Empirically we perform evaluations on common graph classification benchmarks.
To our surprise, we find that, despite the simplification, GFN could match or
exceed the best accuracies produced by recently proposed GNNs (with a fraction
of computation cost), while GLN underperforms significantly. Our results
demonstrate the importance of non-linear set function, and suggest that linear
graph filtering with non-linear set function is an efficient and powerful
scheme for modeling existing graph classification benchmarks. | [
"cs.LG",
"cs.SI",
"stat.ML"
] |
Many approaches have recently been proposed to detect irregular scene text
and achieved promising results. However, their localization results may not
well satisfy the following text recognition part mainly because of two reasons:
1) recognizing arbitrary shaped text is still a challenging task, and 2)
prevalent non-trainable pipeline strategies between text detection and text
recognition will lead to suboptimal performances. To handle this
incompatibility problem, in this paper we propose an end-to-end trainable text
spotting approach named Text Perceptron. Concretely, Text Perceptron first
employs an efficient segmentation-based text detector that learns the latent
text reading order and boundary information. Then a novel Shape Transform
Module (abbr. STM) is designed to transform the detected feature regions into
regular morphologies without extra parameters. It unites text detection and the
following recognition part into a whole framework, and helps the whole network
achieve global optimization. Experiments show that our method achieves
competitive performance on two standard text benchmarks, i.e., ICDAR 2013 and
ICDAR 2015, and also obviously outperforms existing methods on irregular text
benchmarks SCUT-CTW1500 and Total-Text. | [
"cs.CV"
] |
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