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Generative adversarial networks (GANs) have achieved great success in image
translation and manipulation. However, high-fidelity image generation with
faithful style control remains a grand challenge in computer vision. This paper
presents a versatile image translation and manipulation framework that achieves
accurate ... | [
"cs.CV"
] |
Anomaly detection is being regarded as an unsupervised learning task as
anomalies stem from adversarial or unlikely events with unknown distributions.
However, the predictive performance of purely unsupervised anomaly detection
often fails to match the required detection rates in many tasks and there
exists a need for ... | [
"cs.LG"
] |
The data drawn from biological, economic, and social systems are often
confounded due to the presence of unmeasured variables. Prior work in causal
discovery has focused on discrete search procedures for selecting acyclic
directed mixed graphs (ADMGs), specifically ancestral ADMGs, that encode
ordinary conditional inde... | [
"cs.LG",
"stat.ML",
"G.3; J.3; F.2.2"
] |
This paper concerns dictionary learning, i.e., sparse coding, a fundamental
representation learning problem. We show that a subgradient descent algorithm,
with random initialization, can provably recover orthogonal dictionaries on a
natural nonsmooth, nonconvex $\ell_1$ minimization formulation of the problem,
under mi... | [
"cs.LG",
"cs.IT",
"math.IT",
"math.OC",
"stat.ML"
] |
Biometric authentication involves various technologies to identify
individuals by exploiting their unique, measurable physiological and behavioral
characteristics. However, traditional biometric authentication systems (e.g.,
face recognition, iris, retina, voice, and fingerprint) are facing an
increasing risk of being ... | [
"cs.LG"
] |
The Exploration-Exploitation tradeoff arises in Reinforcement Learning when
one cannot tell if a policy is optimal. Then, there is a constant need to
explore new actions instead of exploiting past experience. In practice, it is
common to resolve the tradeoff by using a fixed exploration mechanism, such as
$\epsilon$-gr... | [
"cs.LG",
"stat.ML"
] |
State-of-the-art temporal action detectors to date are based on two-stream
input including RGB frames and optical flow. Although combining RGB frames and
optical flow boosts performance significantly, optical flow is a hand-designed
representation which not only requires heavy computation, but also makes it
methodologi... | [
"cs.CV",
"cs.AI"
] |
Event forecasting is a challenging, yet important task, as humans seek to
constantly plan for the future. Existing automated forecasting studies rely
mostly on structured data, such as time-series or event-based knowledge graphs,
to help predict future events. In this work, we aim to formulate a task,
construct a datas... | [
"cs.LG",
"stat.ML"
] |
In this work we propose a new computational framework, based on generative
deep models, for synthesis of photo-realistic food meal images from textual
descriptions of its ingredients. Previous works on synthesis of images from
text typically rely on pre-trained text models to extract text features,
followed by a genera... | [
"cs.CV",
"cs.GR",
"cs.LG",
"stat.ML"
] |
Conventional image retrieval techniques for Structure-from-Motion (SfM)
suffer from the limit of effectively recognizing repetitive patterns and cannot
guarantee to create just enough match pairs with high precision and high
recall. In this paper, we present a novel retrieval method based on Graph
Convolutional Network... | [
"cs.CV"
] |
Despite the success of Generative Adversarial Networks (GANs), mode collapse
remains a serious issue during GAN training. To date, little work has focused
on understanding and quantifying which modes have been dropped by a model. In
this work, we visualize mode collapse at both the distribution level and the
instance l... | [
"cs.CV",
"cs.GR",
"cs.LG",
"eess.IV"
] |
Recurrent neural networks (RNNs) for reinforcement learning (RL) have shown
distinct advantages, e.g., solving memory-dependent tasks and meta-learning.
However, little effort has been spent on improving RNN architectures and on
understanding the underlying neural mechanisms for performance gain. In this
paper, we prop... | [
"cs.LG",
"stat.ML"
] |
Many problems at the intersection of combinatorics and computer science
require solving for a permutation that optimally matches, ranks, or sorts some
data. These problems usually have a task-specific, often non-differentiable
objective function that data-driven algorithms can use as a learning signal. In
this paper, w... | [
"cs.LG",
"stat.ML"
] |
One obstacle that so far prevents the introduction of machine learning models
primarily in critical areas is the lack of explainability. In this work, a
practicable approach of gaining explainability of deep artificial neural
networks (NN) using an interpretable surrogate model based on decision trees is
presented. Sim... | [
"cs.LG",
"stat.ML"
] |
Hypergraphs are a generalized data structure of graphs to model higher-order
correlations among entities, which have been successfully adopted into various
research domains. Meanwhile, HyperGraph Neural Network (HGNN) is currently the
de-facto method for hypergraph representation learning. However, HGNN aims at
single ... | [
"cs.CV"
] |
Deep learning has given way to a new era of machine learning, apart from
computer vision. Convolutional neural networks have been implemented in image
classification, segmentation and object detection. Despite recent advancements,
we are still in the very early stages and have yet to settle on best practices
for networ... | [
"cs.CV"
] |
Image segmentation is the process of partitioning a image into different
regions or groups based on some characteristics like color, texture, motion or
shape etc. Active contours is a popular variational method for object
segmentation in images, in which the user initializes a contour which evolves
in order to optimize... | [
"cs.CV"
] |
Multi-kernel learning (MKL) has been widely used in function approximation
tasks. The key problem of MKL is to combine kernels in a prescribed dictionary.
Inclusion of irrelevant kernels in the dictionary can deteriorate accuracy of
MKL, and increase the computational complexity. To improve the accuracy of
function app... | [
"cs.LG"
] |
Recent saliency models extensively explore to incorporate multi-scale
contextual information from Convolutional Neural Networks (CNNs). Besides
direct fusion strategies, many approaches introduce message-passing to enhance
CNN features or predictions. However, the messages are mainly transmitted in
two ways, by feature... | [
"cs.CV"
] |
Almost all of the current top-performing object detection networks employ
region proposals to guide the search for object instances. State-of-the-art
region proposal methods usually need several thousand proposals to get high
recall, thus hurting the detection efficiency. Although the latest Region
Proposal Network met... | [
"cs.CV"
] |
Sensitive inferences and user re-identification are major threats to privacy
when raw sensor data from wearable or portable devices are shared with
cloud-assisted applications. To mitigate these threats, we propose mechanisms
to transform sensor data before sharing them with applications running on
users' devices. Thes... | [
"cs.LG",
"cs.HC",
"eess.SP",
"stat.ML"
] |
Point cloud semantic segmentation often requires largescale annotated
training data, but clearly, point-wise labels are too tedious to prepare. While
some recent methods propose to train a 3D network with small percentages of
point labels, we take the approach to an extreme and propose "One Thing One
Click," meaning th... | [
"cs.CV"
] |
Designing deep networks robust to adversarial examples remains an open
problem. Likewise, recent zeroth order hard-label attacks on image
classification models have shown comparable performance to their first-order,
gradient-level alternatives. It was recently shown in the gradient-level
setting that regular adversaria... | [
"cs.LG"
] |
Bayesian interpretations of neural network have a long history, dating back
to early work in the 1990's and have recently regained attention because of
their desirable properties like uncertainty estimation, model robustness and
regularisation. We want to discuss here the application of Bayesian models to
knowledge sha... | [
"stat.ML",
"cs.LG"
] |
This paper explores the use of the Learning Automata (LA) algorithm to
compute threshold selection for image segmentation as it is a critical
preprocessing step for image analysis, pattern recognition and computer vision.
LA is a heuristic method which is able to solve complex optimization problems
with interesting res... | [
"cs.CV"
] |
Considering the inherent stochasticity and uncertainty, predicting future
video frames is exceptionally challenging. In this work, we study the problem
of video prediction by combining interpretability of stochastic state space
models and representation learning of deep neural networks. Our model builds
upon an variati... | [
"cs.CV"
] |
In this paper we investigate the use of model-based reinforcement learning to
assist people with Type 1 Diabetes with insulin dose decisions. The proposed
architecture consists of multiple Echo State Networks to predict blood glucose
levels combined with Model Predictive Controller for planning. Echo State
Network is a... | [
"cs.LG"
] |
Arbitrary shape text detection is a challenging task due to the high
complexity and variety of scene texts. In this work, we propose a novel
adaptive boundary proposal network for arbitrary shape text detection, which
can learn to directly produce accurate boundary for arbitrary shape text
without any post-processing. ... | [
"cs.CV"
] |
Learning nonlinear dynamics from aggregate data is a challenging problem
because the full trajectory of each individual is not available, namely, the
individual observed at one time may not be observed at the next time point, or
the identity of individual is unavailable. This is in sharp contrast to
learning dynamics w... | [
"cs.LG",
"math.AP",
"stat.ML"
] |
Retrieval-based place recognition is an efficient and effective solution for
enabling re-localization within a pre-built map or global data association for
Simultaneous Localization and Mapping (SLAM). The accuracy of such an approach
is heavily dependent on the quality of the extracted scene-level
representation. Whil... | [
"cs.CV",
"cs.RO"
] |
The success of machine learning applications often needs a large quantity of
data. Recently, federated learning (FL) is attracting increasing attention due
to the demand for data privacy and security, especially in the medical field.
However, the performance of existing FL approaches often deteriorates when
there exist... | [
"cs.LG",
"cs.AI"
] |
In this paper, we propose to learn an Unsupervised Single Object Tracker
(USOT) from scratch. We identify that three major challenges, i.e., moving
object discovery, rich temporal variation exploitation, and online update, are
the central causes of the performance bottleneck of existing unsupervised
trackers. To narrow... | [
"cs.CV"
] |
One of the key challenges of visual perception is to extract abstract models
of 3D objects and object categories from visual measurements, which are
affected by complex nuisance factors such as viewpoint, occlusion, motion, and
deformations. Starting from the recent idea of viewpoint factorization, we
propose a new app... | [
"cs.CV",
"stat.ML"
] |
In this paper, we investigate the conversion of a Twitter corpus into
geo-referenced raster cells holding the probability of the associated
geographical areas of being flooded. We describe a baseline approach that
combines a density ratio function, aggregation using a spatio-temporal Gaussian
kernel function, and TFIDF... | [
"cs.LG"
] |
Data-driven graph learning models a network by determining the strength of
connections between its nodes. The data refers to a graph signal which
associates a value with each graph node. Existing graph learning methods either
use simplified models for the graph signal, or they are prohibitively expensive
in terms of co... | [
"cs.LG",
"stat.AP",
"stat.ML"
] |
Plant disease detection is a huge problem and often require professional help
to detect the disease. This research focuses on creating a deep learning model
that detects the type of disease that affected the plant from the images of the
leaves of the plants. The deep learning is done with the help of Convolutional
Neur... | [
"cs.CV",
"eess.IV"
] |
Transfer learning aims to exploit pre-trained models for more efficient
follow-up training on wide range of downstream tasks and datasets, enabling
successful training also on small data. Recently, strong improvement was shown
for transfer learning and model generalization when increasing model, data and
compute budget... | [
"cs.LG",
"cs.AI",
"cs.CV"
] |
Combinatorial optimization is frequently used in computer vision. For
instance, in applications like semantic segmentation, human pose estimation and
action recognition, programs are formulated for solving inference in
Conditional Random Fields (CRFs) to produce a structured output that is
consistent with visual featur... | [
"cs.CV",
"I.4.6, I.2.6"
] |
While learning models are typically studied for inputs in the form of a fixed
dimensional feature vector, real world data is rarely found in this form. In
order to meet the basic requirement of traditional learning models, structural
data generally have to be converted into fix-length vectors in a handcrafted
manner, w... | [
"cs.LG",
"stat.ML"
] |
Even though it is well known that for most relevant computational problems
different algorithms may perform better on different classes of problem
instances, most researchers still focus on determining a single best
algorithmic configuration based on aggregate results such as the average. In
this paper, we propose Inte... | [
"cs.LG",
"cs.DM",
"cs.DS",
"90Cxx, 90C05",
"G.2.1; G.2.3; G.4"
] |
We describe a simple pre-training approach for point clouds. It works in
three steps: 1. Mask all points occluded in a camera view; 2. Learn an
encoder-decoder model to reconstruct the occluded points; 3. Use the encoder
weights as initialisation for downstream point cloud tasks. We find that even
when we construct a s... | [
"cs.CV",
"cs.LG"
] |
In the past few years, numerous deep learning methods have been proposed to
address the task of segmenting salient objects from RGB images. However, these
approaches depending on single modality fail to achieve the state-of-the-art
performance on widely used light field salient object detection (SOD) datasets,
which co... | [
"cs.CV"
] |
Batch Normalization (BN) is essential to effectively train state-of-the-art
deep Convolutional Neural Networks (CNN). It normalizes inputs to the layers
during training using the statistics of each mini-batch. In this work, we study
BN from the viewpoint of Fisher kernels. We show that assuming samples within a
mini-ba... | [
"cs.LG",
"cs.CV",
"stat.ML"
] |
The quadratic computational and memory complexities of the Transformer's
attention mechanism have limited its scalability for modeling long sequences.
In this paper, we propose Luna, a linear unified nested attention mechanism
that approximates softmax attention with two nested linear attention functions,
yielding only... | [
"cs.LG",
"cs.CL"
] |
Deep Convolutional Neural Networks (DCNNs) have recently shown state of the
art performance in high level vision tasks, such as image classification and
object detection. This work brings together methods from DCNNs and
probabilistic graphical models for addressing the task of pixel-level
classification (also called "s... | [
"cs.CV",
"cs.LG",
"cs.NE"
] |
Using touch devices to navigate in virtual 3D environments such as computer
assisted design (CAD) models or geographical information systems (GIS) is
inherently difficult for humans, as the 3D operations have to be performed by
the user on a 2D touch surface. This ill-posed problem is classically solved
with a fixed an... | [
"cs.LG",
"cs.AI",
"cs.HC"
] |
Existing works on semantic segmentation typically consider a small number of
labels, ranging from tens to a few hundreds. With a large number of labels,
training and evaluation of such task become extremely challenging due to
correlation between labels and lack of datasets with complete annotations. We
formulate semant... | [
"cs.CV"
] |
We propose a robust in-time predictor for in-hospital COVID-19 patient's
probability of requiring mechanical ventilation. A challenge in the risk
prediction for COVID-19 patients lies in the great variability and irregular
sampling of patient's vitals and labs observed in the clinical setting.
Existing methods have str... | [
"cs.LG",
"stat.AP"
] |
Texts from scene images typically consist of several characters and exhibit a
characteristic sequence structure. Existing methods capture the structure with
the sequence-to-sequence models by an encoder to have the visual
representations and then a decoder to translate the features into the label
sequence. In this pape... | [
"cs.CV"
] |
Embedding static graphs in low-dimensional vector spaces plays a key role in
network analytics and inference, supporting applications like node
classification, link prediction, and graph visualization. However, many
real-world networks present dynamic behavior, including topological evolution,
feature evolution, and di... | [
"cs.LG",
"cs.AI",
"37E25 (Primary) 68T30, 05C62, 58D10 (Secondary)",
"A.1; I.2.6"
] |
The field of DNA nanotechnology has made it possible to assemble, with high
yields, different structures that have actionable properties. For example,
researchers have created components that can be actuated. An exciting next step
is to combine these components into multifunctional nanorobots that could,
potentially, p... | [
"cs.LG",
"cs.AI",
"cs.RO"
] |
We describe a simple and general neural network weight compression approach,
in which the network parameters (weights and biases) are represented in a
"latent" space, amounting to a reparameterization. This space is equipped with
a learned probability model, which is used to impose an entropy penalty on the
parameter r... | [
"cs.LG",
"cs.CV",
"stat.ML"
] |
Classification of time series is a growing problem in different disciplines
due to the progressive digitalization of the world. Currently, the state of the
art in time series classification is dominated by Collective of
Transformation-Based Ensembles. This algorithm is composed of several
classifiers of diverse nature ... | [
"cs.LG",
"cs.IT",
"math.IT",
"stat.ML"
] |
Due to the lack of large-scale datasets, the prevailing approach in visual
sentiment analysis is to leverage models trained for object classification in
large datasets like ImageNet. However, objects are sentiment neutral which
hinders the expected gain of transfer learning for such tasks. In this work, we
propose to o... | [
"cs.CV"
] |
The graph Laplacian is a standard tool in data science, machine learning, and
image processing. The corresponding matrix inherits the complex structure of
the underlying network and is in certain applications densely populated. This
makes computations, in particular matrix-vector products, with the graph
Laplacian a ha... | [
"cs.LG",
"math.NA",
"stat.ML",
"68R10, 05C50, 65F15, 65T50, 68T05, 62H30"
] |
Animals exhibit an innate ability to learn regularities of the world through
interaction. By performing experiments in their environment, they are able to
discern the causal factors of variation and infer how they affect the world's
dynamics. Inspired by this, we attempt to equip reinforcement learning agents
with the ... | [
"cs.LG",
"cs.AI",
"cs.RO"
] |
Learning curves provide insight into the dependence of a learner's
generalization performance on the training set size. This important tool can be
used for model selection, to predict the effect of more training data, and to
reduce the computational complexity of model training and hyperparameter
tuning. This review re... | [
"cs.LG"
] |
Electronic Health Records often suffer from missing data, which poses a major
problem in clinical practice and clinical studies. A novel approach for dealing
with missing data are Generative Adversarial Nets (GANs), which have been
generating huge research interest in image generation and transformation.
Recently, rese... | [
"cs.LG"
] |
Generalization to out-of-distribution (OOD) data is a capability natural to
humans yet challenging for machines to reproduce. This is because most learning
algorithms strongly rely on the i.i.d.~assumption on source/target data, which
is often violated in practice due to domain shift. Domain generalization (DG)
aims to... | [
"cs.LG",
"cs.AI",
"cs.CV"
] |
We propose the use of a proportional-derivative (PD) control based policy
learned via reinforcement learning (RL) to estimate and forecast 3D human pose
from egocentric videos. The method learns directly from unsegmented egocentric
videos and motion capture data consisting of various complex human motions
(e.g., crouch... | [
"cs.CV",
"cs.AI",
"cs.LG",
"cs.RO"
] |
We present MBIS (Multivariate Bayesian Image Segmentation tool), a clustering
tool based on the mixture of multivariate normal distributions model. MBIS
supports multi-channel bias field correction based on a B-spline model. A
second methodological novelty is the inclusion of graph-cuts optimization for
the stationary ... | [
"cs.CV",
"62P10, 62F15"
] |
Graph representation learning nowadays becomes fundamental in analyzing
graph-structured data. Inspired by recent success of contrastive methods, in
this paper, we propose a novel framework for unsupervised graph representation
learning by leveraging a contrastive objective at the node level. Specifically,
we generate ... | [
"cs.LG",
"stat.ML"
] |
The adoption of machine learning in health care hinges on the transparency of
the used algorithms, necessitating the need for explanation methods. However,
despite a growing literature on explaining neural networks, no consensus has
been reached on how to evaluate those explanation methods. We propose IROF, a
new appro... | [
"cs.CV"
] |
In order to keep track of the operational state of power grid, the world's
largest sensor systems, smart grid, was built by deploying hundreds of millions
of smart meters. Such system makes it possible to discover and make quick
response to any hidden threat to the entire power grid. Non-technical losses
(NTLs) have al... | [
"cs.LG",
"stat.ML"
] |
Convolutional networks have marked their place over the last few years as the
best performing model for various visual tasks. They are, however, most suited
for supervised learning from large amounts of labeled data. Previous attempts
have been made to use unlabeled data to improve model performance by applying
unsuper... | [
"stat.ML",
"cs.LG"
] |
Accurate real-time traffic forecasting is a core technological problem
against the implementation of the intelligent transportation system. However,
it remains challenging considering the complex spatial and temporal
dependencies among traffic flows. In the spatial dimension, due to the
connectivity of the road network... | [
"cs.LG",
"stat.ML"
] |
In inductive transfer learning, fine-tuning pre-trained convolutional
networks substantially outperforms training from scratch. When using
fine-tuning, the underlying assumption is that the pre-trained model extracts
generic features, which are at least partially relevant for solving the target
task, but would be diffi... | [
"cs.LG"
] |
Image demosaicing - one of the most important early stages in digital camera
pipelines - addressed the problem of reconstructing a full-resolution image
from so-called color-filter-arrays. Despite tremendous progress made in the
pase decade, a fundamental issue that remains to be addressed is how to assure
the visual q... | [
"cs.CV"
] |
Networks have been widely used to represent the relations between objects
such as academic networks and social networks, and learning embedding for
networks has thus garnered plenty of research attention. Self-supervised
network representation learning aims at extracting node embedding without
external supervision. Rec... | [
"cs.LG",
"cs.IT",
"cs.SI",
"math.IT"
] |
Graph neural networks (GNNs) have been popularly used in analyzing
graph-structured data, showing promising results in various applications such
as node classification, link prediction and network recommendation. In this
paper, we present a new graph attention neural network, namely GIPA, for
attributed graph data lear... | [
"cs.LG"
] |
Group re-identification (G-ReID) is an important yet less-studied task. Its
challenges not only lie in appearance changes of individuals which have been
well-investigated in general person re-identification (ReID), but also derive
from group layout and membership changes. So the key task of G-ReID is to learn
represent... | [
"cs.CV",
"cs.MM"
] |
We consider distributions arising from a mixture of causal models, where each
model is represented by a directed acyclic graph (DAG). We provide a graphical
representation of such mixture distributions and prove that this representation
encodes the conditional independence relations of the mixture distribution. We
then... | [
"stat.ML",
"cs.LG"
] |
In this paper, we propose a neuro-symbolic framework called weighted Signal
Temporal Logic Neural Network (wSTL-NN) that combines the characteristics of
neural networks and temporal logics. Weighted Signal Temporal Logic (wSTL)
formulas are recursively composed of subformulas that are combined using
logical and tempora... | [
"cs.LG",
"cs.NE"
] |
Every year physicians face an increasing demand of image-based diagnosis from
patients, a problem that can be addressed with recent artificial intelligence
methods. In this context, we survey works in the area of automatic report
generation from medical images, with emphasis on methods using deep neural
networks, with ... | [
"cs.CV",
"cs.AI",
"cs.CL",
"cs.LG"
] |
Resolution of the complex problem of image retrieval for diagram images has
yet to be reached. Deep learning methods continue to excel in the fields of
object detection and image classification applied to natural imagery. However,
the application of such methodologies applied to binary imagery remains limited
due to la... | [
"cs.CV"
] |
Feature representations from pre-trained deep neural networks have been known
to exhibit excellent generalization and utility across a variety of related
tasks. Fine-tuning is by far the simplest and most widely used approach that
seeks to exploit and adapt these feature representations to novel tasks with
limited data... | [
"cs.CV",
"cs.LG",
"stat.ML"
] |
The development of practical applications, such as autonomous driving and
robotics, has brought increasing attention to 3D point cloud understanding.
While deep learning has achieved remarkable success on image-based tasks, there
are many unique challenges faced by deep neural networks in processing massive,
unstructur... | [
"cs.CV",
"cs.LG"
] |
The rapid development and wide utilization of object detection techniques
have aroused attention on both accuracy and speed of object detectors. However,
the current state-of-the-art object detection works are either
accuracy-oriented using a large model but leading to high latency or
speed-oriented using a lightweight... | [
"cs.CV",
"cs.AI",
"cs.LG"
] |
Registration is a fundamental task in medical robotics and is often a crucial
step for many downstream tasks such as motion analysis, intra-operative
tracking and image segmentation. Popular registration methods such as ANTs and
NiftyReg optimize objective functions for each pair of images from scratch,
which are time-... | [
"cs.CV",
"cs.LG",
"cs.NE",
"cs.RO",
"eess.IV"
] |
Due to the lack of enough generalization in the state-space, common methods
in Reinforcement Learning (RL) suffer from slow learning speed especially in
the early learning trials. This paper introduces a model-based method in
discrete state-spaces for increasing learning speed in terms of required
experience (but not r... | [
"stat.ML",
"cs.AI",
"cs.LG"
] |
Electronic Health Records (EHRs) provide vital contextual information to
radiologists and other physicians when making a diagnosis. Unfortunately,
because a given patient's record may contain hundreds of notes and reports,
identifying relevant information within these in the short time typically
allotted to a case is v... | [
"cs.LG",
"stat.ML"
] |
Knowledge Graph Embeddings (KGEs) have shown promising performance on link
prediction tasks by mapping the entities and relations from a knowledge graph
into a geometric space (usually a vector space). Ultimately, the plausibility
of the predicted links is measured by using a scoring function over the learned
embedding... | [
"cs.LG",
"cs.AI"
] |
This paper proposes the idea of using a generative adversarial network (GAN)
to assist a novice user in designing real-world shapes with a simple interface.
The user edits a voxel grid with a painting interface (like Minecraft). Yet, at
any time, he/she can execute a SNAP command, which projects the current voxel
grid ... | [
"cs.CV",
"cs.GR"
] |
The extension of image generation to video generation turns out to be a very
difficult task, since the temporal dimension of videos introduces an extra
challenge during the generation process. Besides, due to the limitation of
memory and training stability, the generation becomes increasingly challenging
with the incre... | [
"cs.CV",
"cs.AI",
"stat.ML"
] |
Neural Architecture Search (NAS) was first proposed to achieve
state-of-the-art performance through the discovery of new architecture
patterns, without human intervention. An over-reliance on expert knowledge in
the search space design has however led to increased performance (local optima)
without significant architec... | [
"cs.LG",
"cs.NE",
"stat.ML"
] |
Virtually all aspects of modern life depend on space technology. Thanks to
the great advancement of computer vision in general and deep learning-based
techniques in particular, over the decades, the world witnessed the growing use
of deep learning in solving problems for space applications, such as
self-driving robot, ... | [
"cs.CV"
] |
Attribute image manipulation has been a very active topic since the
introduction of Generative Adversarial Networks (GANs). Exploring the
disentangled attribute space within a transformation is a very challenging task
due to the multiple and mutually-inclusive nature of the facial images, where
different labels (eyegla... | [
"cs.CV"
] |
This work proposes a new method to accurately complete sparse LiDAR maps
guided by RGB images. For autonomous vehicles and robotics the use of LiDAR is
indispensable in order to achieve precise depth predictions. A multitude of
applications depend on the awareness of their surroundings, and use depth cues
to reason and... | [
"cs.CV"
] |
Video Visual Relation Detection (VidVRD), has received significant attention
of our community over recent years. In this paper, we apply the
state-of-the-art video object tracklet detection pipeline MEGA and deepSORT to
generate tracklet proposals. Then we perform VidVRD in a tracklet-based manner
without any pre-cutti... | [
"cs.CV"
] |
Deep learning models with attention mechanisms have achieved exceptional
results for many tasks, including language tasks and recommendation systems.
Whereas previous studies have emphasized allocation of phone agents, we focused
on inbound call prediction for customer service. A common method of analyzing
user history... | [
"cs.LG"
] |
We present a method for reconstructing images viewed by observers based only
on their eye movements. By exploring the relationships between gaze patterns
and image stimuli, the "What Are You Looking At?" (WAYLA) system learns to
synthesize photo-realistic images that are similar to the original pictures
being viewed. T... | [
"cs.CV"
] |
The principle of Photo Response Non Uniformity (PRNU) is often exploited to
deduce the identity of the smartphone device whose camera or sensor was used to
acquire a certain image. In this work, we design an algorithm that perturbs a
face image acquired using a smartphone camera such that (a) sensor-specific
details pe... | [
"cs.CV",
"eess.IV"
] |
Transfer learning which aims at utilizing knowledge learned from one problem
(source domain) to solve another different but related problem (target domain)
has attracted wide research attentions. However, the current transfer learning
methods are mostly uninterpretable, especially to people without ML expertise.
In thi... | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
Object recognition has become a crucial part of machine learning and computer
vision recently. The current approach to object recognition involves Deep
Learning and uses Convolutional Neural Networks to learn the pixel patterns of
the objects implicitly through backpropagation. However, CNNs require thousands
of exampl... | [
"cs.CV"
] |
Visual perception is critically influenced by the focus of attention. Due to
limited resources, it is well known that neural representations are biased in
favor of attended locations. Using concurrent eye-tracking and functional
Magnetic Resonance Imaging (fMRI) recordings from a large cohort of human
subjects watching... | [
"cs.CV",
"cs.LG",
"q-bio.NC"
] |
Model-based reinforcement learning (RL) is appealing because (i) it enables
planning and thus more strategic exploration, and (ii) by decoupling dynamics
from rewards, it enables fast transfer to new reward functions. However,
learning an accurate Markov Decision Process (MDP) over high-dimensional states
(e.g., raw pi... | [
"cs.LG",
"cs.AI",
"stat.ML"
] |
Facial expression recognition has been an active research area over the past
few decades, and it is still challenging due to the high intra-class variation.
Traditional approaches for this problem rely on hand-crafted features such as
SIFT, HOG and LBP, followed by a classifier trained on a database of images or
vide... | [
"cs.CV"
] |
Transformers provide promising accuracy and have become popular and used in
various domains such as natural language processing and computer vision.
However, due to their massive number of model parameters, memory and
computation requirements, they are not suitable for resource-constrained
low-power devices. Even with ... | [
"cs.LG",
"cs.CV"
] |
Vision-based sign language recognition aims at helping deaf people to
communicate with others. However, most existing sign language datasets are
limited to a small number of words. Due to the limited vocabulary size, models
learned from those datasets cannot be applied in practice. In this paper, we
introduce a new lar... | [
"cs.CV",
"cs.HC",
"cs.MM",
"cs.NE"
] |
We give an overview of recent exciting achievements of deep reinforcement
learning (RL). We discuss six core elements, six important mechanisms, and
twelve applications. We start with background of machine learning, deep
learning and reinforcement learning. Next we discuss core RL elements,
including value function, in... | [
"cs.LG"
] |
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