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In recent years, deep learning has rapidly become a method of choice for the
segmentation of medical images. Deep Neural Network (DNN) architectures such as
UNet have achieved state-of-the-art results on many medical datasets. To
further improve the performance in the segmentation task, we develop an
ensemble system which combines various deep learning architectures. We propose
a two-layer ensemble of deep learning models for the segmentation of medical
images. The prediction for each training image pixel made by each model in the
first layer is used as the augmented data of the training image for the second
layer of the ensemble. The prediction of the second layer is then combined by
using a weights-based scheme in which each model contributes differently to the
combined result. The weights are found by solving linear regression problems.
Experiments conducted on two popular medical datasets namely CAMUS and
Kvasir-SEG show that the proposed method achieves better results concerning two
performance metrics (Dice Coefficient and Hausdorff distance) compared to some
well-known benchmark algorithms. | [
"cs.CV"
] |
We introduce a self-supervised method for learning visual correspondence from
unlabeled video. The main idea is to use cycle-consistency in time as free
supervisory signal for learning visual representations from scratch. At
training time, our model learns a feature map representation to be useful for
performing cycle-consistent tracking. At test time, we use the acquired
representation to find nearest neighbors across space and time. We demonstrate
the generalizability of the representation -- without finetuning -- across a
range of visual correspondence tasks, including video object segmentation,
keypoint tracking, and optical flow. Our approach outperforms previous
self-supervised methods and performs competitively with strongly supervised
methods. | [
"cs.CV",
"cs.AI",
"cs.LG"
] |
Data augmentation is an effective technique to improve the generalization of
deep neural networks. However, previous data augmentation methods usually treat
the augmented samples equally without considering their individual impacts on
the model. To address this, for the augmented samples from the same training
example, we propose to assign different weights to them. We construct the
maximal expected loss which is the supremum over any reweighted loss on
augmented samples. Inspired by adversarial training, we minimize this maximal
expected loss (MMEL) and obtain a simple and interpretable closed-form
solution: more attention should be paid to augmented samples with large loss
values (i.e., harder examples). Minimizing this maximal expected loss enables
the model to perform well under any reweighting strategy. The proposed method
can generally be applied on top of any data augmentation methods. Experiments
are conducted on both natural language understanding tasks with token-level
data augmentation, and image classification tasks with commonly-used image
augmentation techniques like random crop and horizontal flip. Empirical results
show that the proposed method improves the generalization performance of the
model. | [
"cs.LG"
] |
Single-view depth estimation suffers from the problem that a network trained
on images from one camera does not generalize to images taken with a different
camera model. Thus, changing the camera model requires collecting an entirely
new training dataset. In this work, we propose a new type of convolution that
can take the camera parameters into account, thus allowing neural networks to
learn calibration-aware patterns. Experiments confirm that this improves the
generalization capabilities of depth prediction networks considerably, and
clearly outperforms the state of the art when the train and test images are
acquired with different cameras. | [
"cs.CV"
] |
A lack of generalizability is one key limitation of deep learning based
segmentation. Typically, one manually labels new training images when
segmenting organs in different imaging modalities or segmenting abnormal organs
from distinct disease cohorts. The manual efforts can be alleviated if one is
able to reuse manual labels from one modality (e.g., MRI) to train a
segmentation network for a new modality (e.g., CT). Previously, two stage
methods have been proposed to use cycle generative adversarial networks
(CycleGAN) to synthesize training images for a target modality. Then, these
efforts trained a segmentation network independently using synthetic images.
However, these two independent stages did not use the complementary information
between synthesis and segmentation. Herein, we proposed a novel end-to-end
synthesis and segmentation network (EssNet) to achieve the unpaired MRI to CT
image synthesis and CT splenomegaly segmentation simultaneously without using
manual labels on CT. The end-to-end EssNet achieved significantly higher median
Dice similarity coefficient (0.9188) than the two stages strategy (0.8801), and
even higher than canonical multi-atlas segmentation (0.9125) and ResNet method
(0.9107), which used the CT manual labels. | [
"cs.CV"
] |
Image-to-image (i2i) translation is the dense regression problem of learning
how to transform an input image into an output using aligned image pairs.
Remarkable progress has been made in i2i translation with the advent of Deep
Convolutional Neural Networks (DCNNs) and particular using the learning
paradigm of Generative Adversarial Networks (GANs). In the absence of paired
images, i2i translation is tackled with one or multiple domain transformations
(i.e., CycleGAN, StarGAN etc.). In this paper, we study a new problem, that of
image-to-image translation, under a set of continuous parameters that
correspond to a model describing a physical process. In particular, we propose
the SliderGAN which transforms an input face image into a new one according to
the continuous values of a statistical blendshape model of facial motion. We
show that it is possible to edit a facial image according to expression and
speech blendshapes, using sliders that control the continuous values of the
blendshape model. This provides much more flexibility in various tasks,
including but not limited to face editing, expression transfer and face
neutralisation, comparing to models based on discrete expressions or action
units. | [
"cs.CV"
] |
Recently, directly detecting 3D objects from 3D point clouds has received
increasing attention. To extract object representation from an irregular point
cloud, existing methods usually take a point grouping step to assign the points
to an object candidate so that a PointNet-like network could be used to derive
object features from the grouped points. However, the inaccurate point
assignments caused by the hand-crafted grouping scheme decrease the performance
of 3D object detection.
In this paper, we present a simple yet effective method for directly
detecting 3D objects from the 3D point cloud. Instead of grouping local points
to each object candidate, our method computes the feature of an object from all
the points in the point cloud with the help of an attention mechanism in the
Transformers \cite{vaswani2017attention}, where the contribution of each point
is automatically learned in the network training. With an improved attention
stacking scheme, our method fuses object features in different stages and
generates more accurate object detection results. With few bells and whistles,
the proposed method achieves state-of-the-art 3D object detection performance
on two widely used benchmarks, ScanNet V2 and SUN RGB-D. The code and models
are publicly available at \url{https://github.com/zeliu98/Group-Free-3D} | [
"cs.CV"
] |
Combinatorial Optimization (CO) has been a long-standing challenging research
topic featured by its NP-hard nature. Traditionally such problems are
approximately solved with heuristic algorithms which are usually fast but may
sacrifice the solution quality. Currently, machine learning for combinatorial
optimization (MLCO) has become a trending research topic, but most existing
MLCO methods treat CO as a single-level optimization by directly learning the
end-to-end solutions, which are hard to scale up and mostly limited by the
capacity of ML models given the high complexity of CO. In this paper, we
propose a hybrid approach to combine the best of the two worlds, in which a
bi-level framework is developed with an upper-level learning method to optimize
the graph (e.g. add, delete or modify edges in a graph), fused with a
lower-level heuristic algorithm solving on the optimized graph. Such a bi-level
approach simplifies the learning on the original hard CO and can effectively
mitigate the demand for model capacity. The experiments and results on several
popular CO problems like Directed Acyclic Graph scheduling, Graph Edit Distance
and Hamiltonian Cycle Problem show its effectiveness over manually designed
heuristics and single-level learning methods. | [
"cs.LG",
"math.CO"
] |
Photorealism is a complex concept that cannot easily be formulated
mathematically. Deep Photo Style Transfer is an attempt to transfer the style
of a reference image to a content image while preserving its photorealism. This
is achieved by introducing a constraint that prevents distortions in the
content image and by applying the style transfer independently for semantically
different parts of the images. In addition, an automated segmentation process
is presented that consists of a neural network based segmentation method
followed by a semantic grouping step. To further improve the results a measure
for image aesthetics is used and elaborated. If the content and the style image
are sufficiently similar, the result images look very realistic. With the
automation of the image segmentation the pipeline becomes completely
independent from any user interaction, which allows for new applications. | [
"cs.CV"
] |
Person re-identification (Re-ID) via gait features within 3D skeleton
sequences is a newly-emerging topic with several advantages. Existing solutions
either rely on hand-crafted descriptors or supervised gait representation
learning. This paper proposes a self-supervised gait encoding approach that can
leverage unlabeled skeleton data to learn gait representations for person
Re-ID. Specifically, we first create self-supervision by learning to
reconstruct unlabeled skeleton sequences reversely, which involves richer
high-level semantics to obtain better gait representations. Other pretext tasks
are also explored to further improve self-supervised learning. Second, inspired
by the fact that motion's continuity endows adjacent skeletons in one skeleton
sequence and temporally consecutive skeleton sequences with higher correlations
(referred as locality in 3D skeleton data), we propose a locality-aware
attention mechanism and a locality-aware contrastive learning scheme, which aim
to preserve locality-awareness on intra-sequence level and inter-sequence level
respectively during self-supervised learning. Last, with context vectors
learned by our locality-aware attention mechanism and contrastive learning
scheme, a novel feature named Constrastive Attention-based Gait Encodings
(CAGEs) is designed to represent gait effectively. Empirical evaluations show
that our approach significantly outperforms skeleton-based counterparts by
15-40% Rank-1 accuracy, and it even achieves superior performance to numerous
multi-modal methods with extra RGB or depth information. Our codes are
available at https://github.com/Kali-Hac/Locality-Awareness-SGE. | [
"cs.CV",
"cs.LG"
] |
A growing branch of computer vision is object detection. Object detection is
used in many applications such as industrial process, medical imaging analysis,
and autonomous vehicles. The ability to detect objects in videos is crucial.
Object detection systems are trained on large image datasets. For applications
such as autonomous vehicles, it is crucial that the object detection system can
identify objects through multiple frames in video. There are many problems with
applying these systems to video. Shadows or changes in brightness that can
cause the system to incorrectly identify objects frame to frame and cause an
unintended system response. There are many neural networks that have been used
for object detection and if there was a way of connecting objects between
frames then these problems could be eliminated. For these neural networks to
get better at identifying objects in video, they need to be re-trained. A
dataset must be created with images that represent consecutive video frames and
have matching ground-truth layers. A method is proposed that can generate these
datasets. The ground-truth layer contains only moving objects. To generate this
layer, FlowNet2-Pytorch was used to create the flow mask using the novel
Magnitude Method. As well, a segmentation mask will be generated using networks
such as Mask R-CNN or Refinenet. These segmentation masks will contain all
objects detected in a frame. By comparing this segmentation mask to the flow
mask ground-truth layer, a loss function is generated. This loss function can
be used to train a neural network to be better at making consistent predictions
on video. The system was tested on multiple video samples and a loss was
generated for each frame, proving the Magnitude Method's ability to be used to
train object detection neural networks in future work. | [
"cs.CV",
"eess.IV"
] |
Given a query patch from a novel class, one-shot object detection aims to
detect all instances of that class in a target image through the semantic
similarity comparison. However, due to the extremely limited guidance in the
novel class as well as the unseen appearance difference between query and
target instances, it is difficult to appropriately exploit their semantic
similarity and generalize well. To mitigate this problem, we present a
universal Cross-Attention Transformer (CAT) module for accurate and efficient
semantic similarity comparison in one-shot object detection. The proposed CAT
utilizes transformer mechanism to comprehensively capture bi-directional
correspondence between any paired pixels from the query and the target image,
which empowers us to sufficiently exploit their semantic characteristics for
accurate similarity comparison. In addition, the proposed CAT enables feature
dimensionality compression for inference speedup without performance loss.
Extensive experiments on COCO, VOC, and FSOD under one-shot settings
demonstrate the effectiveness and efficiency of our method, e.g., it surpasses
CoAE, a major baseline in this task by 1.0% in AP on COCO and runs nearly 2.5
times faster. Code will be available in the future. | [
"cs.CV"
] |
Time series data that are not measured at regular intervals are commonly
discretized as a preprocessing step. For example, data about customer arrival
times might be simplified by summing the number of arrivals within hourly
intervals, which produces a discrete-time time series that is easier to model.
In this abstract, we show that discretization introduces a bias that affects
models trained for decision-making. We refer to this phenomenon as
discretization bias, and show that we can avoid it by using continuous-time
models instead. | [
"stat.ML",
"cs.AI",
"cs.LG",
"cs.SY"
] |
Vehicle re-identification (reID) is to identify a target vehicle in different
cameras with non-overlapping views. When deploy the well-trained model to a new
dataset directly, there is a severe performance drop because of differences
among datasets named domain bias. To address this problem, this paper proposes
an domain adaptation framework which contains an image-to-image translation
network named vehicle transfer generative adversarial network (VTGAN) and an
attention-based feature learning network (ATTNet). VTGAN could make images from
the source domain (well-labeled) have the style of target domain (unlabeled)
and preserve identity information of source domain. To further improve the
domain adaptation ability for various backgrounds, ATTNet is proposed to train
generated images with the attention structure for vehicle reID. Comprehensive
experimental results clearly demonstrate that our method achieves excellent
performance on VehicleID dataset. | [
"cs.CV"
] |
Goal-conditioned hierarchical reinforcement learning (HRL) is a promising
approach for scaling up reinforcement learning (RL) techniques. However, it
often suffers from training inefficiency as the action space of the high-level,
i.e., the goal space, is often large. Searching in a large goal space poses
difficulties for both high-level subgoal generation and low-level policy
learning. In this paper, we show that this problem can be effectively
alleviated by restricting the high-level action space from the whole goal space
to a $k$-step adjacent region of the current state using an adjacency
constraint. We theoretically prove that the proposed adjacency constraint
preserves the optimal hierarchical policy in deterministic MDPs, and show that
this constraint can be practically implemented by training an adjacency network
that can discriminate between adjacent and non-adjacent subgoals. Experimental
results on discrete and continuous control tasks show that incorporating the
adjacency constraint improves the performance of state-of-the-art HRL
approaches in both deterministic and stochastic environments. | [
"cs.LG",
"stat.ML"
] |
Traditional techniques for emotion recognition have focused on the facial
expression analysis only, thus providing limited ability to encode context that
comprehensively represents the emotional responses. We present deep networks
for context-aware emotion recognition, called CAER-Net, that exploit not only
human facial expression but also context information in a joint and boosting
manner. The key idea is to hide human faces in a visual scene and seek other
contexts based on an attention mechanism. Our networks consist of two
sub-networks, including two-stream encoding networks to seperately extract the
features of face and context regions, and adaptive fusion networks to fuse such
features in an adaptive fashion. We also introduce a novel benchmark for
context-aware emotion recognition, called CAER, that is more appropriate than
existing benchmarks both qualitatively and quantitatively. On several
benchmarks, CAER-Net proves the effect of context for emotion recognition. Our
dataset is available at http://caer-dataset.github.io. | [
"cs.CV",
"cs.HC",
"cs.MM"
] |
Deep learning approaches have achieved state-of-the-art performance in
cardiac magnetic resonance (CMR) image segmentation. However, most approaches
have focused on learning image intensity features for segmentation, whereas the
incorporation of anatomical shape priors has received less attention. In this
paper, we combine a multi-task deep learning approach with atlas propagation to
develop a shape-constrained bi-ventricular segmentation pipeline for short-axis
CMR volumetric images. The pipeline first employs a fully convolutional network
(FCN) that learns segmentation and landmark localisation tasks simultaneously.
The architecture of the proposed FCN uses a 2.5D representation, thus combining
the computational advantage of 2D FCNs networks and the capability of
addressing 3D spatial consistency without compromising segmentation accuracy.
Moreover, the refinement step is designed to explicitly enforce a shape
constraint and improve segmentation quality. This step is effective for
overcoming image artefacts (e.g. due to different breath-hold positions and
large slice thickness), which preclude the creation of anatomically meaningful
3D cardiac shapes. The proposed pipeline is fully automated, due to network's
ability to infer landmarks, which are then used downstream in the pipeline to
initialise atlas propagation. We validate the pipeline on 1831 healthy subjects
and 649 subjects with pulmonary hypertension. Extensive numerical experiments
on the two datasets demonstrate that our proposed method is robust and capable
of producing accurate, high-resolution and anatomically smooth bi-ventricular
3D models, despite the artefacts in input CMR volumes. | [
"cs.CV",
"cs.AI"
] |
In traffic forecasting, graph convolutional networks (GCNs), which model
traffic flows as spatio-temporal graphs, have achieved remarkable performance.
However, existing GCN-based methods heuristically define the graph structure as
the physical topology of the road network, ignoring potential dependence of the
graph structure over traffic data. And the defined graph structure is
deterministic, which lacks investigation of uncertainty. In this paper, we
propose a Bayesian Spatio-Temporal Graph Convolutional Network (BSTGCN) for
traffic prediction. The graph structure in our network is learned from the
physical topology of the road network and traffic data in an end-to-end manner,
which discovers a more accurate description of the relationship among traffic
flows. Moreover, a parametric generative model is proposed to represent the
graph structure, which enhances the generalization capability of GCNs. We
verify the effectiveness of our method on two real-world datasets, and the
experimental results demonstrate that BSTGCN attains superior performance
compared with state-of-the-art methods. | [
"cs.LG",
"cs.GR"
] |
We propose a robust implementation of the Nerlove--Arrow model using a
Bayesian structural time series model to explain the relationship between
advertising expenditures of a country-wide fast-food franchise network with its
weekly sales. Thanks to the flexibility and modularity of the model, it is well
suited to generalization to other markets or situations. Its Bayesian nature
facilitates incorporating \emph{a priori} information (the manager's views),
which can be updated with relevant data. This aspect of the model will be used
to present a strategy of budget scheduling across time and channels. | [
"stat.ML",
"econ.EM",
"q-fin.RM",
"stat.AP"
] |
The deep two-stream architecture exhibited excellent performance on video
based action recognition. The most computationally expensive step in this
approach comes from the calculation of optical flow which prevents it to be
real-time. This paper accelerates this architecture by replacing optical flow
with motion vector which can be obtained directly from compressed videos
without extra calculation. However, motion vector lacks fine structures, and
contains noisy and inaccurate motion patterns, leading to the evident
degradation of recognition performance. Our key insight for relieving this
problem is that optical flow and motion vector are inherent correlated.
Transferring the knowledge learned with optical flow CNN to motion vector CNN
can significantly boost the performance of the latter. Specifically, we
introduce three strategies for this, initialization transfer, supervision
transfer and their combination. Experimental results show that our method
achieves comparable recognition performance to the state-of-the-art, while our
method can process 390.7 frames per second, which is 27 times faster than the
original two-stream method. | [
"cs.CV"
] |
With the rise in edge-computing devices, there has been an increasing demand
to deploy energy and resource-efficient models. A large body of research has
been devoted to developing methods that can reduce the size of the model
considerably without affecting the standard metrics such as top-1 accuracy.
However, these pruning approaches tend to result in a significant mismatch in
other metrics such as fairness across classes and explainability. To combat
such misalignment, we propose a novel multi-part loss function inspired by the
knowledge-distillation literature. Through extensive experiments, we
demonstrate the effectiveness of our approach across different compression
algorithms, architectures, tasks as well as datasets. In particular, we obtain
up to $4.1\times$ reduction in the number of prediction mismatches between the
compressed and reference models, and up to $5.7\times$ in cases where the
reference model makes the correct prediction; all while making no changes to
the compression algorithm, and minor modifications to the loss function.
Furthermore, we demonstrate how inducing simple alignment between the
predictions of the models naturally improves the alignment on other metrics
including fairness and attributions. Our framework can thus serve as a simple
plug-and-play component for compression algorithms in the future. | [
"cs.CV",
"cs.AI",
"cs.LG"
] |
Detecting small objects over large areas remains a significant challenge in
satellite imagery analytics. Among the challenges is the sheer number of pixels
and geographical extent per image: a single DigitalGlobe satellite image
encompasses over 64 km2 and over 250 million pixels. Another challenge is that
objects of interest are often minuscule (~pixels in extent even for the highest
resolution imagery), which complicates traditional computer vision techniques.
To address these issues, we propose a pipeline (SIMRDWN) that evaluates
satellite images of arbitrarily large size at native resolution at a rate of >
0.2 km2/s. Building upon the tensorflow object detection API paper, this
pipeline offers a unified approach to multiple object detection frameworks that
can run inference on images of arbitrary size. The SIMRDWN pipeline includes a
modified version of YOLO (known as YOLT), along with the models of the
tensorflow object detection API: SSD, Faster R-CNN, and R-FCN. The proposed
approach allows comparison of the performance of these four frameworks, and can
rapidly detect objects of vastly different scales with relatively little
training data over multiple sensors. For objects of very different scales (e.g.
airplanes versus airports) we find that using two different detectors at
different scales is very effective with negligible runtime cost.We evaluate
large test images at native resolution and find mAP scores of 0.2 to 0.8 for
vehicle localization, with the YOLT architecture achieving both the highest mAP
and fastest inference speed. | [
"cs.CV"
] |
Drawing an inspiration from behavioral studies of human decision making, we
propose here a general parametric framework for a reinforcement learning
problem, which extends the standard Q-learning approach to incorporate a
two-stream framework of reward processing with biases biologically associated
with several neurological and psychiatric conditions, including Parkinson's and
Alzheimer's diseases, attention-deficit/hyperactivity disorder (ADHD),
addiction, and chronic pain. For AI community, the development of agents that
react differently to different types of rewards can enable us to understand a
wide spectrum of multi-agent interactions in complex real-world socioeconomic
systems. Moreover, from the behavioral modeling perspective, our parametric
framework can be viewed as a first step towards a unifying computational model
capturing reward processing abnormalities across multiple mental conditions and
user preferences in long-term recommendation systems. | [
"cs.LG",
"cs.AI",
"cs.MA",
"q-bio.NC",
"stat.ML"
] |
Security, privacy, and fairness have become critical in the era of data
science and machine learning. More and more we see that achieving universally
secure, private, and fair systems is practically impossible. We have seen for
example how generative adversarial networks can be used to learn about the
expected private training data; how the exploitation of additional data can
reveal private information in the original one; and how what looks like
unrelated features can teach us about each other. Confronted with this
challenge, in this paper we open a new line of research, where the security,
privacy, and fairness is learned and used in a closed environment. The goal is
to ensure that a given entity (e.g., the company or the government), trusted to
infer certain information with our data, is blocked from inferring protected
information from it. For example, a hospital might be allowed to produce
diagnosis on the patient (the positive task), without being able to infer the
gender of the subject (negative task). Similarly, a company can guarantee that
internally it is not using the provided data for any undesired task, an
important goal that is not contradicting the virtually impossible challenge of
blocking everybody from the undesired task. We design a system that learns to
succeed on the positive task while simultaneously fail at the negative one, and
illustrate this with challenging cases where the positive task is actually
harder than the negative one being blocked. Fairness, to the information in the
negative task, is often automatically obtained as a result of this proposed
approach. The particular framework and examples open the door to security,
privacy, and fairness in very important closed scenarios, ranging from private
data accumulation companies like social networks to law-enforcement and
hospitals. | [
"stat.ML",
"cs.LG"
] |
This paper presents a novel personal identification and verification system
using information extracted from the hand shape and texture. The system has two
major constituent modules: a fully automatic and robust peg free segmentation
and pose normalisation module, and a recognition module. In the first module,
the hand is segmented from its background using a thresholding technique based
on Otsu`s method combined with a skin colour detector. A set of fully automatic
algorithms are then proposed to segment the palm and fingers. In these
algorithms, the skeleton and the contour of the hand and fingers are estimated
and used to determine the global pose of the hand and the pose of each
individual finger. Finally the palm and fingers are cropped, pose corrected and
normalised. In the recognition module, various shape and texture based features
are extracted and used for matching purposes. The modified Hausdorff distance,
the Iterative Closest Point (ICP) and Independent Component Analysis (ICA)
algorithms are used for shape and texture features of the fingers. For the
palmprints, we use the Discrete Cosine Transform (DCT), directional line
features and ICA. Recognition (identification and verification) tests were
performed using fusion strategies based on the similarity scores of the fingers
and the palm. Experimental results show that the proposed system exhibits a
superior performance over existing systems with an accuracy of over 98\% for
hand identification and verification (at equal error rate) in a database of 560
different subjects. | [
"cs.CV"
] |
Deep learning-based point cloud registration models are often generalized
from extensive training over a large volume of data to learn the ability to
predict the desired geometric transformation to register 3D point clouds. In
this paper, we propose a meta-learning based 3D registration model, named 3D
Meta-Registration, that is capable of rapidly adapting and well generalizing to
new 3D registration tasks for unseen 3D point clouds. Our 3D Meta-Registration
gains a competitive advantage by training over a variety of 3D registration
tasks, which leads to an optimized model for the best performance on the
distribution of registration tasks including potentially unseen tasks.
Specifically, the proposed 3D Meta-Registration model consists of two modules:
3D registration learner and 3D registration meta-learner. During the training,
the 3D registration learner is trained to complete a specific registration task
aiming to determine the desired geometric transformation that aligns the source
point cloud with the target one. In the meantime, the 3D registration
meta-learner is trained to provide the optimal parameters to update the 3D
registration learner based on the learned task distribution. After training,
the 3D registration meta-learner, which is learned with the optimized coverage
of distribution of 3D registration tasks, is able to dynamically update 3D
registration learners with desired parameters to rapidly adapt to new
registration tasks. We tested our model on synthesized dataset ModelNet and
FlyingThings3D, as well as real-world dataset KITTI. Experimental results
demonstrate that 3D Meta-Registration achieves superior performance over other
previous techniques (e.g. FlowNet3D). | [
"cs.CV"
] |
Remote Sensing applications can benefit from a relatively fine spatial
resolution multispectral (MS) images and a high revisit frequency ensured by
the twin satellites Sentinel-2. Unfortunately, only four out of thirteen bands
are provided at the highest resolution of 10 meters, and the others at 20 or 60
meters. For instance the Short-Wave Infrared (SWIR) bands, provided at 20
meters, are very useful to detect active fires. Aiming to a more detailed
Active Fire Detection (AFD) maps, we propose a super-resolution data fusion
method based on Convolutional Neural Network (CNN) to move towards the 10-m
spatial resolution the SWIR bands. The proposed CNN-based solution achieves
better results than alternative methods in terms of some accuracy metrics.
Moreover we test the super-resolved bands from an application point of view by
monitoring active fire through classic indices. Advantages and limits of our
proposed approach are validated on specific geographical area (the mount
Vesuvius, close to Naples) that was damaged by widespread fires during the
summer of 2017. | [
"cs.CV",
"eess.IV"
] |
The area of building energy management has received a significant amount of
interest in recent years. This area is concerned with combining advancements in
sensor technologies, communications and advanced control algorithms to optimize
energy utilization. Reinforcement learning is one of the most prominent machine
learning algorithms used for control problems and has had many successful
applications in the area of building energy management. This research gives a
comprehensive review of the literature relating to the application of
reinforcement learning to developing autonomous building energy management
systems. The main direction for future research and challenges in reinforcement
learning are also outlined. | [
"cs.LG",
"cs.SY",
"stat.ML"
] |
Most monocular depth sensing methods use conventionally captured images that
are created without considering scene content. In contrast, animal eyes have
fast mechanical motions, called saccades, that control how the scene is imaged
by the fovea, where resolution is highest. In this paper, we present the
SaccadeCam framework for adaptively distributing resolution onto regions of
interest in the scene. Our algorithm for adaptive resolution is a
self-supervised network and we demonstrate results for end-to-end learning for
monocular depth estimation. We also show preliminary results with a real
SaccadeCam hardware prototype. | [
"cs.CV"
] |
A policy is said to be robust if it maximizes the reward while considering a
bad, or even adversarial, model. In this work we formalize two new criteria of
robustness to action uncertainty. Specifically, we consider two scenarios in
which the agent attempts to perform an action $a$, and (i) with probability
$\alpha$, an alternative adversarial action $\bar a$ is taken, or (ii) an
adversary adds a perturbation to the selected action in the case of continuous
action space. We show that our criteria are related to common forms of
uncertainty in robotics domains, such as the occurrence of abrupt forces, and
suggest algorithms in the tabular case. Building on the suggested algorithms,
we generalize our approach to deep reinforcement learning (DRL) and provide
extensive experiments in the various MuJoCo domains. Our experiments show that
not only does our approach produce robust policies, but it also improves the
performance in the absence of perturbations. This generalization indicates that
action-robustness can be thought of as implicit regularization in RL problems. | [
"cs.LG",
"stat.ML"
] |
Planning methods can solve temporally extended sequential decision making
problems by composing simple behaviors. However, planning requires suitable
abstractions for the states and transitions, which typically need to be
designed by hand. In contrast, model-free reinforcement learning (RL) can
acquire behaviors from low-level inputs directly, but often struggles with
temporally extended tasks. Can we utilize reinforcement learning to
automatically form the abstractions needed for planning, thus obtaining the
best of both approaches? We show that goal-conditioned policies learned with RL
can be incorporated into planning, so that a planner can focus on which states
to reach, rather than how those states are reached. However, with complex state
observations such as images, not all inputs represent valid states. We
therefore also propose using a latent variable model to compactly represent the
set of valid states for the planner, so that the policies provide an
abstraction of actions, and the latent variable model provides an abstraction
of states. We compare our method with planning-based and model-free methods and
find that our method significantly outperforms prior work when evaluated on
image-based robot navigation and manipulation tasks that require non-greedy,
multi-staged behavior. | [
"cs.LG",
"cs.AI",
"cs.RO",
"stat.ML"
] |
Deep reinforcement learning (DRL) requires large samples and a long training
time to operate optimally. Yet humans rarely require long periods training to
perform well on novel tasks, such as computer games, once they are provided
with an accurate program of instructions. We used perceptual control theory
(PCT) to construct a simple closed-loop model which requires no training
samples and training time within a video game study using the Arcade Learning
Environment (ALE). The model was programmed to parse inputs from the
environment into hierarchically organised perceptual signals, and it computed a
dynamic error signal by subtracting the incoming signal for each perceptual
variable from a reference signal to drive output signals to reduce this error.
We tested the same model across two different Atari paddle games Breakout and
Pong to achieve performance at least as high as DRL paradigms, and close to
good human performance. Our study shows that perceptual control models, based
on simple assumptions, can perform well without learning. We conclude by
specifying a parsimonious role of learning that may be more similar to
psychological functioning. | [
"cs.LG",
"cs.HC"
] |
Procedural models are being widely used to synthesize scenes for graphics,
gaming, and to create (labeled) synthetic datasets for ML. In order to produce
realistic and diverse scenes, a number of parameters governing the procedural
models have to be carefully tuned by experts. These parameters control both the
structure of scenes being generated (e.g. how many cars in the scene), as well
as parameters which place objects in valid configurations. Meta-Sim aimed at
automatically tuning parameters given a target collection of real images in an
unsupervised way. In Meta-Sim2, we aim to learn the scene structure in addition
to parameters, which is a challenging problem due to its discrete nature.
Meta-Sim2 proceeds by learning to sequentially sample rule expansions from a
given probabilistic scene grammar. Due to the discrete nature of the problem,
we use Reinforcement Learning to train our model, and design a feature space
divergence between our synthesized and target images that is key to successful
training. Experiments on a real driving dataset show that, without any
supervision, we can successfully learn to generate data that captures discrete
structural statistics of objects, such as their frequency, in real images. We
also show that this leads to downstream improvement in the performance of an
object detector trained on our generated dataset as opposed to other baseline
simulation methods. Project page:
https://nv-tlabs.github.io/meta-sim-structure/. | [
"cs.CV",
"cs.GR",
"cs.LG",
"eess.IV"
] |
One principled approach for provably efficient exploration is incorporating
the upper confidence bound (UCB) into the value function as a bonus. However,
UCB is specified to deal with linear and tabular settings and is incompatible
with Deep Reinforcement Learning (DRL). In this paper, we propose a principled
exploration method for DRL through Optimistic Bootstrapping and Backward
Induction (OB2I). OB2I constructs a general-purpose UCB-bonus through
non-parametric bootstrap in DRL. The UCB-bonus estimates the epistemic
uncertainty of state-action pairs for optimistic exploration. We build
theoretical connections between the proposed UCB-bonus and the LSVI-UCB in a
linear setting. We propagate future uncertainty in a time-consistent manner
through episodic backward update, which exploits the theoretical advantage and
empirically improves the sample-efficiency. Our experiments in the MNIST maze
and Atari suite suggest that OB2I outperforms several state-of-the-art
exploration approaches. | [
"cs.LG"
] |
Transformer is a powerful tool for many natural language tasks which is based
on self-attention, a mechanism that encodes the dependence of other tokens on
each specific token, but the computation of self-attention is a bottleneck due
to its quadratic time complexity. There are various approaches to reduce the
time complexity and approximation of matrix is one such. In Nystr\"omformer,
the authors used Nystr\"om based method for approximation of softmax. The
Nystr\"om method generates a fast approximation to any large-scale symmetric
positive semidefinite (SPSD) matrix using only a few columns of the SPSD
matrix. However, since the Nystr\"om approximation is low-rank when the
spectrum of the SPSD matrix decays slowly, the Nystr\"om approximation is of
low accuracy. Here an alternative method is proposed for approximation which
has a much stronger error bound than the Nystr\"om method. The time complexity
of this same as Nystr\"omformer which is $O\left({n}\right)$. | [
"cs.LG",
"cs.CL"
] |
A cornerstone of geometric reconstruction, rotation averaging seeks the set
of absolute rotations that optimally explains a set of measured relative
orientations between them. In spite of being an integral part of bundle
adjustment and structure-from-motion, averaging rotations is both a non-convex
and high-dimensional optimization problem. In this paper, we address it from a
maximum likelihood estimation standpoint and make a twofold contribution.
Firstly, we set forth a novel initialization-free primal-dual method which we
show empirically to converge to the global optimum. Further, we derive what is
to our knowledge, the first optimal closed-form solution for rotation averaging
in cycle graphs and contextualize this result within spectral graph theory. Our
proposed methods achieve a significant gain both in precision and performance. | [
"cs.CV"
] |
Unique micro-Doppler signature ($\boldsymbol{\mu}$-D) of a human body motion
can be analyzed as the superposition of different body parts
$\boldsymbol{\mu}$-D signatures. Extraction of human limbs $\boldsymbol{\mu}$-D
signatures in real-time can be used to detect, classify and track human motion
especially for safety application. In this paper, two methods are combined to
simulate $\boldsymbol{\mu}$-D signatures of a walking human. Furthermore, a
novel limbs $\mu$-D signature time independent decomposition feasibility study
is presented based on features as $\mu$-D signatures and range profiles also
known as micro-Range ($\mu$-R). Walking human body parts can be divided into
four classes (base, arms, legs, feet) and a decision tree classifier is used.
Validation is done and the classifier is able to decompose $\mu$-D signatures
of limbs from a walking human signature on real-time basis. | [
"cs.CV",
"eess.SP",
"68T10 (Primary), 68T40 (Secondary)"
] |
In statistics and machine learning, approximation of an intractable
integration is often achieved by using the unbiased Monte Carlo estimator, but
the variances of the estimation are generally high in many applications.
Control variates approaches are well-known to reduce the variance of the
estimation. These control variates are typically constructed by employing
predefined parametric functions or polynomials, determined by using those
samples drawn from the relevant distributions. Instead, we propose to construct
those control variates by learning neural networks to handle the cases when
test functions are complex. In many applications, obtaining a large number of
samples for Monte Carlo estimation is expensive, which may result in
overfitting when training a neural network. We thus further propose to employ
auxiliary random variables induced by the original ones to extend data samples
for training the neural networks. We apply the proposed control variates with
augmented variables to thermodynamic integration and reinforcement learning.
Experimental results demonstrate that our method can achieve significant
variance reduction compared with other alternatives. | [
"stat.ML",
"cs.LG"
] |
It is common in graphic design humans visually arrange various elements
according to their design intent and semantics. For example, a title text
almost always appears on top of other elements in a document. In this work, we
generate graphic layouts that can flexibly incorporate such design semantics,
either specified implicitly or explicitly by a user. We optimize using the
latent space of an off-the-shelf layout generation model, allowing our approach
to be complementary to and used with existing layout generation models. Our
approach builds on a generative layout model based on a Transformer
architecture, and formulates the layout generation as a constrained
optimization problem where design constraints are used for element alignment,
overlap avoidance, or any other user-specified relationship. We show in the
experiments that our approach is capable of generating realistic layouts in
both constrained and unconstrained generation tasks with a single model. The
code is available at https://github.com/ktrk115/const_layout . | [
"cs.CV",
"cs.MM"
] |
Generative adversarial networks (GANs) provide state-of-the-art results in
image generation. However, despite being so powerful, they still remain very
challenging to train. This is in particular caused by their highly non-convex
optimization space leading to a number of instabilities. Among them, mode
collapse stands out as one of the most daunting ones. This undesirable event
occurs when the model can only fit a few modes of the data distribution, while
ignoring the majority of them. In this work, we combat mode collapse using
second-order gradient information. To do so, we analyse the loss surface
through its Hessian eigenvalues, and show that mode collapse is related to the
convergence towards sharp minima. In particular, we observe how the eigenvalues
of the $G$ are directly correlated with the occurrence of mode collapse.
Finally, motivated by these findings, we design a new optimization algorithm
called nudged-Adam (NuGAN) that uses spectral information to overcome mode
collapse, leading to empirically more stable convergence properties. | [
"cs.LG",
"cs.CV"
] |
We prove under commonly used assumptions the convergence of actor-critic
reinforcement learning algorithms, which simultaneously learn a policy
function, the actor, and a value function, the critic. Both functions can be
deep neural networks of arbitrary complexity. Our framework allows showing
convergence of the well known Proximal Policy Optimization (PPO) and of the
recently introduced RUDDER. For the convergence proof we employ recently
introduced techniques from the two time-scale stochastic approximation theory.
Our results are valid for actor-critic methods that use episodic samples and
that have a policy that becomes more greedy during learning. Previous
convergence proofs assume linear function approximation, cannot treat episodic
examples, or do not consider that policies become greedy. The latter is
relevant since optimal policies are typically deterministic. | [
"cs.LG",
"cs.AI",
"math.OC"
] |
In this paper, we propose a monocular 3D object detection framework in the
domain of autonomous driving. Unlike previous image-based methods which focus
on RGB feature extracted from 2D images, our method solves this problem in the
reconstructed 3D space in order to exploit 3D contexts explicitly. To this end,
we first leverage a stand-alone module to transform the input data from 2D
image plane to 3D point clouds space for a better input representation, then we
perform the 3D detection using PointNet backbone net to obtain objects 3D
locations, dimensions and orientations. To enhance the discriminative
capability of point clouds, we propose a multi-modal feature fusion module to
embed the complementary RGB cue into the generated point clouds representation.
We argue that it is more effective to infer the 3D bounding boxes from the
generated 3D scene space (i.e., X,Y, Z space) compared to the image plane
(i.e., R,G,B image plane). Evaluation on the challenging KITTI dataset shows
that our approach boosts the performance of state-of-the-art monocular approach
by a large margin. | [
"cs.CV"
] |
Current face or object detection methods via convolutional neural network
(such as OverFeat, R-CNN and DenseNet) explicitly extract multi-scale features
based on an image pyramid. However, such a strategy increases the computational
burden for face detection. In this paper, we propose a fast face detection
method based on discriminative complete features (DCFs) extracted by an
elaborately designed convolutional neural network, where face detection is
directly performed on the complete feature maps. DCFs have shown the ability of
scale invariance, which is beneficial for face detection with high speed and
promising performance. Therefore, extracting multi-scale features on an image
pyramid employed in the conventional methods is not required in the proposed
method, which can greatly improve its efficiency for face detection.
Experimental results on several popular face detection datasets show the
efficiency and the effectiveness of the proposed method for face detection. | [
"cs.CV"
] |
Complex plasmas consist of microparticles embedded in a low-temperature
plasma containing ions, electrons and neutral particles. The microparticles
form a dynamical system that can be used to study a multitude of effects on the
level of the constituent particles. The microparticles are usually illuminated
with a sheet of laser light, and the scattered light can be observed with
digital cameras. Some complex plasma microgravity research facilities use two
cameras with an overlapping field of view.
An overlapping field of view can be used to combine the resulting images into
one and trace the particles in the larger field of view. In previous work this
was discussed for the images recorded by the PK-4 Laboratory on board the
International Space Station. In that work the width of the laser sheet was,
however, not taken into account. In this paper, we will discuss how to improve
the transformation of the features into a joint coordinate system, and possibly
extract information on the 3D position of particles in the overlap region. | [
"cs.CV",
"physics.plasm-ph"
] |
Optimal transport is a notoriously difficult problem to solve numerically,
with current approaches often remaining intractable for very large scale
applications such as those encountered in machine learning. Wasserstein
barycenters -- the problem of finding measures in-between given input measures
in the optimal transport sense -- is even more computationally demanding as it
requires to solve an optimization problem involving optimal transport
distances. By training a deep convolutional neural network, we improve by a
factor of 60 the computational speed of Wasserstein barycenters over the
fastest state-of-the-art approach on the GPU, resulting in milliseconds
computational times on $512\times512$ regular grids. We show that our network,
trained on Wasserstein barycenters of pairs of measures, generalizes well to
the problem of finding Wasserstein barycenters of more than two measures. We
demonstrate the efficiency of our approach for computing barycenters of
sketches and transferring colors between multiple images. | [
"cs.LG",
"stat.ML",
"I.2.6; I.4.9; G.2.1; G.3; I.3.3"
] |
Generalization beyond a training dataset is a main goal of machine learning,
but theoretical understanding of generalization remains an open problem for
many models. The need for a new theory is exacerbated by recent observations in
deep neural networks where overparameterization leads to better performance,
contradicting the conventional wisdom from classical statistics. In this paper,
we investigate generalization error for kernel regression, which, besides being
a popular machine learning method, also includes infinitely overparameterized
neural networks trained with gradient descent. We use techniques from
statistical mechanics to derive an analytical expression for generalization
error applicable to any kernel or data distribution. We present applications of
our theory to real and synthetic datasets, and for many kernels including those
that arise from training deep neural networks in the infinite-width limit. We
elucidate an inductive bias of kernel regression to explain data with "simple
functions", which are identified by solving a kernel eigenfunction problem on
the data distribution. This notion of simplicity allows us to characterize
whether a kernel is compatible with a learning task, facilitating good
generalization performance from a small number of training examples. We show
that more data may impair generalization when noisy or not expressible by the
kernel, leading to non-monotonic learning curves with possibly many peaks. To
further understand these phenomena, we turn to the broad class of rotation
invariant kernels, which is relevant to training deep neural networks in the
infinite-width limit, and present a detailed mathematical analysis of them when
data is drawn from a spherically symmetric distribution and the number of input
dimensions is large. | [
"stat.ML",
"cond-mat.dis-nn",
"cs.LG"
] |
Cross-spectral verification remains a big issue in biometrics, especially for
the ocular area due to differences in the reflected features in the images
depending on the region and spectrum used.
In this paper, we investigate the use of Conditional Adversarial Networks for
spectrum translation between near infra-red and visual light images for ocular
biometrics. We analyze the transformation based on the overall visual quality
of the transformed images and the accuracy drop of the identification system
when trained with opposing data.
We use the PolyU database and propose two different systems for biometric
verification, the first one based on Siamese Networks trained with Softmax and
Cross-Entropy loss, and the second one a Triplet Loss network. We achieved an
EER of 1\% when using a Triplet Loss network trained for NIR and finding the
Euclidean distance between the real NIR images and the fake ones translated
from the visible spectrum. We also outperform previous results using baseline
algorithms. | [
"cs.CV"
] |
Classifying the sub-categories of an object from the same super-category
(e.g. bird species, car and aircraft models) in fine-grained visual
classification (FGVC) highly relies on discriminative feature representation
and accurate region localization. Existing approaches mainly focus on
distilling information from high-level features. In this paper, however, we
show that by integrating low-level information (e.g. color, edge junctions,
texture patterns), performance can be improved with enhanced feature
representation and accurately located discriminative regions. Our solution,
named Attention Pyramid Convolutional Neural Network (AP-CNN), consists of a) a
pyramidal hierarchy structure with a top-down feature pathway and a bottom-up
attention pathway, and hence learns both high-level semantic and low-level
detailed feature representation, and b) an ROI guided refinement strategy with
ROI guided dropblock and ROI guided zoom-in, which refines features with
discriminative local regions enhanced and background noises eliminated. The
proposed AP-CNN can be trained end-to-end, without the need of additional
bounding box/part annotations. Extensive experiments on three commonly used
FGVC datasets (CUB-200-2011, Stanford Cars, and FGVC-Aircraft) demonstrate that
our approach can achieve state-of-the-art performance. Code available at
\url{http://dwz1.cc/ci8so8a} | [
"cs.CV"
] |
Advancements in deep generative models have made it possible to synthesize
images, videos and audio signals that are difficult to distinguish from natural
signals, creating opportunities for potential abuse of these capabilities. This
motivates the problem of tracking the provenance of signals, i.e., being able
to determine the original source of a signal. Watermarking the signal at the
time of signal creation is a potential solution, but current techniques are
brittle and watermark detection mechanisms can easily be bypassed by applying
post-processing transformations (cropping images, shifting pitch in the audio
etc.). In this paper, we introduce ReSWAT (Resilient Signal Watermarking via
Adversarial Training), a framework for learning transformation-resilient
watermark detectors that are able to detect a watermark even after a signal has
been through several post-processing transformations. Our detection method can
be applied to domains with continuous data representations such as images,
videos or sound signals. Experiments on watermarking image and audio signals
show that our method can reliably detect the provenance of a signal, even if it
has been through several post-processing transformations, and improve upon
related work in this setting. Furthermore, we show that for specific kinds of
transformations (perturbations bounded in the L2 norm), we can even get formal
guarantees on the ability of our model to detect the watermark. We provide
qualitative examples of watermarked image and audio samples in
https://drive.google.com/open?id=1-yZ0WIGNu2Iez7UpXBjtjVgZu3jJjFga. | [
"cs.LG",
"cs.CR"
] |
Soft biometrics analysis is seen as an important research topic, given its
relevance to various applications. However, even though it is frequently seen
as a solved task, it can still be very hard to perform in wild conditions,
under varying image conditions, uncooperative poses, and occlusions.
Considering the gender trait as our topic of study, we report an extensive
analysis of the feasibility of its inference regarding image (resolution,
luminosity, and blurriness) and subject-based features (face and body keypoints
confidence). Using three state-of-the-art datasets (PETA, PA-100K, RAP) and
five Person Attribute Recognition models, we correlate feature analysis with
gender inference accuracy using the Shapley value, enabling us to perceive the
importance of each image/subject-based feature. Furthermore, we analyze
face-based gender inference and assess the pose effect on it. Our results
suggest that: 1) image-based features are more influential for low-quality
data; 2) an increase in image quality translates into higher subject-based
feature importance; 3) face-based gender inference accuracy correlates with
image quality increase; and 4) subjects' frontal pose promotes an implicit
attention towards the face. The reported results are seen as a basis for
subsequent developments of inference approaches in uncontrolled outdoor
environments, which typically correspond to visual surveillance conditions. | [
"cs.CV"
] |
Continual learning could shift the machine learning paradigm from data
centric to model centric. A continual learning model needs to scale efficiently
to handle semantically different datasets, while avoiding unnecessary growth.
We introduce hash-routed convolutional neural networks: a group of
convolutional units where data flows dynamically. Feature maps are compared
using feature hashing and similar data is routed to the same units. A
hash-routed network provides excellent plasticity thanks to its routed nature,
while generating stable features through the use of orthogonal feature hashing.
Each unit evolves separately and new units can be added (to be used only when
necessary). Hash-routed networks achieve excellent performance across a variety
of typical continual learning benchmarks without storing raw data and train
using only gradient descent. Besides providing a continual learning framework
for supervised tasks with encouraging results, our model can be used for
unsupervised or reinforcement learning. | [
"cs.LG",
"cs.AI"
] |
We propose an active learning approach to image segmentation that exploits
geometric priors to speed up and streamline the annotation process. It can be
applied for both background-foreground and multi-class segmentation tasks in 2D
images and 3D image volumes. Our approach combines geometric smoothness priors
in the image space with more traditional uncertainty measures to estimate which
pixels or voxels are the most informative, and thus should to be annotated
next. For multi-class settings, we additionally introduce two novel criteria
for uncertainty. In the 3D case, we use the resulting uncertainty measure to
select voxels lying on a planar patch, which makes batch annotation much more
convenient for the end user compared to the setting where voxels are randomly
distributed in a volume. The planar patch is found using a branch-and-bound
algorithm that looks for a 2D patch in a 3D volume where the most informative
instances are located. We evaluate our approach on Electron Microscopy and
Magnetic Resonance image volumes, as well as on regular images of horses and
faces. We demonstrate a substantial performance increase over other approaches
thanks to the use of geometric priors. | [
"cs.CV"
] |
Detecting aligned 3D keypoints is essential under many scenarios such as
object tracking, shape retrieval and robotics. However, it is generally hard to
prepare a high-quality dataset for all types of objects due to the ambiguity of
keypoint itself. Meanwhile, current unsupervised detectors are unable to
generate aligned keypoints with good coverage. In this paper, we propose an
unsupervised aligned keypoint detector, Skeleton Merger, which utilizes
skeletons to reconstruct objects. It is based on an Autoencoder architecture.
The encoder proposes keypoints and predicts activation strengths of edges
between keypoints. The decoder performs uniform sampling on the skeleton and
refines it into small point clouds with pointwise offsets. Then the activation
strengths are applied and the sub-clouds are merged. Composite Chamfer Distance
(CCD) is proposed as a distance between the input point cloud and the
reconstruction composed of sub-clouds masked by activation strengths. We
demonstrate that Skeleton Merger is capable of detecting semantically-rich
salient keypoints with good alignment, and shows comparable performance to
supervised methods on the KeypointNet dataset. It is also shown that the
detector is robust to noise and subsampling. Our code is available at
https://github.com/eliphatfs/SkeletonMerger. | [
"cs.CV"
] |
Electroencephalograph (EEG) emotion recognition is a significant task in the
brain-computer interface field. Although many deep learning methods are
proposed recently, it is still challenging to make full use of the information
contained in different domains of EEG signals. In this paper, we present a
novel method, called four-dimensional attention-based neural network (4D-aNN)
for EEG emotion recognition. First, raw EEG signals are transformed into 4D
spatial-spectral-temporal representations. Then, the proposed 4D-aNN adopts
spectral and spatial attention mechanisms to adaptively assign the weights of
different brain regions and frequency bands, and a convolutional neural network
(CNN) is utilized to deal with the spectral and spatial information of the 4D
representations. Moreover, a temporal attention mechanism is integrated into a
bidirectional Long Short-Term Memory (LSTM) to explore temporal dependencies of
the 4D representations. Our model achieves state-of-the-art performance on the
SEED dataset under intra-subject splitting. The experimental results have shown
the effectiveness of the attention mechanisms in different domains for EEG
emotion recognition. | [
"cs.LG"
] |
Few-shot image classification is a challenging problem which aims to achieve
the human level of recognition based only on a small number of images. Deep
learning algorithms such as meta-learning, transfer learning, and metric
learning have been employed recently and achieved the state-of-the-art
performance. In this survey, we review representative deep metric learning
methods for few-shot classification, and categorize them into three groups
according to the major problems and novelties they focus on. We conclude this
review with a discussion on current challenges and future trends in few-shot
image classification. | [
"cs.CV",
"cs.LG"
] |
We study reinforcement learning (RL) with linear function approximation under
the adaptivity constraint. We consider two popular limited adaptivity models:
batch learning model and rare policy switch model, and propose two efficient
online RL algorithms for linear Markov decision processes. In specific, for the
batch learning model, our proposed LSVI-UCB-Batch algorithm achieves an $\tilde
O(\sqrt{d^3H^3T} + dHT/B)$ regret, where $d$ is the dimension of the feature
mapping, $H$ is the episode length, $T$ is the number of interactions and $B$
is the number of batches. Our result suggests that it suffices to use only
$\sqrt{T/dH}$ batches to obtain $\tilde O(\sqrt{d^3H^3T})$ regret. For the rare
policy switch model, our proposed LSVI-UCB-RareSwitch algorithm enjoys an
$\tilde O(\sqrt{d^3H^3T[1+T/(dH)]^{dH/B}})$ regret, which implies that $dH\log
T$ policy switches suffice to obtain the $\tilde O(\sqrt{d^3H^3T})$ regret. Our
algorithms achieve the same regret as the LSVI-UCB algorithm (Jin et al.,
2019), yet with a substantially smaller amount of adaptivity. | [
"cs.LG",
"math.OC",
"stat.ML"
] |
Anomaly detection is a task that recognizes whether an input sample is
included in the distribution of a target normal class or an anomaly class.
Conventional generative adversarial network (GAN)-based methods utilize an
entire image including foreground and background as an input. However, in these
methods, a useless region unrelated to the normal class (e.g., unrelated
background) is learned as normal class distribution, thereby leading to false
detection. To alleviate this problem, this paper proposes a novel two-stage
network consisting of an attention network and an anomaly detection GAN
(ADGAN). The attention network generates an attention map that can indicate the
region representing the normal class distribution. To generate an accurate
attention map, we propose the attention loss and the adversarial anomaly loss
based on synthetic anomaly samples generated from hard augmentation. By
applying the attention map to an image feature map, ADGAN learns the normal
class distribution from which the useless region is removed, and it is possible
to greatly reduce the problem difficulty of the anomaly detection task.
Additionally, the estimated attention map can be used for anomaly segmentation
because it can distinguish between normal and anomaly regions. As a result, the
proposed method outperforms the state-of-the-art anomaly detection and anomaly
segmentation methods for widely used datasets. | [
"cs.CV"
] |
Reconstruction error is a prevalent score used to identify anomalous samples
when data are modeled by generative models, such as (variational) auto-encoders
or generative adversarial networks. This score relies on the assumption that
normal samples are located on a manifold and all anomalous samples are located
outside. Since the manifold can be learned only where the training data lie,
there are no guarantees how the reconstruction error behaves elsewhere and the
score, therefore, seems to be ill-defined. This work defines an anomaly score
that is theoretically compatible with generative models, and very natural for
(variational) auto-encoders as they seem to be prevalent. The new score can be
also used to select hyper-parameters and models. Finally, we explain why
reconstruction error delivers good experimental results despite weak
theoretical justification. | [
"stat.ML",
"cs.LG"
] |
Graph neural networks (GNNs) have emerged as a powerful tool for nonlinear
processing of graph signals, exhibiting success in recommender systems, power
outage prediction, and motion planning, among others. GNNs consists of a
cascade of layers, each of which applies a graph convolution, followed by a
pointwise nonlinearity. In this work, we study the impact that changes in the
underlying topology have on the output of the GNN. First, we show that GNNs are
permutation equivariant, which implies that they effectively exploit internal
symmetries of the underlying topology. Then, we prove that graph convolutions
with integral Lipschitz filters, in combination with the frequency mixing
effect of the corresponding nonlinearities, yields an architecture that is both
stable to small changes in the underlying topology and discriminative of
information located at high frequencies. These are two properties that cannot
simultaneously hold when using only linear graph filters, which are either
discriminative or stable, thus explaining the superior performance of GNNs. | [
"cs.LG",
"stat.ML"
] |
We introduce LOGAN, a deep neural network aimed at learning general-purpose
shape transforms from unpaired domains. The network is trained on two sets of
shapes, e.g., tables and chairs, while there is neither a pairing between
shapes from the domains as supervision nor any point-wise correspondence
between any shapes. Once trained, LOGAN takes a shape from one domain and
transforms it into the other. Our network consists of an autoencoder to encode
shapes from the two input domains into a common latent space, where the latent
codes concatenate multi-scale shape features, resulting in an overcomplete
representation. The translator is based on a generative adversarial network
(GAN), operating in the latent space, where an adversarial loss enforces
cross-domain translation while a feature preservation loss ensures that the
right shape features are preserved for a natural shape transform. We conduct
ablation studies to validate each of our key network designs and demonstrate
superior capabilities in unpaired shape transforms on a variety of examples
over baselines and state-of-the-art approaches. We show that LOGAN is able to
learn what shape features to preserve during shape translation, either local or
non-local, whether content or style, depending solely on the input domains for
training. | [
"cs.CV",
"cs.GR",
"cs.LG"
] |
Graph Convolutional Networks (GCNs) have been widely studied for compact data
representation and semi-supervised learning tasks. However, existing GCNs
usually use a fixed neighborhood graph which is not guaranteed to be optimal
for semi-supervised learning tasks. In this paper, we first re-interpret graph
convolution operation in GCNs as a composition of feature propagation and
(non-linear) transformation. Based on this observation, we then propose a
unified adaptive neighborhood feature propagation model and derive a novel
Adaptive Neighborhood Graph Propagation Network (ANGPN) for data representation
and semi-supervised learning. The aim of ANGPN is to conduct both graph
construction and graph convolution simultaneously and cooperatively in a
unified formulation and thus can learn an optimal neighborhood graph that best
serves graph convolution for data representation and semi-supervised learning.
One main benefit of ANGPN is that the learned (convolutional) representation
can provide useful weakly supervised information for constructing a better
neighborhood graph which meanwhile facilitates data representation and
learning. Experimental results on four benchmark datasets demonstrate the
effectiveness and benefit of the proposed ANGPN. | [
"cs.CV"
] |
The task of object viewpoint estimation has been a challenge since the early
days of computer vision. To estimate the viewpoint (or pose) of an object,
people have mostly looked at object intrinsic features, such as shape or
appearance. Surprisingly, informative features provided by other, extrinsic
elements in the scene, have so far mostly been ignored. At the same time,
contextual cues have been proven to be of great benefit for related tasks such
as object detection or action recognition. In this paper, we explore how
information from other objects in the scene can be exploited for viewpoint
estimation. In particular, we look at object configurations by following a
relational neighbor-based approach for reasoning about object relations. We
show that, starting from noisy object detections and viewpoint estimates,
exploiting the estimated viewpoint and location of other objects in the scene
can lead to improved object viewpoint predictions. Experiments on the KITTI
dataset demonstrate that object configurations can indeed be used as a
complementary cue to appearance-based viewpoint estimation. Our analysis
reveals that the proposed context-based method can improve object viewpoint
estimation by reducing specific types of viewpoint estimation errors commonly
made by methods that only consider local information. Moreover, considering
contextual information produces superior performance in scenes where a high
number of object instances occur. Finally, our results suggest that, following
a cautious relational neighbor formulation brings improvements over its
aggressive counterpart for the task of object viewpoint estimation. | [
"cs.CV"
] |
In this work, we propose CARLS, a novel framework for augmenting the capacity
of existing deep learning frameworks by enabling multiple components -- model
trainers, knowledge makers and knowledge banks -- to concertedly work together
in an asynchronous fashion across hardware platforms. The proposed CARLS is
particularly suitable for learning paradigms where model training benefits from
additional knowledge inferred or discovered during training, such as node
embeddings for graph neural networks or reliable pseudo labels from model
predictions. We also describe three learning paradigms -- semi-supervised
learning, curriculum learning and multimodal learning -- as examples that can
be scaled up efficiently by CARLS. One version of CARLS has been open-sourced
and available for download at:
https://github.com/tensorflow/neural-structured-learning/tree/master/research/carls | [
"cs.LG"
] |
In this work, we address the challenging task of referring segmentation. The
query expression in referring segmentation typically indicates the target
object by describing its relationship with others. Therefore, to find the
target one among all instances in the image, the model must have a holistic
understanding of the whole image. To achieve this, we reformulate referring
segmentation as a direct attention problem: finding the region in the image
where the query language expression is most attended to. We introduce
transformer and multi-head attention to build a network with an encoder-decoder
attention mechanism architecture that "queries" the given image with the
language expression. Furthermore, we propose a Query Generation Module, which
produces multiple sets of queries with different attention weights that
represent the diversified comprehensions of the language expression from
different aspects. At the same time, to find the best way from these
diversified comprehensions based on visual clues, we further propose a Query
Balance Module to adaptively select the output features of these queries for a
better mask generation. Without bells and whistles, our approach is
light-weight and achieves new state-of-the-art performance consistently on
three referring segmentation datasets, RefCOCO, RefCOCO+, and G-Ref. Our code
is available at https://github.com/henghuiding/Vision-Language-Transformer. | [
"cs.CV"
] |
Proximal policy optimization and trust region policy optimization (PPO and
TRPO) with actor and critic parametrized by neural networks achieve significant
empirical success in deep reinforcement learning. However, due to nonconvexity,
the global convergence of PPO and TRPO remains less understood, which separates
theory from practice. In this paper, we prove that a variant of PPO and TRPO
equipped with overparametrized neural networks converges to the globally
optimal policy at a sublinear rate. The key to our analysis is the global
convergence of infinite-dimensional mirror descent under a notion of one-point
monotonicity, where the gradient and iterate are instantiated by neural
networks. In particular, the desirable representation power and optimization
geometry induced by the overparametrization of such neural networks allow them
to accurately approximate the infinite-dimensional gradient and iterate. | [
"cs.LG",
"math.OC",
"stat.ML"
] |
Dense pixel matching is important for many computer vision tasks such as
disparity and flow estimation. We present a robust, unified descriptor network
that considers a large context region with high spatial variance. Our network
has a very large receptive field and avoids striding layers to maintain spatial
resolution. These properties are achieved by creating a novel neural network
layer that consists of multiple, parallel, stacked dilated convolutions (SDC).
Several of these layers are combined to form our SDC descriptor network. In our
experiments, we show that our SDC features outperform state-of-the-art feature
descriptors in terms of accuracy and robustness. In addition, we demonstrate
the superior performance of SDC in state-of-the-art stereo matching, optical
flow and scene flow algorithms on several famous public benchmarks. | [
"cs.CV"
] |
While causal models are becoming one of the mainstays of machine learning,
the problem of uncertainty quantification in causal inference remains
challenging. In this paper, we study the causal data fusion problem, where
datasets pertaining to multiple causal graphs are combined to estimate the
average treatment effect of a target variable. As data arises from multiple
sources and can vary in quality and quantity, principled uncertainty
quantification becomes essential. To that end, we introduce Bayesian
Interventional Mean Processes, a framework which combines ideas from
probabilistic integration and kernel mean embeddings to represent
interventional distributions in the reproducing kernel Hilbert space, while
taking into account the uncertainty within each causal graph. To demonstrate
the utility of our uncertainty estimation, we apply our method to the Causal
Bayesian Optimisation task and show improvements over state-of-the-art methods. | [
"stat.ML",
"cs.LG"
] |
Detecting and localizing objects in the real 3D space, which plays a crucial
role in scene understanding, is particularly challenging given only a monocular
image due to the geometric information loss during imagery projection. We
propose MonoGRNet for the amodal 3D object detection from a monocular image via
geometric reasoning in both the observed 2D projection and the unobserved depth
dimension. MonoGRNet decomposes the monocular 3D object detection task into
four sub-tasks including 2D object detection, instance-level depth estimation,
projected 3D center estimation and local corner regression. The task
decomposition significantly facilitates the monocular 3D object detection,
allowing the target 3D bounding boxes to be efficiently predicted in a single
forward pass, without using object proposals, post-processing or the
computationally expensive pixel-level depth estimation utilized by previous
methods. In addition, MonoGRNet flexibly adapts to both fully and weakly
supervised learning, which improves the feasibility of our framework in diverse
settings. Experiments are conducted on KITTI, Cityscapes and MS COCO datasets.
Results demonstrate the promising performance of our framework in various
scenarios. | [
"cs.CV"
] |
Multi-view subspace clustering has been applied to applications such as image
processing and video surveillance, and has attracted increasing attention. Most
existing methods learn view-specific self-representation matrices, and
construct a combined affinity matrix from multiple views. The affinity
construction process is time-consuming, and the combined affinity matrix is not
guaranteed to reflect the whole true subspace structure. To overcome these
issues, the Latent Complete Row Space Recovery (LCRSR) method is proposed.
Concretely, LCRSR is based on the assumption that the multi-view observations
are generated from an underlying latent representation, which is further
assumed to collect the authentic samples drawn exactly from multiple subspaces.
LCRSR is able to recover the row space of the latent representation, which not
only carries complete information from multiple views but also determines the
subspace membership under certain conditions. LCRSR does not involve the graph
construction procedure and is solved with an efficient and convergent
algorithm, thereby being more scalable to large-scale datasets. The
effectiveness and efficiency of LCRSR are validated by clustering various kinds
of multi-view data and illustrated in the background subtraction task. | [
"cs.LG",
"stat.ML"
] |
Off-policy evaluation in reinforcement learning offers the chance of using
observational data to improve future outcomes in domains such as healthcare and
education, but safe deployment in high stakes settings requires ways of
assessing its validity. Traditional measures such as confidence intervals may
be insufficient due to noise, limited data and confounding. In this paper we
develop a method that could serve as a hybrid human-AI system, to enable human
experts to analyze the validity of policy evaluation estimates. This is
accomplished by highlighting observations in the data whose removal will have a
large effect on the OPE estimate, and formulating a set of rules for choosing
which ones to present to domain experts for validation. We develop methods to
compute exactly the influence functions for fitted Q-evaluation with two
different function classes: kernel-based and linear least squares, as well as
importance sampling methods. Experiments on medical simulations and real-world
intensive care unit data demonstrate that our method can be used to identify
limitations in the evaluation process and make evaluation more robust. | [
"cs.LG",
"stat.ML"
] |
Text-based games (TBGs) have become a popular proving ground for the
demonstration of learning-based agents that make decisions in quasi real-world
settings. The crux of the problem for a reinforcement learning agent in such
TBGs is identifying the objects in the world, and those objects' relations with
that world. While the recent use of text-based resources for increasing an
agent's knowledge and improving its generalization have shown promise, we posit
in this paper that there is much yet to be learned from visual representations
of these same worlds. Specifically, we propose to retrieve images that
represent specific instances of text observations from the world and train our
agents on such images. This improves the agent's overall understanding of the
game 'scene' and objects' relationships to the world around them, and the
variety of visual representations on offer allow the agent to generate a better
generalization of a relationship. We show that incorporating such images
improves the performance of agents in various TBG settings. | [
"cs.LG",
"cs.CL"
] |
Customers are usually exposed to online digital advertisement channels, such
as email marketing, display advertising, paid search engine marketing, along
their way to purchase or subscribe products( aka. conversion). The marketers
track all the customer journey data and try to measure the effectiveness of
each advertising channel. The inference about the influence of each channel
plays an important role in budget allocation and inventory pricing decisions.
Several simplistic rule-based strategies and data-driven algorithmic strategies
have been widely used in marketing field, but they do not address the issues,
such as channel interaction, time dependency, user characteristics. In this
paper, we propose a novel attribution algorithm based on deep learning to
assess the impact of each advertising channel. We present Deep Neural Net With
Attention multi-touch attribution model (DNAMTA) model in a supervised learning
fashion of predicting if a series of events leads to conversion, and it leads
us to have a deep understanding of the dynamic interaction effects between
media channels. DNAMTA also incorporates user-context information, such as user
demographics and behavior, as control variables to reduce the estimation biases
of media effects. We used computational experiment of large real world
marketing dataset to demonstrate that our proposed model is superior to
existing methods in both conversion prediction and media channel influence
evaluation. | [
"cs.LG",
"stat.ML"
] |
Value-based methods constitute a fundamental methodology in planning and deep
reinforcement learning (RL). In this paper, we propose to exploit the
underlying structures of the state-action value function, i.e., Q function, for
both planning and deep RL. In particular, if the underlying system dynamics
lead to some global structures of the Q function, one should be capable of
inferring the function better by leveraging such structures. Specifically, we
investigate the low-rank structure, which widely exists for big data matrices.
We verify empirically the existence of low-rank Q functions in the context of
control and deep RL tasks. As our key contribution, by leveraging Matrix
Estimation (ME) techniques, we propose a general framework to exploit the
underlying low-rank structure in Q functions. This leads to a more efficient
planning procedure for classical control, and additionally, a simple scheme
that can be applied to any value-based RL techniques to consistently achieve
better performance on "low-rank" tasks. Extensive experiments on control tasks
and Atari games confirm the efficacy of our approach. Code is available at
https://github.com/YyzHarry/SV-RL. | [
"cs.LG",
"stat.ML"
] |
Though deep neural networks have achieved the state of the art performance in
visual classification, recent studies have shown that they are all vulnerable
to the attack of adversarial examples. In this paper, we develop improved
techniques for defending against adversarial examples. First, we propose an
enhanced defense technique denoted Attention and Adversarial Logit
Pairing(AT+ALP), which encourages both attention map and logit for the pairs of
examples to be similar. When being applied to clean examples and their
adversarial counterparts, AT+ALP improves accuracy on adversarial examples over
adversarial training. We show that AT+ALP can effectively increase the average
activations of adversarial examples in the key area and demonstrate that it
focuses on discriminate features to improve the robustness of the model.
Finally, we conduct extensive experiments using a wide range of datasets and
the experiment results show that our AT+ALP achieves the state of the art
defense performance. For example, on 17 Flower Category Database, under strong
200-iteration PGD gray-box and black-box attacks where prior art has 34% and
39% accuracy, our method achieves 50% and 51%. Compared with previous work, our
work is evaluated under highly challenging PGD attack: the maximum perturbation
$\epsilon \in \{0.25,0.5\}$ i.e. $L_\infty \in \{0.25,0.5\}$ with 10 to 200
attack iterations. To the best of our knowledge, such a strong attack has not
been previously explored on a wide range of datasets. | [
"cs.LG",
"cs.CR",
"eess.IV"
] |
Data transformations (e.g. rotations, reflections, and cropping) play an
important role in self-supervised learning. Typically, images are transformed
into different views, and neural networks trained on tasks involving these
views produce useful feature representations for downstream tasks, including
anomaly detection. However, for anomaly detection beyond image data, it is
often unclear which transformations to use. Here we present a simple end-to-end
procedure for anomaly detection with learnable transformations. The key idea is
to embed the transformed data into a semantic space such that the transformed
data still resemble their untransformed form, while different transformations
are easily distinguishable. Extensive experiments on time series demonstrate
that our proposed method outperforms existing approaches in the one-vs.-rest
setting and is competitive in the more challenging n-vs.-rest anomaly detection
task. On tabular datasets from the medical and cyber-security domains, our
method learns domain-specific transformations and detects anomalies more
accurately than previous work. | [
"cs.LG",
"cs.AI"
] |
Link prediction is a very fundamental task on graphs. Inspired by traditional
path-based methods, in this paper we propose a general and flexible
representation learning framework based on paths for link prediction.
Specifically, we define the representation of a pair of nodes as the
generalized sum of all path representations, with each path representation as
the generalized product of the edge representations in the path. Motivated by
the Bellman-Ford algorithm for solving the shortest path problem, we show that
the proposed path formulation can be efficiently solved by the generalized
Bellman-Ford algorithm. To further improve the capacity of the path
formulation, we propose the Neural Bellman-Ford Network (NBFNet), a general
graph neural network framework that solves the path formulation with learned
operators in the generalized Bellman-Ford algorithm. The NBFNet parameterizes
the generalized Bellman-Ford algorithm with 3 neural components, namely
INDICATOR, MESSAGE and AGGREGATE functions, which corresponds to the boundary
condition, multiplication operator, and summation operator respectively. The
NBFNet is very general, covers many traditional path-based methods, and can be
applied to both homogeneous graphs and multi-relational graphs (e.g., knowledge
graphs) in both transductive and inductive settings. Experiments on both
homogeneous graphs and knowledge graphs show that the proposed NBFNet
outperforms existing methods by a large margin in both transductive and
inductive settings, achieving new state-of-the-art results. | [
"cs.LG"
] |
Recent work has exposed the vulnerability of computer vision models to vector
field attacks. Due to the widespread usage of such models in safety-critical
applications, it is crucial to quantify their robustness against such spatial
transformations. However, existing work only provides empirical robustness
quantification against vector field deformations via adversarial attacks, which
lack provable guarantees. In this work, we propose novel convex relaxations,
enabling us, for the first time, to provide a certificate of robustness against
vector field transformations. Our relaxations are model-agnostic and can be
leveraged by a wide range of neural network verifiers. Experiments on various
network architectures and different datasets demonstrate the effectiveness and
scalability of our method. | [
"cs.LG",
"cs.AI",
"cs.CV",
"stat.ML"
] |
Electric utilities are struggling to manage increasing wildfire risk in a
hotter and drier climate. Utility transmission and distribution lines regularly
ignite destructive fires when they make contact with surrounding vegetation.
Trimming vegetation to maintain the separation from utility assets is as
critical to safety as it is difficult. Each utility has tens of thousands of
linear miles to manage, poor knowledge of where those assets are located, and
no way to prioritize trimming. Feature-enhanced convolutional neural networks
(CNNs) have proven effective in this problem space. Histograms of oriented
gradients (HOG) and Hough transforms are used to increase the salience of the
linear structures like power lines and poles. Data is frequently taken from
drone or satellite footage, but Google Street View offers an even more scalable
and lower cost solution. This paper uses $1,320$ images scraped from Street
View, transfer learning on popular CNNs, and feature engineering to place
images in one of three classes: (1) no utility systems, (2) utility systems
with no overgrown vegetation, or (3) utility systems with overgrown vegetation.
The CNN output thus yields a prioritized vegetation management system and
creates a geotagged map of utility assets as a byproduct. Test set accuracy
with reached $80.15\%$ using VGG11 with a trained first layer and classifier,
and a model ensemble correctly classified $88.88\%$ of images with risky
vegetation overgrowth. | [
"cs.CV",
"cs.AI",
"cs.LG"
] |
Correspondence-based shape models are key to various medical imaging
applications that rely on a statistical analysis of anatomies. Such shape
models are expected to represent consistent anatomical features across the
population for population-specific shape statistics. Early approaches for
correspondence placement rely on nearest neighbor search for simpler anatomies.
Coordinate transformations for shape correspondence hold promise to address the
increasing anatomical complexities. Nonetheless, due to the inherent
shape-level geometric complexity and population-level shape variation, the
coordinate-wise correspondence often does not translate to the anatomical
correspondence. An alternative, group-wise approach for correspondence
placement explicitly models the trade-off between geometric description and the
population's statistical compactness. However, these models achieve limited
success in resolving nonlinear shape correspondence. Recent works have
addressed this limitation by adopting an application-specific notion of
correspondence through lifting positional data to a higher dimensional feature
space. However, they heavily rely on manual expertise to create domain-specific
features and consistent landmarks. This paper proposes an automated feature
learning approach, using deep convolutional neural networks to extract
correspondence-friendly features from shape ensembles. Further, an unsupervised
domain adaptation scheme is introduced to augment the pretrained geometric
features with new anatomies. Results on anatomical datasets of human scapula,
femur, and pelvis bones demonstrate that features learned in supervised fashion
show improved performance for correspondence estimation compared to the manual
features. Further, unsupervised learning is demonstrated to learn complex
anatomy features using the supervised domain adaptation from features learned
on simpler anatomy. | [
"cs.CV",
"cs.LG"
] |
Reinforcement learning (RL) algorithms are typically limited to learning a
single solution of a specified task, even though there often exists diverse
solutions to a given task. Compared with learning a single solution, learning a
set of diverse solutions is beneficial because diverse solutions enable robust
few-shot adaptation and allow the user to select a preferred solution. Although
previous studies have showed that diverse behaviors can be modeled with a
policy conditioned on latent variables, an approach for modeling an infinite
set of diverse solutions with continuous latent variables has not been
investigated. In this study, we propose an RL method that can learn infinitely
many solutions by training a policy conditioned on a continuous or discrete
low-dimensional latent variable. Through continuous control tasks, we
demonstrate that our method can learn diverse solutions in a data-efficient
manner and that the solutions can be used for few-shot adaptation to solve
unseen tasks. | [
"stat.ML",
"cs.AI",
"cs.LG"
] |
Deep generative models based on Generative Adversarial Networks (GANs) have
demonstrated impressive sample quality but in order to work they require a
careful choice of architecture, parameter initialization, and selection of
hyper-parameters. This fragility is in part due to a dimensional mismatch or
non-overlapping support between the model distribution and the data
distribution, causing their density ratio and the associated f-divergence to be
undefined. We overcome this fundamental limitation and propose a new
regularization approach with low computational cost that yields a stable GAN
training procedure. We demonstrate the effectiveness of this regularizer across
several architectures trained on common benchmark image generation tasks. Our
regularization turns GAN models into reliable building blocks for deep
learning. | [
"cs.LG",
"stat.ML"
] |
Accurate detection and tracking of objects is vital for effective video
understanding. In previous work, the two tasks have been combined in a way that
tracking is based heavily on detection, but the detection benefits marginally
from the tracking. To increase synergy, we propose to more tightly integrate
the tasks by conditioning the object detection in the current frame on
tracklets computed in prior frames. With this approach, the object detection
results not only have high detection responses, but also improved coherence
with the existing tracklets. This greater coherence leads to estimated object
trajectories that are smoother and more stable than the jittered paths obtained
without tracklet-conditioned detection. Over extensive experiments, this
approach is shown to achieve state-of-the-art performance in terms of both
detection and tracking accuracy, as well as noticeable improvements in tracking
stability. | [
"cs.CV"
] |
We present a novel method that can learn a graph representation from
multivariate data. In our representation, each node represents a cluster of
data points and each edge represents the subset-superset relationship between
clusters, which can be mutually overlapped. The key to our method is to use
formal concept analysis (FCA), which can extract hierarchical relationships
between clusters based on the algebraic closedness property. We empirically
show that our method can effectively extract hierarchical structures of
clusters compared to the baseline method. | [
"cs.LG",
"stat.ML"
] |
The main success stories of deep learning, starting with ImageNet, depend on
deep convolutional networks, which on certain tasks perform significantly
better than traditional shallow classifiers, such as support vector machines,
and also better than deep fully connected networks; but what is so special
about deep convolutional networks? Recent results in approximation theory
proved an exponential advantage of deep convolutional networks with or without
shared weights in approximating functions with hierarchical locality in their
compositional structure. More recently, the hierarchical structure was proved
to be hard to learn from data, suggesting that it is a powerful prior embedded
in the architecture of the network. These mathematical results, however, do not
say which real-life tasks correspond to input-output functions with
hierarchical locality. To evaluate this, we consider a set of visual tasks
where we disrupt the local organization of images via "deterministic
scrambling" to later perform a visual task on these images structurally-altered
in the same way for training and testing. For object recognition we find, as
expected, that scrambling does not affect the performance of shallow or deep
fully connected networks contrary to the out-performance of convolutional
networks. Not all tasks involving images are however affected. Texture
perception and global color estimation are much less sensitive to deterministic
scrambling showing that the underlying functions corresponding to these tasks
are not hierarchically local; and also counter-intuitively showing that these
tasks are better approximated by networks that are not deep (texture) nor
convolutional (color). Altogether, these results shed light into the importance
of matching a network architecture with its embedded prior of the task to be
learned. | [
"cs.LG",
"eess.IV",
"q-bio.NC",
"stat.ML"
] |
Increasing attention to the research on activity monitoring in smart homes
has motivated the employment of ambient intelligence to reduce the deployment
cost and solve the privacy issue. Several approaches have been proposed for
multi-resident activity recognition, however, there still lacks a comprehensive
benchmark for future research and practical selection of models. In this paper
we study different methods for multi-resident activity recognition and evaluate
them on same sets of data. The experimental results show that recurrent neural
network with gated recurrent units is better than other models and also
considerably efficient, and that using combined activities as single labels is
more effective than represent them as separate labels. | [
"cs.CV"
] |
Manual counting of mitotic tumor cells in tissue sections constitutes one of
the strongest prognostic markers for breast cancer. This procedure, however, is
time-consuming and error-prone. We developed a method to automatically detect
mitotic figures in breast cancer tissue sections based on convolutional neural
networks (CNNs). Application of CNNs to hematoxylin and eosin (H&E) stained
histological tissue sections is hampered by: (1) noisy and expensive reference
standards established by pathologists, (2) lack of generalization due to
staining variation across laboratories, and (3) high computational requirements
needed to process gigapixel whole-slide images (WSIs). In this paper, we
present a method to train and evaluate CNNs to specifically solve these issues
in the context of mitosis detection in breast cancer WSIs. First, by combining
image analysis of mitotic activity in phosphohistone-H3 (PHH3) restained slides
and registration, we built a reference standard for mitosis detection in entire
H&E WSIs requiring minimal manual annotation effort. Second, we designed a data
augmentation strategy that creates diverse and realistic H&E stain variations
by modifying the hematoxylin and eosin color channels directly. Using it during
training combined with network ensembling resulted in a stain invariant mitosis
detector. Third, we applied knowledge distillation to reduce the computational
requirements of the mitosis detection ensemble with a negligible loss of
performance. The system was trained in a single-center cohort and evaluated in
an independent multicenter cohort from The Cancer Genome Atlas on the three
tasks of the Tumor Proliferation Assessment Challenge (TUPAC). We obtained a
performance within the top-3 best methods for most of the tasks of the
challenge. | [
"cs.CV"
] |
In this paper, from a theoretical perspective, we study how powerful graph
neural networks (GNNs) can be for learning approximation algorithms for
combinatorial problems. To this end, we first establish a new class of GNNs
that can solve a strictly wider variety of problems than existing GNNs. Then,
we bridge the gap between GNN theory and the theory of distributed local
algorithms. We theoretically demonstrate that the most powerful GNN can learn
approximation algorithms for the minimum dominating set problem and the minimum
vertex cover problem with some approximation ratios with the aid of the theory
of distributed local algorithms. We also show that most of the existing GNNs
such as GIN, GAT, GCN, and GraphSAGE cannot perform better than with these
ratios. This paper is the first to elucidate approximation ratios of GNNs for
combinatorial problems. Furthermore, we prove that adding coloring or
weak-coloring to each node feature improves these approximation ratios. This
indicates that preprocessing and feature engineering theoretically strengthen
model capabilities. | [
"cs.LG",
"stat.ML"
] |
Deep neural network models have recently draw lots of attention, as it
consistently produce impressive results in many computer vision tasks such as
image classification, object detection, etc. However, interpreting such model
and show the reason why it performs quite well becomes a challenging question.
In this paper, we propose a novel method to interpret the neural network models
with attention mechanism. Inspired by the heatmap visualization, we analyze the
relation between classification accuracy with the attention based heatmap. An
improved attention based method is also included and illustrate that a better
classifier can be interpreted by the attention based heatmap. | [
"cs.LG",
"cs.CV",
"stat.ML"
] |
Face deepfake detection has seen impressive results recently. Nearly all
existing deep learning techniques for face deepfake detection are fully
supervised and require labels during training. In this paper, we design a novel
deepfake detection method via unsupervised contrastive learning. We first
generate two different transformed versions of an image and feed them into two
sequential sub-networks, i.e., an encoder and a projection head. The
unsupervised training is achieved by maximizing the correspondence degree of
the outputs of the projection head. To evaluate the detection performance of
our unsupervised method, we further use the unsupervised features to train an
efficient linear classification network. Extensive experiments show that our
unsupervised learning method enables comparable detection performance to
state-of-the-art supervised techniques, in both the intra- and inter-dataset
settings. We also conduct ablation studies for our method. | [
"cs.CV",
"cs.LG"
] |
The regression of multiple inter-connected sequence data is a problem in
various disciplines. Formally, we name the regression problem of multiple
inter-connected data entities as the "dynamic network regression" in this
paper. Within the problem of stock forecasting or traffic speed prediction, we
need to consider both the trends of the entities and the relationships among
the entities. A majority of existing approaches can't capture that information
together. Some of the approaches are proposed to deal with the sequence data,
like LSTM. The others use the prior knowledge in a network to get a fixed graph
structure and do prediction on some unknown entities, like GCN. To overcome the
limitations in those methods, we propose a novel graph neural network, namely
Graph Neural Lasso (GNL), to deal with the dynamic network problem. GNL extends
the GDU (gated diffusive unit) as the base neuron to capture the information
behind the sequence. Rather than using a fixed graph structure, GNL can learn
the dynamic graph structure automatically. By adding the attention mechanism in
GNL, we can learn the dynamic relations among entities within each network
snapshot. Combining these two parts, GNL is able to model the dynamic network
problem well. Experimental results provided on two networked sequence datasets,
i.e., Nasdaq-100 and METR-LA, show that GNL can address the network regression
problem very well and is also very competitive among the existing approaches. | [
"cs.LG",
"cs.NE",
"stat.ML"
] |
Autonomous AI systems will be entering human society in the near future to
provide services and work alongside humans. For those systems to be accepted
and trusted, the users should be able to understand the reasoning process of
the system, i.e. the system should be transparent. System transparency enables
humans to form coherent explanations of the system's decisions and actions.
Transparency is important not only for user trust, but also for software
debugging and certification. In recent years, Deep Neural Networks have made
great advances in multiple application areas. However, deep neural networks are
opaque. In this paper, we report on work in transparency in Deep Reinforcement
Learning Networks (DRLN). Such networks have been extremely successful in
accurately learning action control in image input domains, such as Atari games.
In this paper, we propose a novel and general method that (a) incorporates
explicit object recognition processing into deep reinforcement learning models,
(b) forms the basis for the development of "object saliency maps", to provide
visualization of internal states of DRLNs, thus enabling the formation of
explanations and (c) can be incorporated in any existing deep reinforcement
learning framework. We present computational results and human experiments to
evaluate our approach. | [
"cs.LG",
"stat.ML"
] |
Sample efficiency is critical in solving real-world reinforcement learning
problems, where agent-environment interactions can be costly. Imitation
learning from expert advice has proved to be an effective strategy for reducing
the number of interactions required to train a policy. Online imitation
learning, which interleaves policy evaluation and policy optimization, is a
particularly effective technique with provable performance guarantees. In this
work, we seek to further accelerate the convergence rate of online imitation
learning, thereby making it more sample efficient. We propose two model-based
algorithms inspired by Follow-the-Leader (FTL) with prediction: MoBIL-VI based
on solving variational inequalities and MoBIL-Prox based on stochastic
first-order updates. These two methods leverage a model to predict future
gradients to speed up policy learning. When the model oracle is learned online,
these algorithms can provably accelerate the best known convergence rate up to
an order. Our algorithms can be viewed as a generalization of stochastic
Mirror-Prox (Juditsky et al., 2011), and admit a simple constructive FTL-style
analysis of performance. | [
"cs.LG",
"stat.ML"
] |
This paper proposes a Fully Spiking Hybrid Neural Network (FSHNN) for
energy-efficient and robust object detection in resource-constrained platforms.
The network architecture is based on Convolutional SNN using
leaky-integrate-fire neuron models. The model combines unsupervised Spike
Time-Dependent Plasticity (STDP) learning with back-propagation (STBP) learning
methods and also uses Monte Carlo Dropout to get an estimate of the uncertainty
error. FSHNN provides better accuracy compared to DNN based object detectors
while being 150X energy-efficient. It also outperforms these object detectors,
when subjected to noisy input data and less labeled training data with a lower
uncertainty error. | [
"cs.CV",
"cs.AI",
"eess.IV"
] |
State-of-the-art results on image recognition tasks are achieved using
over-parameterized learning algorithms that (nearly) perfectly fit the training
set and are known to fit well even random labels. This tendency to memorize the
labels of the training data is not explained by existing theoretical analyses.
Memorization of the training data also presents significant privacy risks when
the training data contains sensitive personal information and thus it is
important to understand whether such memorization is necessary for accurate
learning.
We provide the first conceptual explanation and a theoretical model for this
phenomenon. Specifically, we demonstrate that for natural data distributions
memorization of labels is necessary for achieving close-to-optimal
generalization error. Crucially, even labels of outliers and noisy labels need
to be memorized. The model is motivated and supported by the results of several
recent empirical works. In our model, data is sampled from a mixture of
subpopulations and our results show that memorization is necessary whenever the
distribution of subpopulation frequencies is long-tailed. Image and text data
is known to be long-tailed and therefore our results establish a formal link
between these empirical phenomena. Our results allow to quantify the cost of
limiting memorization in learning and explain the disparate effects that
privacy and model compression have on different subgroups. | [
"cs.LG",
"stat.ML"
] |
Reinforcement learning (RL) is a foundation of learning in biological systems
and provides a framework to address numerous challenges with real-world
artificial intelligence applications. Efficient implementations of RL
techniques could allow for agents deployed in edge-use cases to gain novel
abilities, such as improved navigation, understanding complex situations and
critical decision making. Towards this goal, we describe a flexible
architecture to carry out reinforcement learning on neuromorphic platforms.
This architecture was implemented using an Intel neuromorphic processor and
demonstrated solving a variety of tasks using spiking dynamics. Our study
proposes a usable energy efficient solution for real-world RL applications and
demonstrates applicability of the neuromorphic platforms for RL problems. | [
"cs.LG",
"cs.AI",
"I.2"
] |
Federated learning is vulnerable to various attacks, such as model poisoning
and backdoor attacks, even if some existing defense strategies are used. To
address this challenge, we propose an attack-adaptive aggregation strategy to
defend against various attacks for robust federated learning. The proposed
approach is based on training a neural network with an attention mechanism that
learns the vulnerability of federated learning models from a set of plausible
attacks. To the best of our knowledge, our aggregation strategy is the first
one that can be adapted to defend against various attacks in a data-driven
fashion. Our approach has achieved competitive performance in defending model
poisoning and backdoor attacks in federated learning tasks on image and text
datasets. | [
"cs.LG",
"cs.CR"
] |
Visual Query Answering (VQA) is of great significance in offering people
convenience: one can raise a question for details of objects, or high-level
understanding about the scene, over an image. This paper proposes a novel
method to address the VQA problem. In contrast to prior works, our method that
targets single scene VQA, replies on graph-based techniques and involves
reasoning. In a nutshell, our approach is centered on three graphs. The first
graph, referred to as inference graph GI , is constructed via learning over
labeled data. The other two graphs, referred to as query graph Q and
entity-attribute graph GEA, are generated from natural language query Qnl and
image Img, that are issued from users, respectively. As GEA often does not take
sufficient information to answer Q, we develop techniques to infer missing
information of GEA with GI . Based on GEA and Q, we provide techniques to find
matches of Q in GEA, as the answer of Qnl in Img. Unlike commonly used VQA
methods that are based on end-to-end neural networks, our graph-based method
shows well-designed reasoning capability, and thus is highly interpretable. We
also create a dataset on soccer match (Soccer-VQA) with rich annotations. The
experimental results show that our approach outperforms the state-of-the-art
method and has high potential for future investigation. | [
"cs.CV"
] |
Computational tools for forecasting yields and prices for fresh produce have
been based on traditional machine learning approaches or time series modelling.
We propose here an alternate approach based on deep learning algorithms for
forecasting strawberry yields and prices in Santa Barbara county, California.
Building the proposed forecasting model comprises three stages: first, the
station-based ensemble model (ATT-CNN-LSTM-SeriesNet_Ens) with its compound
deep learning components, SeriesNet with Gated Recurrent Unit (GRU) and
Convolutional Neural Network LSTM with Attention layer (Att-CNN-LSTM), are
trained and tested using the station-based soil temperature and moisture data
of SantaBarbara as input and the corresponding strawberry yields or prices as
output. Secondly, the remote sensing ensemble model (SIM_CNN-LSTM_Ens), which
is an ensemble model of Convolutional NeuralNetwork LSTM (CNN-LSTM) models, is
trained and tested using satellite images of the same county as input mapped to
the same yields and prices as output. These two ensembles forecast strawberry
yields and prices with minimal forecasting errors and highest model correlation
for five weeks ahead forecasts.Finally, the forecasts of these two models are
ensembled to have a final forecasted value for yields and prices by introducing
a voting ensemble. Based on an aggregated performance measure (AGM), it is
found that this voting ensemble not only enhances the forecasting performance
by 5% compared to its best performing component model but also outperforms the
Deep Learning (DL) ensemble model found in literature by 33% for forecasting
yields and 21% for forecasting prices | [
"cs.LG",
"cs.AI"
] |
Point cloud analysis is a fundamental task in 3D computer vision. Most
previous works have conducted experiments on synthetic datasets with
well-aligned data; while real-world point clouds are often not pre-aligned. How
to achieve rotation invariance remains an open problem in point cloud analysis.
To meet this challenge, we propose a novel approach toward achieving
rotation-invariant (RI) representations by combining local geometry with global
topology. In our local-global-representation (LGR)-Net, we have designed a
two-branch network where one stream encodes local geometric RI features and the
other encodes global topology-preserving RI features. Motivated by the
observation that local geometry and global topology have different yet
complementary RI responses in varying regions, two-branch RI features are fused
by an innovative multi-layer perceptron (MLP) based attention module. To the
best of our knowledge, this work is the first principled approach toward
adaptively combining global and local information under the context of RI point
cloud analysis. Extensive experiments have demonstrated that our LGR-Net
achieves the state-of-the-art performance on various rotation-augmented
versions of ModelNet40, ShapeNet, ScanObjectNN, and S3DIS. | [
"cs.CV"
] |
Estimation of pain intensity from facial expressions captured in videos has
an immense potential for health care applications. Given the challenges related
to subjective variations of facial expressions, and operational capture
conditions, the accuracy of state-of-the-art DL models for recognizing facial
expressions may decline. Domain adaptation has been widely explored to
alleviate the problem of domain shifts that typically occur between video data
captured across various source and target domains. Moreover, given the
laborious task of collecting and annotating videos, and subjective bias due to
ambiguity among adjacent intensity levels, weakly-supervised learning is
gaining attention in such applications. State-of-the-art WSL models are
typically formulated as regression problems, and do not leverage the ordinal
relationship among pain intensity levels, nor temporal coherence of multiple
consecutive frames. This paper introduces a new DL model for weakly-supervised
DA with ordinal regression that can be adapted using target domain videos with
coarse labels provided on a periodic basis. The WSDA-OR model enforces ordinal
relationships among intensity levels assigned to target sequences, and
associates multiple relevant frames to sequence-level labels. In particular, it
learns discriminant and domain-invariant feature representations by integrating
multiple instance learning with deep adversarial DA, where soft Gaussian labels
are used to efficiently represent the weak ordinal sequence-level labels from
target domain. The proposed approach was validated using RECOLA video dataset
as fully-labeled source domain data, and UNBC-McMaster shoulder pain video
dataset as weakly-labeled target domain data. We have also validated WSDA-OR on
BIOVID and Fatigue datasets for sequence level estimation. | [
"cs.CV"
] |
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