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This paper presents a novel obstacle avoidance system for road robots
equipped with RGB-D sensor that captures scenes of its way forward. The purpose
of the system is to have road robots move around autonomously and constantly
without any collision even with small obstacles, which are often missed by
existing solutions. For each input RGB-D image, the system uses a new two-stage
semantic segmentation network followed by the morphological processing to
generate the accurate semantic map containing road and obstacles. Based on the
map, the local path planning is applied to avoid possible collision.
Additionally, optical flow supervision and motion blurring augmented training
scheme is applied to improve temporal consistency between adjacent frames and
overcome the disturbance caused by camera shake. Various experiments are
conducted to show that the proposed architecture obtains high performance both
in indoor and outdoor scenarios. | [
"cs.CV"
] |
Functional Electrical Stimulation (FES) can restore motion to a paralysed
person's muscles. Yet, control stimulating many muscles to restore the
practical function of entire limbs is an unsolved problem. Current
neurostimulation engineering still relies on 20th Century control approaches
and correspondingly shows only modest results that require daily tinkering to
operate at all. Here, we present our state of the art Deep Reinforcement
Learning (RL) developed for real time adaptive neurostimulation of paralysed
legs for FES cycling. Core to our approach is the integration of a personalised
neuromechanical component into our reinforcement learning framework that allows
us to train the model efficiently without demanding extended training sessions
with the patient and working out of the box. Our neuromechanical component
includes merges musculoskeletal models of muscle and or tendon function and a
multistate model of muscle fatigue, to render the neurostimulation responsive
to a paraplegic's cyclist instantaneous muscle capacity. Our RL approach
outperforms PID and Fuzzy Logic controllers in accuracy and performance.
Crucially, our system learned to stimulate a cyclist's legs from ramping up
speed at the start to maintaining a high cadence in steady state racing as the
muscles fatigue. A part of our RL neurostimulation system has been successfully
deployed at the Cybathlon 2020 bionic Olympics in the FES discipline with our
paraplegic cyclist winning the Silver medal among 9 competing teams. | [
"cs.LG"
] |
The computer vision community is currently focusing on solving action
recognition problems in real videos, which contain thousands of samples with
many challenges. In this process, Deep Convolutional Neural Networks (D-CNNs)
have played a significant role in advancing the state-of-the-art in various
vision-based action recognition systems. Recently, the introduction of residual
connections in conjunction with a more traditional CNN model in a single
architecture called Residual Network (ResNet) has shown impressive performance
and great potential for image recognition tasks. In this paper, we investigate
and apply deep ResNets for human action recognition using skeletal data
provided by depth sensors. Firstly, the 3D coordinates of the human body joints
carried in skeleton sequences are transformed into image-based representations
and stored as RGB images. These color images are able to capture the
spatial-temporal evolutions of 3D motions from skeleton sequences and can be
efficiently learned by D-CNNs. We then propose a novel deep learning
architecture based on ResNets to learn features from obtained color-based
representations and classify them into action classes. The proposed method is
evaluated on three challenging benchmark datasets including MSR Action 3D,
KARD, and NTU-RGB+D datasets. Experimental results demonstrate that our method
achieves state-of-the-art performance for all these benchmarks whilst requiring
less computation resource. In particular, the proposed method surpasses
previous approaches by a significant margin of 3.4% on MSR Action 3D dataset,
0.67% on KARD dataset, and 2.5% on NTU-RGB+D dataset. | [
"cs.CV"
] |
In-vehicle human object identification plays an important role in
vision-based automated vehicle driving systems while objects such as
pedestrians and vehicles on roads or streets are the primary targets to protect
from driverless vehicles. A challenge is the difficulty to detect objects in
moving under the wild conditions, while illumination and image quality could
drastically vary. In this work, to address this challenge, we exploit Deep
Convolutional Generative Adversarial Networks (DCGANs) with Single Shot
Detector (SSD) to handle with the wild conditions. In our work, a GAN was
trained with low-quality images to handle with the challenges arising from the
wild conditions in smart cities, while a cascaded SSD is employed as the object
detector to perform with the GAN. We used tested our approach under wild
conditions using taxi driver videos on London street in both daylight and night
times, and the tests from in-vehicle videos demonstrate that this strategy can
drastically achieve a better detection rate under the wild conditions. | [
"cs.CV",
"cs.LG",
"eess.IV"
] |
We propose the first multi-frame video object detection framework trained to
detect great apes. It is applicable to challenging camera trap footage in
complex jungle environments and extends a traditional feature pyramid
architecture by adding self-attention driven feature blending in both the
spatial as well as the temporal domain. We demonstrate that this extension can
detect distinctive species appearance and motion signatures despite significant
partial occlusion. We evaluate the framework using 500 camera trap videos of
great apes from the Pan African Programme containing 180K frames, which we
manually annotated with accurate per-frame animal bounding boxes. These clips
contain significant partial occlusions, challenging lighting, dynamic
backgrounds, and natural camouflage effects. We show that our approach performs
highly robustly and significantly outperforms frame-based detectors. We also
perform detailed ablation studies and validation on the full ILSVRC 2015 VID
data corpus to demonstrate wider applicability at adequate performance levels.
We conclude that the framework is ready to assist human camera trap inspection
efforts. We publish code, weights, and ground truth annotations with this
paper. | [
"cs.CV"
] |
Since the study of deep convolutional neural network became prevalent, one of
the important discoveries is that a feature map from a convolutional network
can be extracted before going into the fully connected layer and can be used as
a saliency map for object detection. Furthermore, the model can use features
from each different layer for accurate object detection: the features from
different layers can have different properties. As the model goes deeper, it
has many latent skip connections and feature maps to elaborate object
detection. Although there are many intermediate layers that we can use for
semantic segmentation through skip connection, still the characteristics of
each skip connection and the best skip connection for this task are uncertain.
Therefore, in this study, we exhaustively research skip connections of
state-of-the-art deep convolutional networks and investigate the
characteristics of the features from each intermediate layer. In addition, this
study would suggest how to use a recent deep neural network model for semantic
segmentation and it would therefore become a cornerstone for later studies with
the state-of-the-art network models. | [
"cs.CV",
"cs.AI"
] |
The allocation of computation resources in the backbone is a crucial issue in
object detection. However, classification allocation pattern is usually adopted
directly to object detector, which is proved to be sub-optimal. In order to
reallocate the engaged computation resources in a more efficient way, we
present CR-NAS (Computation Reallocation Neural Architecture Search) that can
learn computation reallocation strategies across different feature resolution
and spatial position diectly on the target detection dataset. A two-level
reallocation space is proposed for both stage and spatial reallocation. A novel
hierarchical search procedure is adopted to cope with the complex search space.
We apply CR-NAS to multiple backbones and achieve consistent improvements. Our
CR-ResNet50 and CR-MobileNetV2 outperforms the baseline by 1.9% and 1.7% COCO
AP respectively without any additional computation budget. The models
discovered by CR-NAS can be equiped to other powerful detection neck/head and
be easily transferred to other dataset, e.g. PASCAL VOC, and other vision
tasks, e.g. instance segmentation. Our CR-NAS can be used as a plugin to
improve the performance of various networks, which is demanding. | [
"cs.CV",
"cs.LG"
] |
Machine learning models that can exploit the inherent structure in data have
gained prominence. In particular, there is a surge in deep learning solutions
for graph-structured data, due to its wide-spread applicability in several
fields. Graph attention networks (GAT), a recent addition to the broad class of
feature learning models in graphs, utilizes the attention mechanism to
efficiently learn continuous vector representations for semi-supervised
learning problems. In this paper, we perform a detailed analysis of GAT models,
and present interesting insights into their behavior. In particular, we show
that the models are vulnerable to heterogeneous rogue nodes and hence propose
novel regularization strategies to improve the robustness of GAT models. Using
benchmark datasets, we demonstrate performance improvements on semi-supervised
learning, using the proposed robust variant of GAT. | [
"cs.LG",
"stat.ML"
] |
With advances in reinforcement learning (RL), agents are now being developed
in high-stakes application domains such as healthcare and transportation.
Explaining the behavior of these agents is challenging, as the environments in
which they act have large state spaces, and their decision-making can be
affected by delayed rewards, making it difficult to analyze their behavior. To
address this problem, several approaches have been developed. Some approaches
attempt to convey the $\textit{global}$ behavior of the agent, describing the
actions it takes in different states. Other approaches devised $\textit{local}$
explanations which provide information regarding the agent's decision-making in
a particular state. In this paper, we combine global and local explanation
methods, and evaluate their joint and separate contributions, providing (to the
best of our knowledge) the first user study of combined local and global
explanations for RL agents. Specifically, we augment strategy summaries that
extract important trajectories of states from simulations of the agent with
saliency maps which show what information the agent attends to. Our results
show that the choice of what states to include in the summary (global
information) strongly affects people's understanding of agents: participants
shown summaries that included important states significantly outperformed
participants who were presented with agent behavior in a randomly set of chosen
world-states. We find mixed results with respect to augmenting demonstrations
with saliency maps (local information), as the addition of saliency maps did
not significantly improve performance in most cases. However, we do find some
evidence that saliency maps can help users better understand what information
the agent relies on in its decision making, suggesting avenues for future work
that can further improve explanations of RL agents. | [
"cs.LG",
"cs.AI",
"cs.HC",
"cs.NE",
"stat.ML"
] |
Graph representation learning, aiming to learn low-dimensional
representations which capture the geometric dependencies between nodes in the
original graph, has gained increasing popularity in a variety of graph analysis
tasks, including node classification and link prediction. Existing
representation learning methods based on graph neural networks and their
variants rely on the aggregation of neighborhood information, which makes it
sensitive to noises in the graph. In this paper, we propose Graph Denoising
Policy Network (short for GDPNet) to learn robust representations from noisy
graph data through reinforcement learning. GDPNet first selects signal
neighborhoods for each node, and then aggregates the information from the
selected neighborhoods to learn node representations for the down-stream tasks.
Specifically, in the signal neighborhood selection phase, GDPNet optimizes the
neighborhood for each target node by formulating the process of removing noisy
neighborhoods as a Markov decision process and learning a policy with
task-specific rewards received from the representation learning phase. In the
representation learning phase, GDPNet aggregates features from signal neighbors
to generate node representations for down-stream tasks, and provides
task-specific rewards to the signal neighbor selection phase. These two phases
are jointly trained to select optimal sets of neighbors for target nodes with
maximum cumulative task-specific rewards, and to learn robust representations
for nodes. Experimental results on node classification task demonstrate the
effectiveness of GDNet, outperforming the state-of-the-art graph representation
learning methods on several well-studied datasets. Additionally, GDPNet is
mathematically equivalent to solving the submodular maximizing problem, which
theoretically guarantees the best approximation to the optimal solution with
GDPNet. | [
"cs.LG",
"stat.ML"
] |
Remarkable performance from Transformer networks in Natural Language
Processing promote the development of these models in dealing with computer
vision tasks such as image recognition and segmentation. In this paper, we
introduce a novel framework, called Multi-level Multi-scale Point Transformer
(MLMSPT) that works directly on the irregular point clouds for representation
learning. Specifically, a point pyramid transformer is investigated to model
features with diverse resolutions or scales we defined, followed by a
multi-level transformer module to aggregate contextual information from
different levels of each scale and enhance their interactions. While a
multi-scale transformer module is designed to capture the dependencies among
representations across different scales. Extensive evaluation on public
benchmark datasets demonstrate the effectiveness and the competitive
performance of our methods on 3D shape classification, part segmentation and
semantic segmentation tasks. | [
"cs.CV"
] |
In the field of pattern recognition research, the method of using deep neural
networks based on improved computing hardware recently attracted attention
because of their superior accuracy compared to conventional methods. Deep
neural networks simulate the human visual system and achieve human equivalent
accuracy in image classification, object detection, and segmentation. This
chapter introduces the basic structure of deep neural networks that simulate
human neural networks. Then we identify the operational processes and
applications of conditional generative adversarial networks, which are being
actively researched based on the bottom-up and top-down mechanisms, the most
important functions of the human visual perception process. Finally, recent
developments in training strategies for effective learning of complex deep
neural networks are addressed. | [
"cs.CV",
"cs.LG"
] |
Recently there has been an increasing trend to use deep learning frameworks
for both 2D consumer images and for 3D medical images. However, there has been
little effort to use deep frameworks for volumetric vascular segmentation. We
wanted to address this by providing a freely available dataset of 12 annotated
two-photon vasculature microscopy stacks. We demonstrated the use of deep
learning framework consisting both 2D and 3D convolutional filters (ConvNet).
Our hybrid 2D-3D architecture produced promising segmentation result. We
derived the architectures from Lee et al. who used the ZNN framework initially
designed for electron microscope image segmentation. We hope that by sharing
our volumetric vasculature datasets, we will inspire other researchers to
experiment with vasculature dataset and improve the used network architectures. | [
"cs.CV",
"cs.AI",
"I.2.6; I.5.1; I.5.4; I.4.6"
] |
We propose a method for effectively utilizing weakly annotated image data in
an object detection tasks of breast ultrasound images. Given the problem
setting where a small, strongly annotated dataset and a large, weakly annotated
dataset with no bounding box information are available, training an object
detection model becomes a non-trivial problem. We suggest a controlled weight
for handling the effect of weakly annotated images in a two stage object
detection model. We~also present a subsequent active learning scheme for safely
assigning weakly annotated images a strong annotation using the trained model.
Experimental results showed a 24\% point increase in correct localization
(CorLoc) measure, which is the ratio of correctly localized and classified
images, by assigning the properly controlled weight. Performing active learning
after a model is trained showed an additional increase in CorLoc. We tested the
proposed method on the Stanford Dog datasets to assure that it can be applied
to general cases, where strong annotations are insufficient to obtain
resembling results. The presented method showed that higher performance is
achievable with lesser annotation effort. | [
"cs.CV",
"cs.LG",
"eess.IV"
] |
Clustering techniques attempt to group objects with similar properties into a
cluster. Clustering the nodes of an attributed graph, in which each node is
associated with a set of feature attributes, has attracted significant
attention. Graph convolutional networks (GCNs) represent an effective approach
for integrating the two complementary factors of node attributes and structural
information for attributed graph clustering. However, oversmoothing of GCNs
produces indistinguishable representations of nodes, such that the nodes in a
graph tend to be grouped into fewer clusters, and poses a challenge due to the
resulting performance drop. In this study, we propose a smoothness sensor for
attributed graph clustering based on adaptive smoothness-transition graph
convolutions, which senses the smoothness of a graph and adaptively terminates
the current convolution once the smoothness is saturated to prevent
oversmoothing. Furthermore, as an alternative to graph-level smoothness, a
novel fine-gained node-wise level assessment of smoothness is proposed, in
which smoothness is computed in accordance with the neighborhood conditions of
a given node at a certain order of graph convolution. In addition, a
self-supervision criterion is designed considering both the tightness within
clusters and the separation between clusters to guide the whole neural network
training process. Experiments show that the proposed methods significantly
outperform 12 other state-of-the-art baselines in terms of three different
metrics across four benchmark datasets. In addition, an extensive study reveals
the reasons for their effectiveness and efficiency. | [
"cs.CV",
"cs.AI"
] |
We propose a new method for segmentation-free joint estimation of orthogonal
planes, their intersection lines, relationship graph and corners lying at the
intersection of three orthogonal planes. Such unified scene exploration under
orthogonality allows for multitudes of applications such as semantic plane
detection or local and global scan alignment, which in turn can aid robot
localization or grasping tasks. Our two-stage pipeline involves a rough yet
joint estimation of orthogonal planes followed by a subsequent joint refinement
of plane parameters respecting their orthogonality relations. We form a graph
of these primitives, paving the way to the extraction of further reliable
features: lines and corners. Our experiments demonstrate the validity of our
approach in numerous scenarios from wall detection to 6D tracking, both on
synthetic and real data. | [
"cs.CV",
"cs.RO"
] |
Trajectory owner prediction is the basis for many applications such as
personalized recommendation, urban planning. Although much effort has been put
on this topic, the results archived are still not good enough. Existing methods
mainly employ RNNs to model trajectories semantically due to the inherent
sequential attribute of trajectories. However, these approaches are weak at
Point of Interest (POI) representation learning and trajectory feature
detection. Thus, the performance of existing solutions is far from the
requirements of practical applications. In this paper, we propose a novel
CNN-based Trajectory Owner Prediction (CNNTOP) method. Firstly, we connect all
POI according to trajectories from all users. The result is a connected graph
that can be used to generate more informative POI sequences than other
approaches. Secondly, we employ the Node2Vec algorithm to encode each POI into
a low-dimensional real value vector. Then, we transform each trajectory into a
fixed-dimensional matrix, which is similar to an image. Finally, a CNN is
designed to detect features and predict the owner of a given trajectory. The
CNN can extract informative features from the matrix representations of
trajectories by convolutional operations, Batch normalization, and $K$-max
pooling operations. Extensive experiments on real datasets demonstrate that
CNNTOP substantially outperforms existing solutions in terms of
macro-Precision, macro-Recall, macro-F1, and accuracy. | [
"cs.LG",
"stat.ML"
] |
Travel-time prediction constitutes a task of high importance in
transportation networks, with web mapping services like Google Maps regularly
serving vast quantities of travel time queries from users and enterprises
alike. Further, such a task requires accounting for complex spatiotemporal
interactions (modelling both the topological properties of the road network and
anticipating events -- such as rush hours -- that may occur in the future).
Hence, it is an ideal target for graph representation learning at scale. Here
we present a graph neural network estimator for estimated time of arrival (ETA)
which we have deployed in production at Google Maps. While our main
architecture consists of standard GNN building blocks, we further detail the
usage of training schedule methods such as MetaGradients in order to make our
model robust and production-ready. We also provide prescriptive studies:
ablating on various architectural decisions and training regimes, and
qualitative analyses on real-world situations where our model provides a
competitive edge. Our GNN proved powerful when deployed, significantly reducing
negative ETA outcomes in several regions compared to the previous production
baseline (40+% in cities like Sydney). | [
"cs.LG",
"cs.AI",
"cs.SI"
] |
Fully convolutional neural networks (FCN) have been shown to achieve
state-of-the-art performance on the task of classifying time series sequences.
We propose the augmentation of fully convolutional networks with long short
term memory recurrent neural network (LSTM RNN) sub-modules for time series
classification. Our proposed models significantly enhance the performance of
fully convolutional networks with a nominal increase in model size and require
minimal preprocessing of the dataset. The proposed Long Short Term Memory Fully
Convolutional Network (LSTM-FCN) achieves state-of-the-art performance compared
to others. We also explore the usage of attention mechanism to improve time
series classification with the Attention Long Short Term Memory Fully
Convolutional Network (ALSTM-FCN). Utilization of the attention mechanism
allows one to visualize the decision process of the LSTM cell. Furthermore, we
propose fine-tuning as a method to enhance the performance of trained models.
An overall analysis of the performance of our model is provided and compared to
other techniques. | [
"cs.LG",
"stat.ML"
] |
We present an efficient and practical algorithm for the online prediction of
discrete-time linear dynamical systems with a symmetric transition matrix. We
circumvent the non-convex optimization problem using improper learning:
carefully overparameterize the class of LDSs by a polylogarithmic factor, in
exchange for convexity of the loss functions. From this arises a
polynomial-time algorithm with a near-optimal regret guarantee, with an
analogous sample complexity bound for agnostic learning. Our algorithm is based
on a novel filtering technique, which may be of independent interest: we
convolve the time series with the eigenvectors of a certain Hankel matrix. | [
"cs.LG",
"cs.SY",
"math.OC",
"stat.ML"
] |
Energy minimization algorithms, such as graph cuts, enable the computation of
the MAP solution under certain probabilistic models such as Markov random
fields. However, for many computer vision problems, the MAP solution under the
model is not the ground truth solution. In many problem scenarios, the system
has access to certain statistics of the ground truth. For instance, in image
segmentation, the area and boundary length of the object may be known. In these
cases, we want to estimate the most probable solution that is consistent with
such statistics, i.e., satisfies certain equality or inequality constraints.
The above constrained energy minimization problem is NP-hard in general, and
is usually solved using Linear Programming formulations, which relax the
integrality constraints. This paper proposes a novel method that finds the
discrete optimal solution of such problems by maximizing the corresponding
Lagrangian dual. This method can be applied to any constrained energy
minimization problem whose unconstrained version is polynomial time solvable,
and can handle multiple, equality or inequality, and linear or non-linear
constraints. We demonstrate the efficacy of our method on the
foreground/background image segmentation problem, and show that it produces
impressive segmentation results with less error, and runs more than 20 times
faster than the state-of-the-art LP relaxation based approaches. | [
"cs.CV"
] |
The recent success of self-supervised learning can be largely attributed to
content-preserving transformations, which can be used to easily induce
invariances. While transformations generate positive sample pairs in
contrastive loss training, most recent work focuses on developing new objective
formulations, and pays relatively little attention to the transformations
themselves. In this paper, we introduce the framework of Generalized Data
Transformations to (1) reduce several recent self-supervised learning
objectives to a single formulation for ease of comparison, analysis, and
extension, (2) allow a choice between being invariant or distinctive to data
transformations, obtaining different supervisory signals, and (3) derive the
conditions that combinations of transformations must obey in order to lead to
well-posed learning objectives. This framework allows both invariance and
distinctiveness to be injected into representations simultaneously, and lets us
systematically explore novel contrastive objectives. We apply it to study
multi-modal self-supervision for audio-visual representation learning from
unlabelled videos, improving the state-of-the-art by a large margin, and even
surpassing supervised pretraining. We demonstrate results on a variety of
downstream video and audio classification and retrieval tasks, on datasets such
as HMDB-51, UCF-101, DCASE2014, ESC-50 and VGG-Sound. In particular, we achieve
new state-of-the-art accuracies of 72.8% on HMDB-51 and 95.2% on UCF-101. | [
"cs.CV"
] |
Working on the daily closing prices and logreturns, in this paper we deal
with the use of Hidden Markov Models (HMMs) to forecast the price of the
EUR/USD Futures. The aim of our work is to understand how the HMMs describe
different financial time series depending on their structure. Subsequently, we
analyse the forecasting methods exposed in the previous literature, putting on
evidence their pros and cons. | [
"stat.ML",
"cs.LG",
"91B84"
] |
Recent work on predicting patient outcomes in the Intensive Care Unit (ICU)
has focused heavily on the physiological time series data, largely ignoring
sparse data such as diagnoses and medications. When they are included, they are
usually concatenated in the late stages of a model, which may struggle to learn
from rarer disease patterns. Instead, we propose a strategy to exploit
diagnoses as relational information by connecting similar patients in a graph.
To this end, we propose LSTM-GNN for patient outcome prediction tasks: a hybrid
model combining Long Short-Term Memory networks (LSTMs) for extracting temporal
features and Graph Neural Networks (GNNs) for extracting the patient
neighbourhood information. We demonstrate that LSTM-GNNs outperform the
LSTM-only baseline on length of stay prediction tasks on the eICU database.
More generally, our results indicate that exploiting information from
neighbouring patient cases using graph neural networks is a promising research
direction, yielding tangible returns in supervised learning performance on
Electronic Health Records. | [
"cs.LG"
] |
Time series classification is an increasing research topic due to the vast
amount of time series data that are being created over a wide variety of
fields. The particularity of the data makes it a challenging task and different
approaches have been taken, including the distance based approach. 1-NN has
been a widely used method within distance based time series classification due
to it simplicity but still good performance. However, its supremacy may be
attributed to being able to use specific distances for time series within the
classification process and not to the classifier itself. With the aim of
exploiting these distances within more complex classifiers, new approaches have
arisen in the past few years that are competitive or which outperform the 1-NN
based approaches. In some cases, these new methods use the distance measure to
transform the series into feature vectors, bridging the gap between time series
and traditional classifiers. In other cases, the distances are employed to
obtain a time series kernel and enable the use of kernel methods for time
series classification. One of the main challenges is that a kernel function
must be positive semi-definite, a matter that is also addressed within this
review. The presented review includes a taxonomy of all those methods that aim
to classify time series using a distance based approach, as well as a
discussion of the strengths and weaknesses of each method. | [
"stat.ML",
"cs.LG"
] |
Context: in large-scale spatial surveys, the Point Spread Function (PSF)
varies across the instrument field of view (FOV). Local measurements of the
PSFs are given by the isolated stars images. Yet, these estimates may not be
directly usable for post-processings because of the observational noise and
potentially the aliasing. Aims: given a set of aliased and noisy stars images
from a telescope, we want to estimate well-resolved and noise-free PSFs at the
observed stars positions, in particular, exploiting the spatial correlation of
the PSFs across the FOV. Contributions: we introduce RCA (Resolved Components
Analysis) which is a noise-robust dimension reduction and super-resolution
method based on matrix factorization. We propose an original way of using the
PSFs spatial correlation in the restoration process through sparsity. The
introduced formalism can be applied to correlated data sets with respect to any
euclidean parametric space. Results: we tested our method on simulated
monochromatic PSFs of Euclid telescope (launch planned for 2020). The proposed
method outperforms existing PSFs restoration and dimension reduction methods.
We show that a coupled sparsity constraint on individual PSFs and their spatial
distribution yields a significant improvement on both the restored PSFs shapes
and the PSFs subspace identification, in presence of aliasing. Perspectives:
RCA can be naturally extended to account for the wavelength dependency of the
PSFs. | [
"cs.CV",
"astro-ph.IM",
"00"
] |
Localization technology is important for the development of indoor
location-based services (LBS). Global Positioning System (GPS) becomes invalid
in indoor environments due to the non-line-of-sight issue, so it is urgent to
develop a real-time high-accuracy localization approach for smartphones.
However, accurate localization is challenging due to issues such as real-time
response requirements, limited fingerprint samples and mobile device storage.
To address these problems, we propose a novel deep learning architecture:
Tensor-Generative Adversarial Network (TGAN).
We first introduce a transform-based 3D tensor to model fingerprint samples.
Instead of those passive methods that construct a fingerprint database as a
prior, our model applies artificial neural network with deep learning to train
network classifiers and then gives out estimations. Then we propose a novel
tensor-based super-resolution scheme using the generative adversarial network
(GAN) that adopts sparse coding as the generator network and a residual
learning network as the discriminator. Further, we analyze the performance of
tensor-GAN and implement a trace-based localization experiment, which achieves
better performance. Compared to existing methods for smartphones indoor
positioning, that are energy-consuming and high demands on devices, TGAN can
give out an improved solution in localization accuracy, response time and
implementation complexity. | [
"cs.LG",
"cs.NI",
"eess.SP"
] |
Object detection and tracking is a key task in autonomy. Specifically, 3D
object detection and tracking have been an emerging hot topic recently.
Although various methods have been proposed for object detection, uncertainty
in the 3D detection and tracking tasks has been less explored. Uncertainty
helps us tackle the error in the perception system and improve robustness. In
this paper, we propose a method for improving target tracking performance by
adding uncertainty regression to the SECOND detector, which is one of the most
representative algorithms of 3D object detection. Our method estimates
positional and dimensional uncertainties with Gaussian Negative Log-Likelihood
(NLL) Loss for estimation and introduces von-Mises NLL Loss for angular
uncertainty estimation. We fed the uncertainty output into a classical object
tracking framework and proved that our method increased the tracking
performance compared against the vanilla tracker with constant covariance
assumption. | [
"cs.CV",
"cs.LG"
] |
Learning powerful discriminative features for remote sensing image scene
classification is a challenging computer vision problem. In the past, most
classification approaches were based on handcrafted features. However, most
recent approaches to remote sensing scene classification are based on
Convolutional Neural Networks (CNNs). The de facto practice when learning these
CNN models is only to use original RGB patches as input with training performed
on large amounts of labeled data (ImageNet). In this paper, we show class
activation map (CAM) encoded CNN models, codenamed DDRL-AM, trained using
original RGB patches and attention map based class information provide
complementary information to the standard RGB deep models. To the best of our
knowledge, we are the first to investigate attention information encoded CNNs.
Additionally, to enhance the discriminability, we further employ a recently
developed object function called "center loss," which has proved to be very
useful in face recognition. Finally, our framework provides attention guidance
to the model in an end-to-end fashion. Extensive experiments on two benchmark
datasets show that our approach matches or exceeds the performance of other
methods. | [
"cs.CV",
"cs.LG"
] |
There is an emerging sense that the vulnerability of Image Convolutional
Neural Networks (CNN), i.e., sensitivity to image corruptions, perturbations,
and adversarial attacks, is connected with Texture Bias. This relative lack of
Shape Bias is also responsible for poor performance in Domain Generalization
(DG). The inclusion of a role of shape alleviates these vulnerabilities and
some approaches have achieved this by training on negative images, images
endowed with edge maps, or images with conflicting shape and texture
information. This paper advocates an explicit and complete representation of
shape using a classical computer vision approach, namely, representing the
shape content of an image with the shock graph of its contour map. The
resulting graph and its descriptor is a complete representation of contour
content and is classified using recent Graph Neural Network (GNN) methods. The
experimental results on three domain shift datasets, Colored MNIST, PACS, and
VLCS demonstrate that even without using appearance the shape-based approach
exceeds classical Image CNN based methods in domain generalization. | [
"cs.CV",
"cs.LG"
] |
Face recognition systems are present in many modern solutions and thousands
of applications in our daily lives. However, current solutions are not easily
scalable, especially when it comes to the addition of new targeted people. We
propose a cluster-matching-based approach for face recognition in video. In our
approach, we use unsupervised learning to cluster the faces present in both the
dataset and targeted videos selected for face recognition. Moreover, we design
a cluster matching heuristic to associate clusters in both sets that is also
capable of identifying when a face belongs to a non-registered person. Our
method has achieved a recall of 99.435% and a precision of 99.131% in the task
of video face recognition. Besides performing face recognition, it can also be
used to determine the video segments where each person is present. | [
"cs.CV",
"cs.AI",
"cs.LG",
"cs.MM"
] |
Inspired by the recent PointHop classification method, an unsupervised 3D
point cloud registration method, called R-PointHop, is proposed in this work.
R-PointHop first determines a local reference frame (LRF) for every point using
its nearest neighbors and finds its local attributes. Next, R-PointHop obtains
local-to-global hierarchical features by point downsampling, neighborhood
expansion, attribute construction and dimensionality reduction steps. Thus, we
can build the correspondence of points in the hierarchical feature space using
the nearest neighbor rule. Afterwards, a subset of salient points of good
correspondence is selected to estimate the 3D transformation. The use of LRF
allows for hierarchical features of points to be invariant with respect to
rotation and translation, thus making R-PointHop more robust in building point
correspondence even when rotation angles are large. Experiments are conducted
on the ModelNet40 and the Stanford Bunny dataset, which demonstrate the
effectiveness of R-PointHop on the 3D point cloud registration task. R-PointHop
is a green and accurate solution since its model size and training time are
smaller than those of deep learning methods by an order of magnitude while its
registration errors are smaller. Our codes are available on GitHub. | [
"cs.CV"
] |
VDN and QMIX are two popular value-based algorithms for cooperative MARL that
learn a centralized action value function as a monotonic mixing of per-agent
utilities. While this enables easy decentralization of the learned policy, the
restricted joint action value function can prevent them from solving tasks that
require significant coordination between agents at a given timestep. We show
that this problem can be overcome by improving the joint exploration of all
agents during training. Specifically, we propose a novel MARL approach called
Universal Value Exploration (UneVEn) that learns a set of related tasks
simultaneously with a linear decomposition of universal successor features.
With the policies of already solved related tasks, the joint exploration
process of all agents can be improved to help them achieve better coordination.
Empirical results on a set of exploration games, challenging cooperative
predator-prey tasks requiring significant coordination among agents, and
StarCraft II micromanagement benchmarks show that UneVEn can solve tasks where
other state-of-the-art MARL methods fail. | [
"cs.LG",
"cs.AI",
"cs.MA"
] |
Video inpainting, which aims at filling in missing regions of a video,
remains challenging due to the difficulty of preserving the precise spatial and
temporal coherence of video contents. In this work we propose a novel
flow-guided video inpainting approach. Rather than filling in the RGB pixels of
each frame directly, we consider video inpainting as a pixel propagation
problem. We first synthesize a spatially and temporally coherent optical flow
field across video frames using a newly designed Deep Flow Completion network.
Then the synthesized flow field is used to guide the propagation of pixels to
fill up the missing regions in the video. Specifically, the Deep Flow
Completion network follows a coarse-to-fine refinement to complete the flow
fields, while their quality is further improved by hard flow example mining.
Following the guide of the completed flow, the missing video regions can be
filled up precisely. Our method is evaluated on DAVIS and YouTube-VOS datasets
qualitatively and quantitatively, achieving the state-of-the-art performance in
terms of inpainting quality and speed. | [
"cs.CV"
] |
In image restoration tasks, like denoising and super resolution, continual
modulation of restoration levels is of great importance for real-world
applications, but has failed most of existing deep learning based image
restoration methods. Learning from discrete and fixed restoration levels, deep
models cannot be easily generalized to data of continuous and unseen levels.
This topic is rarely touched in literature, due to the difficulty of modulating
well-trained models with certain hyper-parameters. We make a step forward by
proposing a unified CNN framework that consists of few additional parameters
than a single-level model yet could handle arbitrary restoration levels between
a start and an end level. The additional module, namely AdaFM layer, performs
channel-wise feature modification, and can adapt a model to another restoration
level with high accuracy. By simply tweaking an interpolation coefficient, the
intermediate model - AdaFM-Net could generate smooth and continuous restoration
effects without artifacts. Extensive experiments on three image restoration
tasks demonstrate the effectiveness of both model training and modulation
testing. Besides, we carefully investigate the properties of AdaFM layers,
providing a detailed guidance on the usage of the proposed method. | [
"cs.CV"
] |
Sequence transduction models have been widely explored in many natural
language processing tasks. However, the target sequence usually consists of
discrete tokens which represent word indices in a given vocabulary. We barely
see the case where target sequence is composed of continuous vectors, where
each vector is an element of a time series taken successively in a temporal
domain. In this work, we introduce a new data set, named Action Generation Data
Set (AGDS) which is specifically designed to carry out the task of
caption-to-action generation. This data set contains caption-action pairs. The
caption is comprised of a sequence of words describing the interactive movement
between two people, and the action is a captured sequence of poses representing
the movement. This data set is introduced to study the ability of generating
continuous sequences through sequence transduction models. We also propose a
model to innovatively combine Multi-Head Attention (MHA) and Generative
Adversarial Network (GAN) together. In our model, we have one generator to
generate actions from captions and three discriminators where each of them is
designed to carry out a unique functionality: caption-action consistency
discriminator, pose discriminator and pose transition discriminator. This novel
design allowed us to achieve plausible generation performance which is
demonstrated in the experiments. | [
"cs.CV",
"cs.CL"
] |
Image quality assessment (IQA) is an important research topic for
understanding and improving visual experience. The current state-of-the-art IQA
methods are based on convolutional neural networks (CNNs). The performance of
CNN-based models is often compromised by the fixed shape constraint in batch
training. To accommodate this, the input images are usually resized and cropped
to a fixed shape, causing image quality degradation. To address this, we design
a multi-scale image quality Transformer (MUSIQ) to process native resolution
images with varying sizes and aspect ratios. With a multi-scale image
representation, our proposed method can capture image quality at different
granularities. Furthermore, a novel hash-based 2D spatial embedding and a scale
embedding is proposed to support the positional embedding in the multi-scale
representation. Experimental results verify that our method can achieve
state-of-the-art performance on multiple large scale IQA datasets such as
PaQ-2-PiQ, SPAQ and KonIQ-10k. | [
"cs.CV"
] |
Locating populations in rural areas of developing countries has attracted the
attention of humanitarian mapping projects since it is important to plan
actions that affect vulnerable areas. Recent efforts have tackled this problem
as the detection of buildings in aerial images. However, the quality and the
amount of rural building annotated data in open mapping services like
OpenStreetMap (OSM) is not sufficient for training accurate models for such
detection. Although these methods have the potential of aiding in the update of
rural building information, they are not accurate enough to automatically
update the rural building maps. In this paper, we explore a human-computer
interaction approach and propose an interactive method to support and optimize
the work of volunteers in OSM. The user is asked to verify/correct the
annotation of selected tiles during several iterations and therefore improving
the model with the new annotated data. The experimental results, with simulated
and real user annotation corrections, show that the proposed method greatly
reduces the amount of data that the volunteers of OSM need to verify/correct.
The proposed methodology could benefit humanitarian mapping projects, not only
by making more efficient the process of annotation but also by improving the
engagement of volunteers. | [
"cs.CV",
"cs.HC",
"eess.IV"
] |
Detecting the singular point accurately and efficiently is one of the most
important tasks for fingerprint recognition. In recent years, deep learning has
been gradually used in the fingerprint singular point detection. However,
current deep learning-based singular point detection methods are either
two-stage or multi-stage, which makes them time-consuming. More importantly,
their detection accuracy is yet unsatisfactory, especially in the case of the
low-quality fingerprint. In this paper, we make a Real One-Stage Effort to
detect fingerprint singular points more accurately and efficiently, and
therefore we name the proposed algorithm ROSE for short, in which the
multi-scale spatial attention, the Gaussian heatmap and the variant of focal
loss are applied together to achieve a higher detection rate. Experimental
results on the datasets FVC2002 DB1 and NIST SD4 show that our ROSE outperforms
the state-of-art algorithms in terms of detection rate, false alarm rate and
detection speed. | [
"cs.CV"
] |
Most existing Multi-Object Tracking (MOT) approaches follow the
Tracking-by-Detection paradigm and the data association framework where objects
are firstly detected and then associated. Although deep-learning based method
can noticeably improve the object detection performance and also provide good
appearance features for cross-frame association, the framework is not
completely end-to-end, and therefore the computation is huge while the
performance is limited. To address the problem, we present a completely
end-to-end approach that takes image-sequence/video as input and outputs
directly the located and tracked objects of learned types. Specifically, with
our introduced multi-object representation strategy, a global response map can
be accurately generated over frames, from which the trajectory of each tracked
object can be easily picked up, just like how a detector inputs an image and
outputs the bounding boxes of each detected object. The proposed model is fast
and accurate. Experimental results based on the MOT16 and MOT17 benchmarks show
that our proposed on-line tracker achieved state-of-the-art performance on
several tracking metrics. | [
"cs.CV",
"cs.LG"
] |
Event cameras are novel sensors with outstanding properties such as high
temporal resolution and high dynamic range. Despite these characteristics,
event-based vision has been held back by the shortage of labeled datasets due
to the novelty of event cameras. To overcome this drawback, we propose a task
transfer method that allows models to be trained directly with labeled images
and unlabeled event data. Compared to previous approaches, (i) our method
transfers from single images to events instead of high frame rate videos, and
(ii) does not rely on paired sensor data. To achieve this, we leverage the
generative event model to split event features into content and motion
features. This feature split enables to efficiently match the latent space for
events and images, which is crucial for a successful task transfer. Thus, our
approach unlocks the vast amount of existing image datasets for the training of
event-based neural networks. Our task transfer method consistently outperforms
methods applicable in the Unsupervised Domain Adaptation setting for object
detection by 0.26 mAP (increase by 93%) and classification by 2.7% accuracy. | [
"cs.CV"
] |
Accurate prediction of drug-target interaction (DTI) is essential for in
silico drug design. For the purpose, we propose a novel approach for predicting
DTI using a GNN that directly incorporates the 3D structure of a protein-ligand
complex. We also apply a distance-aware graph attention algorithm with gate
augmentation to increase the performance of our model. As a result, our model
shows better performance than docking and other deep learning methods for both
virtual screening and pose prediction. In addition, our model can reproduce the
natural population distribution of active molecules and inactive molecules. | [
"cs.LG",
"stat.ML"
] |
Decision trees and their ensembles are very popular models of supervised
machine learning. In this paper we merge the ideas underlying decision trees,
their ensembles and FCA by proposing a new supervised machine learning model
which can be constructed in polynomial time and is applicable for both
classification and regression problems. Specifically, we first propose a
polynomial-time algorithm for constructing a part of the concept lattice that
is based on a decision tree. Second, we describe a prediction scheme based on a
concept lattice for solving both classification and regression tasks with
prediction quality comparable to that of state-of-the-art models. | [
"cs.LG"
] |
The recent work of Gatys et al., who characterized the style of an image by
the statistics of convolutional neural network filters, ignited a renewed
interest in the texture generation and image stylization problems. While their
image generation technique uses a slow optimization process, recently several
authors have proposed to learn generator neural networks that can produce
similar outputs in one quick forward pass. While generator networks are
promising, they are still inferior in visual quality and diversity compared to
generation-by-optimization. In this work, we advance them in two significant
ways. First, we introduce an instance normalization module to replace batch
normalization with significant improvements to the quality of image
stylization. Second, we improve diversity by introducing a new learning
formulation that encourages generators to sample unbiasedly from the Julesz
texture ensemble, which is the equivalence class of all images characterized by
certain filter responses. Together, these two improvements take feed forward
texture synthesis and image stylization much closer to the quality of
generation-via-optimization, while retaining the speed advantage. | [
"cs.CV"
] |
Clustering is a fundamental tool in unsupervised learning, used to group
objects by distinguishing between similar and dissimilar features of a given
data set. One of the most common clustering algorithms is k-means.
Unfortunately, when dealing with real-world data many traditional clustering
algorithms are compromised by lack of clear separation between groups, noisy
observations, and/or outlying data points. Thus, robust statistical algorithms
are required for successful data analytics. Current methods that robustify
k-means clustering are specialized for either single or multi-membership data,
but do not perform competitively in both cases. We propose an extension of the
k-means algorithm, which we call Robust Trimmed k-means (RTKM) that
simultaneously identifies outliers and clusters points and can be applied to
either single- or multi-membership data. We test RTKM on various real-world
datasets and show that RTKM performs competitively with other methods on single
membership data with outliers and multi-membership data without outliers. We
also show that RTKM leverages its relative advantages to outperform other
methods on multi-membership data containing outliers. | [
"stat.ML",
"cs.LG",
"math.OC",
"90C26, 62F35",
"I.5.3"
] |
Recently, deep image compression has shown a big progress in terms of coding
efficiency and image quality improvement. However, relatively less attention
has been put on video compression using deep learning networks. In the paper,
we first propose a deep learning based bi-predictive coding network, called
BP-DVC Net, for video compression. Learned from the lesson of the conventional
video coding, a B-frame coding structure is incorporated in our BP-DVC Net.
While the bi-predictive coding in the conventional video codecs requires to
transmit to decoder sides the motion vectors for block motion and the residues
from prediction, our BP-DVC Net incorporates optical flow estimation networks
in both encoder and decoder sides so as not to transmit the motion information
to the decoder sides for coding efficiency improvement. Also, a bi-prediction
network in the BP-DVC Net is proposed and used to precisely predict the current
frame and to yield the resulting residues as small as possible. Furthermore,
our BP-DVC Net allows for the compressive feature maps to be entropy-coded
using the temporal context among the feature maps of adjacent frames. The
BP-DVC Net has an end-to-end video compression architecture with newly designed
flow and prediction losses. Experimental results show that the compression
performance of our proposed method is comparable to those of H.264, HEVC in
terms of PSNR and MS-SSIM. | [
"cs.CV"
] |
Meta-reinforcement learning algorithms can enable robots to acquire new
skills much more quickly, by leveraging prior experience to learn how to learn.
However, much of the current research on meta-reinforcement learning focuses on
task distributions that are very narrow. For example, a commonly used
meta-reinforcement learning benchmark uses different running velocities for a
simulated robot as different tasks. When policies are meta-trained on such
narrow task distributions, they cannot possibly generalize to more quickly
acquire entirely new tasks. Therefore, if the aim of these methods is to enable
faster acquisition of entirely new behaviors, we must evaluate them on task
distributions that are sufficiently broad to enable generalization to new
behaviors. In this paper, we propose an open-source simulated benchmark for
meta-reinforcement learning and multi-task learning consisting of 50 distinct
robotic manipulation tasks. Our aim is to make it possible to develop
algorithms that generalize to accelerate the acquisition of entirely new,
held-out tasks. We evaluate 7 state-of-the-art meta-reinforcement learning and
multi-task learning algorithms on these tasks. Surprisingly, while each task
and its variations (e.g., with different object positions) can be learned with
reasonable success, these algorithms struggle to learn with multiple tasks at
the same time, even with as few as ten distinct training tasks. Our analysis
and open-source environments pave the way for future research in multi-task
learning and meta-learning that can enable meaningful generalization, thereby
unlocking the full potential of these methods. | [
"cs.LG",
"cs.AI",
"cs.RO",
"stat.ML"
] |
Deep convolutional neural network (DCNN) has achieved remarkable performance
on object detection and speech recognition in recent years. However, the
excellent performance of a DCNN incurs high computational complexity and large
memory requirement. In this paper, an equal distance nonuniform quantization
(ENQ) scheme and a K-means clustering nonuniform quantization (KNQ) scheme are
proposed to reduce the required memory storage when low complexity hardware or
software implementations are considered. For the VGG-16 and the AlexNet, the
proposed nonuniform quantization schemes reduce the number of required memory
storage by approximately 50\% while achieving almost the same or even better
classification accuracy compared to the state-of-the-art quantization method.
Compared to the ENQ scheme, the proposed KNQ scheme provides a better tradeoff
when higher accuracy is required. | [
"cs.CV"
] |
The problem of reinforcement learning in an unknown and discrete Markov
Decision Process (MDP) under the average-reward criterion is considered, when
the learner interacts with the system in a single stream of observations,
starting from an initial state without any reset. We revisit the minimax lower
bound for that problem by making appear the local variance of the bias function
in place of the diameter of the MDP. Furthermore, we provide a novel analysis
of the KL-UCRL algorithm establishing a high-probability regret bound scaling
as $\widetilde {\mathcal O}\Bigl({\textstyle \sqrt{S\sum_{s,a}{\bf
V}^\star_{s,a}T}}\Big)$ for this algorithm for ergodic MDPs, where $S$ denotes
the number of states and where ${\bf V}^\star_{s,a}$ is the variance of the
bias function with respect to the next-state distribution following action $a$
in state $s$. The resulting bound improves upon the best previously known
regret bound $\widetilde {\mathcal O}(DS\sqrt{AT})$ for that algorithm, where
$A$ and $D$ respectively denote the maximum number of actions (per state) and
the diameter of MDP. We finally compare the leading terms of the two bounds in
some benchmark MDPs indicating that the derived bound can provide an order of
magnitude improvement in some cases. Our analysis leverages novel variations of
the transportation lemma combined with Kullback-Leibler concentration
inequalities, that we believe to be of independent interest. | [
"stat.ML",
"cs.LG",
"cs.SY"
] |
Tracking humans in crowded video sequences is an important constituent of
visual scene understanding. Increasing crowd density challenges visibility of
humans, limiting the scalability of existing pedestrian trackers to higher
crowd densities. For that reason, we propose to revitalize head tracking with
Crowd of Heads Dataset (CroHD), consisting of 9 sequences of 11,463 frames with
over 2,276,838 heads and 5,230 tracks annotated in diverse scenes. For
evaluation, we proposed a new metric, IDEucl, to measure an algorithm's
efficacy in preserving a unique identity for the longest stretch in image
coordinate space, thus building a correspondence between pedestrian crowd
motion and the performance of a tracking algorithm. Moreover, we also propose a
new head detector, HeadHunter, which is designed for small head detection in
crowded scenes. We extend HeadHunter with a Particle Filter and a color
histogram based re-identification module for head tracking. To establish this
as a strong baseline, we compare our tracker with existing state-of-the-art
pedestrian trackers on CroHD and demonstrate superiority, especially in
identity preserving tracking metrics. With a light-weight head detector and a
tracker which is efficient at identity preservation, we believe our
contributions will serve useful in advancement of pedestrian tracking in dense
crowds. | [
"cs.CV"
] |
We present FourierNet, a single shot, anchor-free, fully convolutional
instance segmentation method that predicts a shape vector. Consequently, this
shape vector is converted into the masks' contour points using a fast numerical
transform. Compared to previous methods, we introduce a new training technique,
where we utilize a differentiable shape decoder, which manages the automatic
weight balancing of the shape vector's coefficients. We used the Fourier series
as a shape encoder because of its coefficient interpretability and fast
implementation. FourierNet shows promising results compared to polygon
representation methods, achieving 30.6 mAP on the MS COCO 2017 benchmark. At
lower image resolutions, it runs at 26.6 FPS with 24.3 mAP. It reaches 23.3 mAP
using just eight parameters to represent the mask (note that at least four
parameters are needed for bounding box prediction only). Qualitative analysis
shows that suppressing a reasonable proportion of higher frequencies of Fourier
series, still generates meaningful masks. These results validate our
understanding that lower frequency components hold higher information for the
segmentation task, and therefore, we can achieve a compressed representation.
Code is available at: github.com/cogsys-tuebingen/FourierNet. | [
"cs.CV",
"eess.IV"
] |
Variational auto-encoders (VAEs) provide an attractive solution to image
generation problem. However, they tend to produce blurred and over-smoothed
images due to their dependence on pixel-wise reconstruction loss. This paper
introduces a new approach to alleviate this problem in the VAE based generative
models. Our model simultaneously learns to match the data, reconstruction loss
and the latent distributions of real and fake images to improve the quality of
generated samples. To compute the loss distributions, we introduce an
auto-encoder based discriminator model which allows an adversarial learning
procedure. The discriminator in our model also provides perceptual guidance to
the VAE by matching the learned similarity metric of the real and fake samples
in the latent space. To stabilize the overall training process, our model uses
an error feedback approach to maintain the equilibrium between competing
networks in the model. Our experiments show that the generated samples from our
proposed model exhibit a diverse set of attributes and facial expressions and
scale up to high-resolution images very well. | [
"cs.CV"
] |
We propose a decentralized learning algorithm over a general social network.
The algorithm leaves the training data distributed on the mobile devices while
utilizing a peer to peer model aggregation method. The proposed algorithm
allows agents with local data to learn a shared model explaining the global
training data in a decentralized fashion. The proposed algorithm can be viewed
as a Bayesian and peer-to-peer variant of federated learning in which each
agent keeps a "posterior probability distribution" over a global model
parameters. The agent update its "posterior" based on 1) the local training
data and 2) the asynchronous communication and model aggregation with their
1-hop neighbors. This Bayesian formulation allows for a systematic treatment of
model aggregation over any arbitrary connected graph. Furthermore, it provides
strong analytic guarantees on converge in the realizable case as well as a
closed form characterization of the rate of convergence. We also show that our
methodology can be combined with efficient Bayesian inference techniques to
train Bayesian neural networks in a decentralized manner. By empirical studies
we show that our theoretical analysis can guide the design of network/social
interactions and data partitioning to achieve convergence. | [
"stat.ML",
"cs.LG"
] |
In this paper, we present an end-to-end future-prediction model that focuses
on pedestrian safety. Specifically, our model uses previous video frames,
recorded from the perspective of the vehicle, to predict if a pedestrian will
cross in front of the vehicle. The long term goal of this work is to design a
fully autonomous system that acts and reacts as a defensive human driver would
--- predicting future events and reacting to mitigate risk. We focus on
pedestrian-vehicle interactions because of the high risk of harm to the
pedestrian if their actions are miss-predicted. Our end-to-end model consists
of two stages: the first stage is an encoder/decoder network that learns to
predict future video frames. The second stage is a deep spatio-temporal network
that utilizes the predicted frames of the first stage to predict the
pedestrian's future action. Our system achieves state-of-the-art accuracy on
pedestrian behavior prediction and future frames prediction on the Joint
Attention for Autonomous Driving (JAAD) dataset. | [
"cs.CV",
"cs.LG"
] |
We present a graph neural network model for solving graph-to-graph learning
problems. Most deep learning on graphs considers ``simple'' problems such as
graph classification or regressing real-valued graph properties. For such
tasks, the main requirement for intermediate representations of the data is to
maintain the structure needed for output, i.e., keeping classes separated or
maintaining the order indicated by the regressor. However, a number of learning
tasks, such as regressing graph-valued output, generative models, or graph
autoencoders, aim to predict a graph-structured output. In order to
successfully do this, the learned representations need to preserve far more
structure. We present a conditional auto-regressive model for graph-to-graph
learning and illustrate its representational capabilities via experiments on
challenging subgraph predictions from graph algorithmics; as a graph
autoencoder for reconstruction and visualization; and on pretraining
representations that allow graph classification with limited labeled data. | [
"cs.LG"
] |
Temporal action localization (TAL) in videos is a challenging task,
especially due to the large variation in action temporal scales. Short actions
usually occupy the major proportion in the data, but have the lowest
performance with all current methods. In this paper, we confront the challenge
of short actions and propose a multi-level cross-scale solution dubbed as video
self-stitching graph network (VSGN). We have two key components in VSGN: video
self-stitching (VSS) and cross-scale graph pyramid network (xGPN). In VSS, we
focus on a short period of a video and magnify it along the temporal dimension
to obtain a larger scale. We stitch the original clip and its magnified
counterpart in one input sequence to take advantage of the complementary
properties of both scales. The xGPN component further exploits the cross-scale
correlations by a pyramid of cross-scale graph networks, each containing a
hybrid module to aggregate features from across scales as well as within the
same scale. Our VSGN not only enhances the feature representations, but also
generates more positive anchors for short actions and more short training
samples. Experiments demonstrate that VSGN obviously improves the localization
performance of short actions as well as achieving the state-of-the-art overall
performance on THUMOS-14 and ActivityNet-v1.3. | [
"cs.CV"
] |
Person re-identification (re-ID) concerns the matching of subject images
across different camera views in a multi camera surveillance system. One of the
major challenges in person re-ID is pose variations across the camera network,
which significantly affects the appearance of a person. Existing development
data lack adequate pose variations to carry out effective training of person
re-ID systems. To solve this issue, in this paper we propose an end-to-end
pose-driven attention-guided generative adversarial network, to generate
multiple poses of a person. We propose to attentively learn and transfer the
subject pose through an attention mechanism. A semantic-consistency loss is
proposed to preserve the semantic information of the person during pose
transfer. To ensure fine image details are realistic after pose translation, an
appearance discriminator is used while a pose discriminator is used to ensure
the pose of the transferred images will exactly be the same as the target pose.
We show that by incorporating the proposed approach in a person
re-identification framework, realistic pose transferred images and
state-of-the-art re-identification results can be achieved. | [
"cs.CV",
"cs.AI",
"cs.LG"
] |
We discuss the problem of learning collaborative behaviour through
communication in multi-agent systems using deep reinforcement learning. A
connectivity-driven communication (CDC) algorithm is proposed to address three
key aspects: what agents to involve in the communication, what information
content to share, and how often to share it. The multi-agent system is modelled
as a weighted graph with nodes representing agents. The unknown edge weights
reflect the degree of communication between pairs of agents, which depends on a
diffusion process on the graph - the heat kernel. An optimal communication
strategy, tightly coupled with overall graph topology, is learned end-to-end
concurrently with the agents' policy so as to maximise future expected returns.
Empirical results show that CDC is capable of superior performance over
alternative algorithms for a range of cooperative navigation tasks, and that
the learned graph structures can be interpretable. | [
"cs.LG",
"cs.AI",
"cs.MA",
"stat.ML"
] |
Although CNNs are widely considered as the state-of-the-art models in various
applications of image analysis, one of the main challenges still open is the
training of a CNN on high resolution images. Different strategies have been
proposed involving either a rescaling of the image or an individual processing
of parts of the image. Such strategies cannot be applied to images, such as
gigapixel histopathological images, for which a high reduction in resolution
inherently effects a loss of discriminative information, and in respect of
which the analysis of single parts of the image suffers from a lack of global
information or implies a high workload in terms of annotating the training
images in such a way as to select significant parts. We propose a method for
the analysis of gigapixel histopathological images solely by using weak
image-level labels. In particular, two analysis tasks are taken into account: a
binary classification and a prediction of the tumor proliferation score. Our
method is based on a CNN structure consisting of a compressing path and a
learning path. In the compressing path, the gigapixel image is packed into a
grid-based feature map by using a residual network devoted to the feature
extraction of each patch into which the image has been divided. In the learning
path, attention modules are applied to the grid-based feature map, taking into
account spatial correlations of neighboring patch features to find regions of
interest, which are then used for the final whole slide analysis. Our method
integrates both global and local information, is flexible with regard to the
size of the input images and only requires weak image-level labels. Comparisons
with different methods of the state-of-the-art on two well known datasets,
Camelyon16 and TUPAC16, have been made to confirm the validity of the proposed
model. | [
"cs.CV"
] |
Nitrogen fertilizers have a detrimental effect on the environment, which can
be reduced by optimizing fertilizer management strategies. We implement an
OpenAI Gym environment where a reinforcement learning agent can learn
fertilization management policies using process-based crop growth models and
identify policies with reduced environmental impact. In our environment, an
agent trained with the Proximal Policy Optimization algorithm is more
successful at reducing environmental impacts than the other baseline agents we
present. | [
"cs.LG"
] |
Despite the success of Generative Adversarial Networks (GANs), their training
suffers from several well-known problems, including mode collapse and
difficulties learning a disconnected set of manifolds. In this paper, we break
down the challenging task of learning complex high dimensional distributions,
supporting diverse data samples, to simpler sub-tasks. Our solution relies on
designing a partitioner that breaks the space into smaller regions, each having
a simpler distribution, and training a different generator for each partition.
This is done in an unsupervised manner without requiring any labels.
We formulate two desired criteria for the space partitioner that aid the
training of our mixture of generators: 1) to produce connected partitions and
2) provide a proxy of distance between partitions and data samples, along with
a direction for reducing that distance. These criteria are developed to avoid
producing samples from places with non-existent data density, and also
facilitate training by providing additional direction to the generators. We
develop theoretical constraints for a space partitioner to satisfy the above
criteria. Guided by our theoretical analysis, we design an effective neural
architecture for the space partitioner that empirically assures these
conditions. Experimental results on various standard benchmarks show that the
proposed unsupervised model outperforms several recent methods. | [
"cs.LG",
"cs.CV"
] |
3D semantic scene labeling is fundamental to agents operating in the real
world. In particular, labeling raw 3D point sets from sensors provides
fine-grained semantics. Recent works leverage the capabilities of Neural
Networks (NNs), but are limited to coarse voxel predictions and do not
explicitly enforce global consistency. We present SEGCloud, an end-to-end
framework to obtain 3D point-level segmentation that combines the advantages of
NNs, trilinear interpolation(TI) and fully connected Conditional Random Fields
(FC-CRF). Coarse voxel predictions from a 3D Fully Convolutional NN are
transferred back to the raw 3D points via trilinear interpolation. Then the
FC-CRF enforces global consistency and provides fine-grained semantics on the
points. We implement the latter as a differentiable Recurrent NN to allow joint
optimization. We evaluate the framework on two indoor and two outdoor 3D
datasets (NYU V2, S3DIS, KITTI, Semantic3D.net), and show performance
comparable or superior to the state-of-the-art on all datasets. | [
"cs.CV"
] |
Although machine learning has become a powerful tool to augment doctors in
clinical analysis, the immense amount of labeled data that is necessary to
train supervised learning approaches burdens each development task as time and
resource intensive. The vast majority of dense clinical information is stored
in written reports, detailing pertinent patient information. The challenge with
utilizing natural language data for standard model development is due to the
complex nature of the modality. In this research, a model pipeline was
developed to utilize an unsupervised approach to train an encoder-language
model, a recurrent network, to generate document encodings; which then can be
used as features passed into a decoder-classifier model that requires
magnitudes less labeled data than previous approaches to differentiate between
fine-grained disease classes accurately. The language model was trained on
unlabeled radiology reports from the Massachusetts General Hospital Radiology
Department (n=218,159) and terminated with a loss of 1.62. The classification
models were trained on three labeled datasets of head CT studies of reported
patients, presenting large vessel occlusion (n=1403), acute ischemic strokes
(n=331), and intracranial hemorrhage (n=4350), to identify a variety of
different findings directly from the radiology report data; resulting in AUCs
of 0.98, 0.95, and 0.99, respectively, for the large vessel occlusion, acute
ischemic stroke, and intracranial hemorrhage datasets. The output encodings are
able to be used in conjunction with imaging data, to create models that can
process a multitude of different modalities. The ability to automatically
extract relevant features from textual data allows for faster model development
and integration of textual modality, overall, allowing clinical reports to
become a more viable input for more encompassing and accurate deep learning
models. | [
"cs.LG",
"cs.CL",
"stat.ML"
] |
It has been a primary concern in recent studies of vision and language tasks
to design an effective attention mechanism dealing with interactions between
the two modalities. The Transformer has recently been extended and applied to
several bi-modal tasks, yielding promising results. For visual dialog, it
becomes necessary to consider interactions between three or more inputs, i.e.,
an image, a question, and a dialog history, or even its individual dialog
components. In this paper, we present a neural architecture named Light-weight
Transformer for Many Inputs (LTMI) that can efficiently deal with all the
interactions between multiple such inputs in visual dialog. It has a block
structure similar to the Transformer and employs the same design of attention
computation, whereas it has only a small number of parameters, yet has
sufficient representational power for the purpose. Assuming a standard setting
of visual dialog, a layer built upon the proposed attention block has less than
one-tenth of parameters as compared with its counterpart, a natural Transformer
extension. The experimental results on the VisDial datasets validate the
effectiveness of the proposed approach, showing improvements of the best NDCG
score on the VisDial v1.0 dataset from 57.59 to 60.92 with a single model, from
64.47 to 66.53 with ensemble models, and even to 74.88 with additional
finetuning. Our implementation code is available at
https://github.com/davidnvq/visdial. | [
"cs.CV"
] |
Intelligent systems are transforming the world, as well as our healthcare
system. We propose a deep learning-based cough sound classification model that
can distinguish between children with healthy versus pathological coughs such
as asthma, upper respiratory tract infection (URTI), and lower respiratory
tract infection (LRTI). In order to train a deep neural network model, we
collected a new dataset of cough sounds, labelled with clinician's diagnosis.
The chosen model is a bidirectional long-short term memory network (BiLSTM)
based on Mel Frequency Cepstral Coefficients (MFCCs) features. The resulting
trained model when trained for classifying two classes of coughs -- healthy or
pathology (in general or belonging to a specific respiratory pathology),
reaches accuracy exceeding 84\% when classifying cough to the label provided by
the physicians' diagnosis. In order to classify subject's respiratory pathology
condition, results of multiple cough epochs per subject were combined. The
resulting prediction accuracy exceeds 91\% for all three respiratory
pathologies. However, when the model is trained to classify and discriminate
among the four classes of coughs, overall accuracy dropped: one class of
pathological coughs are often misclassified as other. However, if one consider
the healthy cough classified as healthy and pathological cough classified to
have some kind of pathologies, then the overall accuracy of four class model is
above 84\%. A longitudinal study of MFCC feature space when comparing
pathological and recovered coughs collected from the same subjects revealed the
fact that pathological cough irrespective of the underlying conditions occupy
the same feature space making it harder to differentiate only using MFCC
features. | [
"cs.LG",
"cs.MM",
"cs.SD",
"eess.AS",
"62-XX, 92-XX, 68Txx,",
"J.3; I.2"
] |
Distance-based classification is among the most competitive classification
methods for time series data. The most critical component of distance-based
classification is the selected distance function. Past research has proposed
various different distance metrics or measures dedicated to particular aspects
of real-world time series data, yet there is an important aspect that has not
been considered so far: Robustness against arbitrary data contamination. In
this work, we propose a novel distance metric that is robust against
arbitrarily "bad" contamination and has a worst-case computational complexity
of $\mathcal{O}(n\log n)$. We formally argue why our proposed metric is robust,
and demonstrate in an empirical evaluation that the metric yields competitive
classification accuracy when applied in k-Nearest Neighbor time series
classification. | [
"cs.LG",
"stat.ML"
] |
Offline reinforcement learning (RL) has increasingly become the focus of the
artificial intelligent research due to its wide real-world applications where
the collection of data may be difficult, time-consuming, or costly. In this
paper, we first propose a two-fold taxonomy for existing offline RL algorithms
from the perspective of exploration and exploitation tendency. Secondly, we
derive the explicit expression of the upper bound of extrapolation error and
explore the correlation between the performance of different types of
algorithms and the distribution of actions under states. Specifically, we relax
the strict assumption on the sufficiently large amount of state-action tuples.
Accordingly, we provably explain why batch constrained Q-learning (BCQ)
performs better than other existing techniques. Thirdly, after identifying the
weakness of BCQ on dataset of low mean episode returns, we propose a modified
variant based on top return selection mechanism, which is proved to be able to
gain state-of-the-art performance on various datasets. Lastly, we create a
benchmark platform on the Atari domain, entitled RL easy go (RLEG), at an
estimated cost of more than 0.3 million dollars. We make it open-source for
fair and comprehensive competitions between offline RL algorithms with complete
datasets and checkpoints being provided. | [
"cs.LG",
"cs.AI"
] |
This fourth and last tome is focusing on describing the envisioned works for
a project that has been presented in the preceding tome. It is about a new
approach dedicated to the coding of still and moving pictures, trying to bridge
the MPEG-4 and MPEG-7 standard bodies. The aim of this project is to define the
principles of self-descriptive video coding. In order to establish them, the
document is composed in five chapters that describe the various envisioned
techniques for developing such a new approach in visual coding: - image
segmentation, - computation of visual descriptors, - computation of perceptual
groupings, - building of visual dictionaries, - picture and video coding. Based
on the techniques of multiresolution computing, it is proposed to develop an
image segmentation made from piecewise regular components, to compute
attributes on the frame and the rendering of so produced shapes, independently
to the geometric transforms that can occur in the image plane, and to gather
them into perceptual groupings so as to be able in performing recognition of
partially hidden patterns. Due to vector quantization of shapes frame and
rendering, it will appear that simple shapes may be compared to a visual
alphabet and that complex shapes then become words written using this alphabet
and be recorded into a dictionary. With the help of a nearest neighbour
scanning applied on the picture shapes, the self-descriptive coding will then
generate a sentence made from words written using the simple shape alphabet. | [
"cs.CV",
"E.1; I.4; I.5; I.6"
] |
Balanced representation learning methods have been applied successfully to
counterfactual inference from observational data. However, approaches that
account for survival outcomes are relatively limited. Survival data are
frequently encountered across diverse medical applications, i.e., drug
development, risk profiling, and clinical trials, and such data are also
relevant in fields like manufacturing (e.g., for equipment monitoring). When
the outcome of interest is a time-to-event, special precautions for handling
censored events need to be taken, as ignoring censored outcomes may lead to
biased estimates. We propose a theoretically grounded unified framework for
counterfactual inference applicable to survival outcomes. Further, we formulate
a nonparametric hazard ratio metric for evaluating average and individualized
treatment effects. Experimental results on real-world and semi-synthetic
datasets, the latter of which we introduce, demonstrate that the proposed
approach significantly outperforms competitive alternatives in both
survival-outcome prediction and treatment-effect estimation. | [
"stat.ML",
"cs.LG"
] |
We present DeepPerimeter, a deep learning based pipeline for inferring a full
indoor perimeter (i.e. exterior boundary map) from a sequence of posed RGB
images. Our method relies on robust deep methods for depth estimation and wall
segmentation to generate an exterior boundary point cloud, and then uses deep
unsupervised clustering to fit wall planes to obtain a final boundary map of
the room. We demonstrate that DeepPerimeter results in excellent visual and
quantitative performance on the popular ScanNet and FloorNet datasets and works
for room shapes of various complexities as well as in multiroom scenarios. We
also establish important baselines for future work on indoor perimeter
estimation, topics which will become increasingly prevalent as application
areas like augmented reality and robotics become more significant. | [
"cs.CV"
] |
Large Scale image classification is a challenging problem within the field of
computer vision. As the real world contains billions of different objects,
understanding the performance of popular techniques and models is vital in
order to apply them to real world tasks. In this paper, we evaluate techniques
and popular CNN based deep learning architectures to perform large scale
species classification on the dataset from iNaturalist 2019 Challenge. Methods
utilizing dataset pruning and transfer learning are shown to outperform models
trained without either of the two techniques. The ResNext based classifier
outperforms other model architectures over 10 epochs and achieves a top-one
validation error of 0.68 when classifying amongst the 1,010 species. | [
"cs.CV"
] |
Self-supervised representation learning targets to learn convnet-based image
representations from unlabeled data. Inspired by the success of NLP methods in
this area, in this work we propose a self-supervised approach based on
spatially dense image descriptions that encode discrete visual concepts, here
called visual words. To build such discrete representations, we quantize the
feature maps of a first pre-trained self-supervised convnet, over a k-means
based vocabulary. Then, as a self-supervised task, we train another convnet to
predict the histogram of visual words of an image (i.e., its Bag-of-Words
representation) given as input a perturbed version of that image. The proposed
task forces the convnet to learn perturbation-invariant and context-aware image
features, useful for downstream image understanding tasks. We extensively
evaluate our method and demonstrate very strong empirical results, e.g., our
pre-trained self-supervised representations transfer better on detection task
and similarly on classification over classes "unseen" during pre-training, when
compared to the supervised case.
This also shows that the process of image discretization into visual words
can provide the basis for very powerful self-supervised approaches in the image
domain, thus allowing further connections to be made to related methods from
the NLP domain that have been extremely successful so far. | [
"cs.CV",
"cs.LG"
] |
Modern deep learning algorithms have triggered various image segmentation
approaches. However most of them deal with pixel based segmentation. However,
superpixels provide a certain degree of contextual information while reducing
computation cost. In our approach, we have performed superpixel level semantic
segmentation considering 3 various levels as neighbours for semantic contexts.
Furthermore, we have enlisted a number of ensemble approaches like max-voting
and weighted-average. We have also used the Dempster-Shafer theory of
uncertainty to analyze confusion among various classes. Our method has proved
to be superior to a number of different modern approaches on the same dataset. | [
"cs.CV"
] |
For the initial shoulder preoperative diagnosis, it is essential to obtain a
three-dimensional (3D) bone mask from medical images, e.g., magnetic resonance
(MR). However, obtaining high-resolution and dense medical scans is both costly
and time-consuming. In addition, the imaging parameters for each 3D scan may
vary from time to time and thus increase the variance between images.
Therefore, it is practical to consider the bone extraction on low-resolution
data which may influence imaging contrast and make the segmentation work
difficult. In this paper, we present a joint segmentation for the humerus and
scapula bones on a small dataset with low-contrast and high-shape-variability
3D MR images. The proposed network has a deep end-to-end architecture to obtain
the initial 3D bone masks. Because the existing scarce and inaccurate
human-labeled ground truth, we design a self-reinforced learning strategy to
increase performance. By comparing with the non-reinforced segmentation and a
classical multi-atlas method with joint label fusion, the proposed approach
obtains better results. | [
"cs.CV",
"cs.LG"
] |
Physics-informed neural networks (PINNs) have become a popular choice for
solving high-dimensional partial differential equations (PDEs) due to their
excellent approximation power and generalization ability. Recently, Extended
PINNs (XPINNs) based on domain decomposition methods have attracted
considerable attention due to their effectiveness in modeling multiscale and
multiphysics problems and their parallelization. However, theoretical
understanding on their convergence and generalization properties remains
unexplored. In this study, we take an initial step towards understanding how
and when XPINNs outperform PINNs. Specifically, for general multi-layer PINNs
and XPINNs, we first provide a prior generalization bound via the complexity of
the target functions in the PDE problem, and a posterior generalization bound
via the posterior matrix norms of the networks after optimization. Moreover,
based on our bounds, we analyze the conditions under which XPINNs improve
generalization. Concretely, our theory shows that the key building block of
XPINN, namely the domain decomposition, introduces a tradeoff for
generalization. On the one hand, XPINNs decompose the complex PDE solution into
several simple parts, which decreases the complexity needed to learn each part
and boosts generalization. On the other hand, decomposition leads to less
training data being available in each subdomain, and hence such model is
typically prone to overfitting and may become less generalizable. Empirically,
we choose five PDEs to show when XPINNs perform better than, similar to, or
worse than PINNs, hence demonstrating and justifying our new theory. | [
"cs.LG",
"cs.NA",
"math.DS",
"math.NA",
"stat.ML"
] |
We propose a novel grayness index for finding gray pixels and demonstrate its
effectiveness and efficiency in illumination estimation. The grayness index, GI
in short, is derived using the Dichromatic Reflection Model and is
learning-free. GI allows to estimate one or multiple illumination sources in
color-biased images. On standard single-illumination and multiple-illumination
estimation benchmarks, GI outperforms state-of-the-art statistical methods and
many recent deep methods. GI is simple and fast, written in a few dozen lines
of code, processing a 1080p image in ~0.4 seconds with a non-optimized Matlab
code. | [
"cs.CV"
] |
With graphs rapidly growing in size and deeper graph neural networks (GNNs)
emerging, the training and inference of GNNs become increasingly expensive.
Existing network weight pruning algorithms cannot address the main space and
computational bottleneck in GNNs, caused by the size and connectivity of the
graph. To this end, this paper first presents a unified GNN sparsification
(UGS) framework that simultaneously prunes the graph adjacency matrix and the
model weights, for effectively accelerating GNN inference on large-scale
graphs. Leveraging this new tool, we further generalize the recently popular
lottery ticket hypothesis to GNNs for the first time, by defining a graph
lottery ticket (GLT) as a pair of core sub-dataset and sparse sub-network,
which can be jointly identified from the original GNN and the full dense graph
by iteratively applying UGS. Like its counterpart in convolutional neural
networks, GLT can be trained in isolation to match the performance of training
with the full model and graph, and can be drawn from both randomly initialized
and self-supervised pre-trained GNNs. Our proposal has been experimentally
verified across various GNN architectures and diverse tasks, on both
small-scale graph datasets (Cora, Citeseer and PubMed), and large-scale
datasets from the challenging Open Graph Benchmark (OGB). Specifically, for
node classification, our found GLTs achieve the same accuracies with 20%~98%
MACs saving on small graphs and 25%~85% MACs saving on large ones. For link
prediction, GLTs lead to 48%~97% and 70% MACs saving on small and large graph
datasets, respectively, without compromising predictive performance. Codes
available at https://github.com/VITA-Group/Unified-LTH-GNN. | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
Given high-dimensional time series data (e.g., sensor data), how can we
detect anomalous events, such as system faults and attacks? More challengingly,
how can we do this in a way that captures complex inter-sensor relationships,
and detects and explains anomalies which deviate from these relationships?
Recently, deep learning approaches have enabled improvements in anomaly
detection in high-dimensional datasets; however, existing methods do not
explicitly learn the structure of existing relationships between variables, or
use them to predict the expected behavior of time series. Our approach combines
a structure learning approach with graph neural networks, additionally using
attention weights to provide explainability for the detected anomalies.
Experiments on two real-world sensor datasets with ground truth anomalies show
that our method detects anomalies more accurately than baseline approaches,
accurately captures correlations between sensors, and allows users to deduce
the root cause of a detected anomaly. | [
"cs.LG",
"cs.AI"
] |
Understanding the predictions made by machine learning (ML) models and their
potential biases remains a challenging and labor-intensive task that depends on
the application, the dataset, and the specific model. We present Amazon
SageMaker Clarify, an explainability feature for Amazon SageMaker that launched
in December 2020, providing insights into data and ML models by identifying
biases and explaining predictions. It is deeply integrated into Amazon
SageMaker, a fully managed service that enables data scientists and developers
to build, train, and deploy ML models at any scale. Clarify supports bias
detection and feature importance computation across the ML lifecycle, during
data preparation, model evaluation, and post-deployment monitoring. We outline
the desiderata derived from customer input, the modular architecture, and the
methodology for bias and explanation computations. Further, we describe the
technical challenges encountered and the tradeoffs we had to make. For
illustration, we discuss two customer use cases. We present our deployment
results including qualitative customer feedback and a quantitative evaluation.
Finally, we summarize lessons learned, and discuss best practices for the
successful adoption of fairness and explanation tools in practice. | [
"cs.LG"
] |
We propose a state of the art method for intelligent object recognition and
video surveillance based on human visual attention. Bottom up and top down
attention are applied respectively in the process of acquiring interested
object(saliency map) and object recognition. The revision of 4 channel PFT
method is proposed for bottom up attention and enhances the speed and accuracy.
Inhibit of return (IOR) is applied in judging the sequence of saliency object
pop out. Euclidean distance of color distribution, object center coordinates
and speed are considered in judging whether the target is match and suspicious.
The extensive tests on videos and images show that our method in video analysis
has high accuracy and fast speed compared with traditional method. The method
can be applied into many fields such as video surveillance and security. | [
"cs.CV"
] |
The underlying structure of natural language is hierarchical; words combine
into phrases, which in turn form clauses. An awareness of this hierarchical
structure can aid machine learning models in performing many linguistic tasks.
However, most such models just process text sequentially and there is no bias
towards learning hierarchical structure encoded into their architecture. In
this paper, we extend the recent transformer model (Vaswani et al., 2017) by
enabling it to learn hierarchical representations. To achieve this, we adapt
the ordering mechanism introduced in Shen et al., 2018, to the self-attention
module of the transformer architecture. We train our new model on language
modelling and then apply it to the task of unsupervised parsing. We achieve
reasonable results on the freely available subset of the WSJ10 dataset with an
F1-score of about 50%. | [
"cs.LG",
"cs.CL"
] |
Action detection plays an important role in high-level video understanding
and media interpretation. Many existing studies fulfill this spatio-temporal
localization by modeling the context, capturing the relationship of actors,
objects, and scenes conveyed in the video. However, they often universally
treat all the actors without considering the consistency and distinctness
between individuals, leaving much room for improvement. In this paper, we
explicitly highlight the identity information of the actors in terms of both
long-term and short-term context through a graph memory network, namely
identity-aware graph memory network (IGMN). Specifically, we propose the
hierarchical graph neural network (HGNN) to comprehensively conduct long-term
relation modeling within the same identity as well as between different ones.
Regarding short-term context, we develop a dual attention module (DAM) to
generate identity-aware constraint to reduce the influence of interference by
the actors of different identities. Extensive experiments on the challenging
AVA dataset demonstrate the effectiveness of our method, which achieves
state-of-the-art results on AVA v2.1 and v2.2. | [
"cs.CV"
] |
As online shopping prevails and e-commerce platforms emerge, there is a
tremendous number of parcels being transported every day. Thus, it is crucial
for the logistics industry on how to assign a candidate logistics route for
each shipping parcel properly as it leaves a significant impact on the total
logistics cost optimization and business constraints satisfaction such as
transit hub capacity and delivery proportion of delivery providers. This online
route-assignment problem can be viewed as a constrained online decision-making
problem. Notably, the large amount (beyond ${10^5}$) of daily parcels, the
variability and non-Markovian characteristics of parcel information impose
difficulties on attaining (near-) optimal solution without violating
constraints excessively. In this paper, we develop a model-free DRL approach
named PPO-RA, in which Proximal Policy Optimization (PPO) is improved with
dedicated techniques to address the challenges for route assignment (RA). The
actor and critic networks use attention mechanism and parameter sharing to
accommodate each incoming parcel with varying numbers and identities of
candidate routes, without modeling non-Markovian parcel arriving dynamics since
we make assumption of i.i.d. parcel arrival. We use recorded delivery parcel
data to evaluate the performance of PPO-RA by comparing it with widely-used
baselines via simulation. The results show the capability of the proposed
approach to achieve considerable cost savings while satisfying most
constraints. | [
"cs.LG",
"cs.AI"
] |
Deep Learning is considered to be a quite young in the area of machine
learning research, found its effectiveness in dealing complex yet high
dimensional dataset that includes but limited to images, text and speech etc.
with multiple levels of representation and abstraction. As there are a plethora
of research on these datasets by various researchers , a win over them needs
lots of attention. Careful setting of Deep learning parameters is of paramount
importance in order to avoid the overfitting unlike conventional methods with
limited parameter settings. Deep Convolutional neural network (DCNN) with
multiple layers of compositions and appropriate settings might be is an
efficient machine learning method that can outperform the conventional methods
in a great way. However, due to its slow adoption in learning, there are also
always a chance of overfitting during feature selection process, which can be
addressed by employing a regularization method called dropout. Fast Random
Forest (FRF) is a powerful ensemble classifier especially when the datasets are
noisy and when the number of attributes is large in comparison to the number of
instances, as is the case of Bioinformatics datasets. Several publicly
available Bioinformatics dataset, Handwritten digits recognition and Image
segmentation dataset are considered for evaluation of the proposed approach.
The excellent performance obtained by the proposed DCNN based feature selection
with FRF classifier on high dimensional datasets makes it a fast and accurate
classifier in comparison the state-of-the-art. | [
"cs.CV"
] |
Electricity theft is a major problem around the world in both developed and
developing countries and may range up to 40% of the total electricity
distributed. More generally, electricity theft belongs to non-technical losses
(NTL), which are losses that occur during the distribution of electricity in
power grids. In this paper, we build features from the neighborhood of
customers. We first split the area in which the customers are located into
grids of different sizes. For each grid cell we then compute the proportion of
inspected customers and the proportion of NTL found among the inspected
customers. We then analyze the distributions of features generated and show why
they are useful to predict NTL. In addition, we compute features from the
consumption time series of customers. We also use master data features of
customers, such as their customer class and voltage of their connection. We
compute these features for a Big Data base of 31M meter readings, 700K
customers and 400K inspection results. We then use these features to train four
machine learning algorithms that are particularly suitable for Big Data sets
because of their parallelizable structure: logistic regression, k-nearest
neighbors, linear support vector machine and random forest. Using the
neighborhood features instead of only analyzing the time series has resulted in
appreciable results for Big Data sets for varying NTL proportions of 1%-90%.
This work can therefore be deployed to a wide range of different regions around
the world. | [
"cs.LG",
"cs.AI"
] |
A complex combination of simultaneous supervised-unsupervised learning is
believed to be the key to humans performing tasks seamlessly across multiple
domains or tasks. This phenomenon of cross-domain learning has been very well
studied in domain adaptation literature. Recent domain adaptation works rely on
an indirect way of first aligning the source and target domain distributions
and then train a classifier on the labeled source domain to classify the target
domain. However, this approach has the main drawback that obtaining a
near-perfect alignment of the domains in itself might be difficult/impossible
(e.g., language domains). To address this, we follow Vapnik's imperative of
statistical learning that states any desired problem should be solved in the
most direct way rather than solving a more general intermediate task and
propose a direct approach to domain adaptation that does not require domain
alignment. We propose a model referred Contradistinguisher that learns
contrastive features and whose objective is to jointly learn to
contradistinguish the unlabeled target domain in an unsupervised way and
classify in a supervised way on the source domain. We achieve the
state-of-the-art on Office-31 and VisDA-2017 datasets in both single-source and
multi-source settings. We also notice that the contradistinguish loss improves
the model performance by increasing the shape bias. | [
"cs.LG",
"stat.ML"
] |
This work proposes the use of Bayesian approximations of uncertainty from
deep learning in a robot planner, showing that this produces more cautious
actions in safety-critical scenarios. The case study investigated is motivated
by a setup where an aerial robot acts as a "scout" for a ground robot. This is
useful when the below area is unknown or dangerous, with applications in space
exploration, military, or search-and-rescue. Images taken from the aerial view
are used to provide a less obstructed map to guide the navigation of the robot
on the ground. Experiments are conducted using a deep learning semantic image
segmentation, followed by a path planner based on the resulting cost map, to
provide an empirical analysis of the proposed method. A comparison with similar
approaches is presented to portray the usefulness of certain techniques, or
variations within a technique, in similar experimental settings. The method is
analyzed to assess the impact of variations in the uncertainty extraction, as
well as the absence of an uncertainty metric, on the overall system with the
use of a defined metric which measures surprise to the planner. The analysis is
performed on multiple datasets, showing a similar trend of lower surprise when
uncertainty information is incorporated in the planning, given threshold values
of the hyperparameters in the uncertainty extraction have been met. We find
that taking uncertainty into account leads to paths that could be 18% less
risky on an average. | [
"cs.LG",
"cs.AI",
"cs.CV",
"cs.SY",
"eess.SY",
"stat.ML"
] |
Human beings are fundamentally sociable -- that we generally organize our
social lives in terms of relations with other people. Understanding social
relations from an image has great potential for intelligent systems such as
social chatbots and personal assistants. In this paper, we propose a simpler,
faster, and more accurate method named graph relational reasoning network
(GR2N) for social relation recognition. Different from existing methods which
process all social relations on an image independently, our method considers
the paradigm of jointly inferring the relations by constructing a social
relation graph. Furthermore, the proposed GR2N constructs several virtual
relation graphs to explicitly grasp the strong logical constraints among
different types of social relations. Experimental results illustrate that our
method generates a reasonable and consistent social relation graph and improves
the performance in both accuracy and efficiency. | [
"cs.CV"
] |
Many automated operations in agriculture, such as weeding and plant counting,
require robust and accurate object detectors. Robotic fruit harvesting is one
of these, and is an important technology to address the increasing labour
shortages and uncertainty suffered by tree crop growers. An eye-in-hand sensing
setup is commonly used in harvesting systems and provides benefits to sensing
accuracy and flexibility. However, as the hand and camera move from viewing the
entire trellis to picking a specific fruit, large changes in lighting, colour,
obscuration and exposure occur. Object detection algorithms used in harvesting
should be robust to these challenges, but few datasets for assessing this
currently exist. In this work, two new datasets are gathered during day and
night operation of an actual robotic plum harvesting system. A range of current
generation deep learning object detectors are benchmarked against these.
Additionally, two methods for fusing depth and image information are tested for
their impact on detector performance. Significant differences between day and
night accuracy of different detectors is found, transfer learning is identified
as essential in all cases, and depth information fusion is assessed as only
marginally effective. The dataset and benchmark models are made available
online. | [
"cs.CV",
"cs.RO"
] |
In this paper, we propose a generic model transfer scheme to make
Convlutional Neural Networks (CNNs) interpretable, while maintaining their high
classification accuracy. We achieve this by building a differentiable decision
forest on top of CNNs, which enjoys two characteristics: 1) During training,
the tree hierarchies of the forest are learned in a top-down manner under the
guidance from the category semantics embedded in the pre-trained CNN weights;
2) During inference, a single decision tree is dynamically selected from the
forest for each input sample, enabling the transferred model to make sequential
decisions corresponding to the attributes shared by semantically-similar
categories, rather than directly performing flat classification. We name the
transferred model deep Dynamic Sequential Decision Forest (dDSDF). Experimental
results show that dDSDF not only achieves higher classification accuracy than
its conuterpart, i.e., the original CNN, but has much better interpretability,
as qualitatively it has plausible hierarchies and quantitatively it leads to
more precise saliency maps. | [
"cs.CV"
] |
Constraint-based learning reduces the burden of collecting labels by having
users specify general properties of structured outputs, such as constraints
imposed by physical laws. We propose a novel framework for simultaneously
learning these constraints and using them for supervision, bypassing the
difficulty of using domain expertise to manually specify constraints. Learning
requires a black-box simulator of structured outputs, which generates valid
labels, but need not model their corresponding inputs or the input-label
relationship. At training time, we constrain the model to produce outputs that
cannot be distinguished from simulated labels by adversarial training.
Providing our framework with a small number of labeled inputs gives rise to a
new semi-supervised structured prediction model; we evaluate this model on
multiple tasks --- tracking, pose estimation and time series prediction --- and
find that it achieves high accuracy with only a small number of labeled inputs.
In some cases, no labels are required at all. | [
"cs.LG",
"cs.CV",
"stat.ML"
] |
Although having achieved great success in medical image segmentation, deep
convolutional neural networks usually require a large dataset with manual
annotations for training and are difficult to generalize to unseen classes.
Few-shot learning has the potential to address these challenges by learning new
classes from only a few labeled examples. In this work, we propose a new
framework for few-shot medical image segmentation based on prototypical
networks. Our innovation lies in the design of two key modules: 1) a context
relation encoder (CRE) that uses correlation to capture local relation features
between foreground and background regions; and 2) a recurrent mask refinement
module that repeatedly uses the CRE and a prototypical network to recapture the
change of context relationship and refine the segmentation mask iteratively.
Experiments on two abdomen CT datasets and an abdomen MRI dataset show the
proposed method obtains substantial improvement over the state-of-the-art
methods by an average of 16.32%, 8.45% and 6.24% in terms of DSC, respectively.
Code is publicly available. | [
"cs.CV"
] |
Learning useful representations without supervision remains a key challenge
in machine learning. In this paper, we propose a simple yet powerful generative
model that learns such discrete representations. Our model, the Vector
Quantised-Variational AutoEncoder (VQ-VAE), differs from VAEs in two key ways:
the encoder network outputs discrete, rather than continuous, codes; and the
prior is learnt rather than static. In order to learn a discrete latent
representation, we incorporate ideas from vector quantisation (VQ). Using the
VQ method allows the model to circumvent issues of "posterior collapse" --
where the latents are ignored when they are paired with a powerful
autoregressive decoder -- typically observed in the VAE framework. Pairing
these representations with an autoregressive prior, the model can generate high
quality images, videos, and speech as well as doing high quality speaker
conversion and unsupervised learning of phonemes, providing further evidence of
the utility of the learnt representations. | [
"cs.LG"
] |
Deep learning models are sensitive to domain shift phenomena. A model trained
on images from one domain cannot generalise well when tested on images from a
different domain, despite capturing similar anatomical structures. It is mainly
because the data distribution between the two domains is different. Moreover,
creating annotation for every new modality is a tedious and time-consuming
task, which also suffers from high inter- and intra- observer variability.
Unsupervised domain adaptation (UDA) methods intend to reduce the gap between
source and target domains by leveraging source domain labelled data to generate
labels for the target domain. However, current state-of-the-art (SOTA) UDA
methods demonstrate degraded performance when there is insufficient data in
source and target domains. In this paper, we present a novel UDA method for
multi-modal cardiac image segmentation. The proposed method is based on
adversarial learning and adapts network features between source and target
domain in different spaces. The paper introduces an end-to-end framework that
integrates: a) entropy minimisation, b) output feature space alignment and c) a
novel point-cloud shape adaptation based on the latent features learned by the
segmentation model. We validated our method on two cardiac datasets by adapting
from the annotated source domain, bSSFP-MRI (balanced Steady-State Free
Procession-MRI), to the unannotated target domain, LGE-MRI (Late-gadolinium
enhance-MRI), for the multi-sequence dataset; and from MRI (source) to CT
(target) for the cross-modality dataset. The results highlighted that by
enforcing adversarial learning in different parts of the network, the proposed
method delivered promising performance, compared to other SOTA methods. | [
"cs.CV"
] |
Dashboard cameras capture a tremendous amount of driving scene video each
day. These videos are purposefully coupled with vehicle sensing data, such as
from the speedometer and inertial sensors, providing an additional sensing
modality for free. In this work, we leverage the large-scale unlabeled yet
naturally paired data for visual representation learning in the driving
scenario. A representation is learned in an end-to-end self-supervised
framework for predicting dense optical flow from a single frame with paired
sensing data. We postulate that success on this task requires the network to
learn semantic and geometric knowledge in the ego-centric view. For example,
forecasting a future view to be seen from a moving vehicle requires an
understanding of scene depth, scale, and movement of objects. We demonstrate
that our learned representation can benefit other tasks that require detailed
scene understanding and outperforms competing unsupervised representations on
semantic segmentation. | [
"cs.CV"
] |
Surgical tool segmentation in endoscopic images is an important problem: it
is a crucial step towards full instrument pose estimation and it is used for
integration of pre- and intra-operative images into the endoscopic view. While
many recent approaches based on convolutional neural networks have shown great
results, a key barrier to progress lies in the acquisition of a large number of
manually-annotated images which is necessary for an algorithm to generalize and
work well in diverse surgical scenarios. Unlike the surgical image data itself,
annotations are difficult to acquire and may be of variable quality. On the
other hand, synthetic annotations can be automatically generated by using
forward kinematic model of the robot and CAD models of tools by projecting them
onto an image plane. Unfortunately, this model is very inaccurate and cannot be
used for supervised learning of image segmentation models. Since generated
annotations will not directly correspond to endoscopic images due to errors, we
formulate the problem as an unpaired image-to-image translation where the goal
is to learn the mapping between an input endoscopic image and a corresponding
annotation using an adversarial model. Our approach allows to train image
segmentation models without the need to acquire expensive annotations and can
potentially exploit large unlabeled endoscopic image collection outside the
annotated distributions of image/annotation data. We test our proposed method
on Endovis 2017 challenge dataset and show that it is competitive with
supervised segmentation methods. | [
"cs.CV"
] |
This paper studies unsupervised monocular depth prediction problem. Most of
existing unsupervised depth prediction algorithms are developed for outdoor
scenarios, while the depth prediction work in the indoor environment is still
very scarce to our knowledge. Therefore, this work focuses on narrowing the gap
by firstly evaluating existing approaches in the indoor environments and then
improving the state-of-the-art design of architecture. Unlike typical outdoor
training dataset, such as KITTI with motion constraints, data for indoor
environment contains more arbitrary camera movement and short baseline between
two consecutive images, which deteriorates the network training for the pose
estimation. To address this issue, we propose two methods: Firstly, we propose
a novel reconstruction loss function to constraint pose estimation, resulting
in accuracy improvement of the predicted disparity map; secondly, we use an
ensemble learning with a flipping strategy along with a median filter, directly
taking operation on the output disparity map. We evaluate our approaches on the
TUM RGB-D and self-collected datasets. The results have shown that both
approaches outperform the previous state-of-the-art unsupervised learning
approaches. | [
"cs.CV"
] |
Stability is an important property of graph neural networks (GNNs) which
explains their success in many problems of practical interest. Existing GNN
stability results depend on the size of the graph, restricting applicability to
graphs of moderate size. To understand the stability properties of GNNs on
large graphs, we consider neural networks supported on manifolds. These are
defined in terms of manifold diffusions mediated by the Laplace-Beltrami (LB)
operator and are interpreted as limits of GNNs running on graphs of growing
size. We define manifold deformations and show that they lead to perturbations
of the manifold's LB operator that consist of an absolute and a relative
perturbation term. We then define filters that split the infinite dimensional
spectrum of the LB operator in finite partitions, and prove that manifold
neural networks (MNNs) with these filters are stable to both, absolute and
relative perturbations of the LB operator. Stability results are illustrated
numerically in resource allocation problems in wireless networks. | [
"cs.LG"
] |
Deep Reinforcement Learning (RL) recently emerged as one of the most
competitive approaches for learning in sequential decision making problems with
fully observable environments, e.g., computer Go. However, very little work has
been done in deep RL to handle partially observable environments. We propose a
new architecture called Action-specific Deep Recurrent Q-Network (ADRQN) to
enhance learning performance in partially observable domains. Actions are
encoded by a fully connected layer and coupled with a convolutional observation
to form an action-observation pair. The time series of action-observation pairs
are then integrated by an LSTM layer that learns latent states based on which a
fully connected layer computes Q-values as in conventional Deep Q-Networks
(DQNs). We demonstrate the effectiveness of our new architecture in several
partially observable domains, including flickering Atari games. | [
"cs.LG",
"stat.ML"
] |
We consider the problem of image representation for the tasks of unsupervised
learning and semi-supervised learning. In those learning tasks, the raw image
vectors may not provide enough representation for their intrinsic structures
due to their highly dense feature space. To overcome this problem, the raw
image vectors should be mapped to a proper representation space which can
capture the latent structure of the original data and represent the data
explicitly for further learning tasks such as clustering.
Inspired by the recent research works on deep neural network and
representation learning, in this paper, we introduce the multiple-layer
auto-encoder into image representation, we also apply the locally invariant
ideal to our image representation with auto-encoders and propose a novel
method, called Graph regularized Auto-Encoder (GAE). GAE can provide a compact
representation which uncovers the hidden semantics and simultaneously respects
the intrinsic geometric structure.
Extensive experiments on image clustering show encouraging results of the
proposed algorithm in comparison to the state-of-the-art algorithms on
real-word cases. | [
"cs.LG",
"K.3.2"
] |
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