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Reinforcement learning usually uses the feedback rewards of environmental to
train agents. But the rewards in the actual environment are sparse, and even
some environments will not rewards. Most of the current methods are difficult
to get good performance in sparse reward or non-reward environments. Although
using shaped rewards is effective when solving sparse reward tasks, it is
limited to specific problems and learning is also susceptible to local optima.
We propose a model-free method that does not rely on environmental rewards to
solve the problem of sparse rewards in the general environment. Our method use
the minimum number of transitions between states as the distance to replace the
rewards of environmental, and proposes a goal-distance gradient to achieve
policy improvement. We also introduce a bridge point planning method based on
the characteristics of our method to improve exploration efficiency, thereby
solving more complex tasks. Experiments show that our method performs better on
sparse reward and local optimal problems in complex environments than previous
work. | [
"cs.LG",
"cs.AI",
"stat.ML"
]
|
Many interactive image segmentation techniques are based on semi-supervised
learning. The user may label some pixels from each object and the SSL algorithm
will propagate the labels from the labeled to the unlabeled pixels, finding
object boundaries. This paper proposes a new SSL graph-based interactive image
segmentation approach, using undirected and unweighted kNN graphs, from which
the unlabeled nodes receive contributions from other nodes (either labeled or
unlabeled). It is simpler than many other techniques, but it still achieves
significant classification accuracy in the image segmentation task. Computer
simulations are performed using some real-world images, extracted from the
Microsoft GrabCut dataset. The segmentation results show the effectiveness of
the proposed approach. | [
"cs.LG",
"stat.ML"
]
|
The end-to-end Human Mesh Recovery (HMR) approach has been successfully used
for 3D body reconstruction. However, most HMR-based frameworks reconstruct
human body by directly learning mesh parameters from images or videos, while
lacking explicit guidance of 3D human pose in visual data. As a result, the
generated mesh often exhibits incorrect pose for complex activities. To tackle
this problem, we propose to exploit 3D pose to calibrate human mesh.
Specifically, we develop two novel Pose Calibration frameworks, i.e., Serial
PC-HMR and Parallel PC-HMR. By coupling advanced 3D pose estimators and HMR in
a serial or parallel manner, these two frameworks can effectively correct human
mesh with guidance of a concise pose calibration module. Furthermore, since the
calibration module is designed via non-rigid pose transformation, our PC-HMR
frameworks can flexibly tackle bone length variations to alleviate misplacement
in the calibrated mesh. Finally, our frameworks are based on generic and
complementary integration of data-driven learning and geometrical modeling. Via
plug-and-play modules, they can be efficiently adapted for both
image/video-based human mesh recovery. Additionally, they have no requirement
of extra 3D pose annotations in the testing phase, which releases inference
difficulties in practice. We perform extensive experiments on the popular
bench-marks, i.e., Human3.6M, 3DPW and SURREAL, where our PC-HMR frameworks
achieve the SOTA results. | [
"cs.CV"
]
|
Two of the main challenges for cropland classification by satellite
time-series images are insufficient ground-truth data and inaccessibility of
high-quality hyperspectral images for under-developed areas. Unlabeled
medium-resolution satellite images are abundant, but how to benefit from them
is an open question. We will show how to leverage their potential for cropland
classification using self-supervised tasks. Self-supervision is an approach
where we provide simple training signals for the samples, which are apparent
from the data's structure. Hence, they are cheap to acquire and explain a
simple concept about the data. We introduce three self-supervised tasks for
cropland classification. They reduce epistemic uncertainty, and the resulting
model shows superior accuracy in a wide range of settings compared to SVM and
Random Forest. Subsequently, we use the self-supervised tasks to perform
unsupervised domain adaptation and benefit from the labeled samples in other
regions. It is crucial to know what information to transfer to avoid degrading
the performance. We show how to automate the information selection and transfer
process in cropland classification even when the source and target areas have a
very different feature distribution. We improved the model by about 24%
compared to a baseline architecture without any labeled sample in the target
domain. Our method is amenable to gradual improvement, works with
medium-resolution satellite images, and does not require complicated models.
Code and data are available. | [
"cs.CV",
"cs.AI"
]
|
To improve the stability of GAN training we need to understand why they can
produce realistic samples. Presently, this is attributed to properties of the
divergence obtained under an optimal discriminator. This argument has a
fundamental flaw: If we do not impose regularity of the discriminator, it can
exploit visually imperceptible errors of the generator to always achieve the
maximal generator loss. In practice, gradient penalties are used to regularize
the discriminator. However, this needs a metric on the space of images that
captures visual similarity. Such a metric is not known, which explains the
limited success of gradient penalties in stabilizing GANs. We argue that the
performance of GANs is instead due to the implicit competitive regularization
(ICR) arising from the simultaneous optimization of generator and
discriminator. ICR promotes solutions that look real to the discriminator and
thus leverages its inductive biases to generate realistic images. We show that
opponent-aware modelling of generator and discriminator, as present in
competitive gradient descent (CGD), can significantly strengthen ICR and thus
stabilize GAN training without explicit regularization. In our experiments, we
use an existing implementation of WGAN-GP and show that by training it with CGD
we can improve the inception score (IS) on CIFAR10 for a wide range of
scenarios, without any hyperparameter tuning. The highest IS is obtained by
combining CGD with the WGAN-loss, without any explicit regularization. | [
"cs.LG",
"stat.ML"
]
|
Scene flow estimation is the task to predict the point-wise 3D displacement
vector between two consecutive frames of point clouds, which has important
application in fields such as service robots and autonomous driving. Although
many previous works have explored greatly on scene flow estimation based on
point clouds, we point out two problems that have not been noticed or well
solved before: 1) Points of adjacent frames in repetitive patterns may be
wrongly associated due to similar spatial structure in their neighbourhoods; 2)
Scene flow between adjacent frames of point clouds with long-distance movement
may be inaccurately estimated. To solve the first problem, we propose a novel
context-aware set conv layer to exploit contextual structure information of
Euclidean space and learn soft aggregation weights for local point features.
Our design is inspired by human perception of contextual structure information
during scene understanding. We incorporate the context-aware set conv layer in
a context-aware point feature pyramid module of 3D point clouds for scene flow
estimation. For the second problem, we propose an explicit residual flow
learning structure in the residual flow refinement layer to cope with
long-distance movement. The experiments and ablation study on FlyingThings3D
and KITTI scene flow datasets demonstrate the effectiveness of each proposed
component and that we solve problem of ambiguous inter-frame association and
long-distance movement estimation. Quantitative results on both FlyingThings3D
and KITTI scene flow datasets show that our method achieves state-of-the-art
performance, surpassing all other previous works to the best of our knowledge
by at least 25%. | [
"cs.CV"
]
|
In this work, we present Point Transformer, a deep neural network that
operates directly on unordered and unstructured point sets. We design Point
Transformer to extract local and global features and relate both
representations by introducing the local-global attention mechanism, which aims
to capture spatial point relations and shape information. For that purpose, we
propose SortNet, as part of the Point Transformer, which induces input
permutation invariance by selecting points based on a learned score. The output
of Point Transformer is a sorted and permutation invariant feature list that
can directly be incorporated into common computer vision applications. We
evaluate our approach on standard classification and part segmentation
benchmarks to demonstrate competitive results compared to the prior work. | [
"cs.CV"
]
|
Hyperspectral image has become increasingly crucial due to its abundant
spectral information. However, It has poor spatial resolution with the
limitation of the current imaging mechanism. Nowadays, many convolutional
neural networks have been proposed for the hyperspectral image super-resolution
problem. However, convolutional neural network (CNN) based methods only
consider the local information instead of the global one with the limited
kernel size of receptive field in the convolution operation. In this paper, we
design a network based on the transformer for fusing the low-resolution
hyperspectral images and high-resolution multispectral images to obtain the
high-resolution hyperspectral images. Thanks to the representing ability of the
transformer, our approach is able to explore the intrinsic relationships of
features globally. Furthermore, considering the LR-HSIs hold the main spectral
structure, the network focuses on the spatial detail estimation releasing from
the burden of reconstructing the whole data. It reduces the mapping space of
the proposed network, which enhances the final performance. Various experiments
and quality indexes show our approach's superiority compared with other
state-of-the-art methods. | [
"cs.CV"
]
|
State-of-the-art methods for object detection use region proposal networks
(RPN) to hypothesize object location. These networks simultaneously predicts
object bounding boxes and \emph{objectness} scores at each location in the
image. Unlike natural images for which RPN algorithms were originally designed,
most medical images are acquired following standard protocols, thus organs in
the image are typically at a similar location and possess similar geometrical
characteristics (e.g. scale, aspect-ratio, etc.). Therefore, medical image
acquisition protocols hold critical localization and geometric information that
can be incorporated for faster and more accurate detection. This paper presents
a novel attention mechanism for the detection of organs by incorporating
imaging protocol information. Our novel selective attention approach (i)
effectively shrinks the search space inside the feature map, (ii) appends
useful localization information to the hypothesized proposal for the detection
architecture to learn where to look for each organ, and (iii) modifies the
pyramid of regression references in the RPN by incorporating organ- and
modality-specific information, which results in additional time reduction. We
evaluated the proposed framework on a dataset of 768 chest X-ray images
obtained from a diverse set of sources. Our results demonstrate superior
performance for the detection of the lung field compared to the
state-of-the-art, both in terms of detection accuracy, demonstrating an
improvement of $>7\%$ in Dice score, and reduced processing time by $27.53\%$
due to fewer hypotheses. | [
"cs.CV"
]
|
We propose a single-shot method for simultaneous 3D object segmentation and
6-DOF pose estimation in pure 3D point clouds scenes based on a consensus that
\emph{one point only belongs to one object}, i.e., each point has the potential
power to predict the 6-DOF pose of its corresponding object. Unlike the
recently proposed methods of the similar task, which rely on 2D detectors to
predict the projection of 3D corners of the 3D bounding boxes and the 6-DOF
pose must be estimated by a PnP like spatial transformation method, ours is
concise enough not to require additional spatial transformation between
different dimensions. Due to the lack of training data for many objects, the
recently proposed 2D detection methods try to generate training data by using
rendering engine and achieve good results. However, rendering in 3D space along
with 6-DOF is relatively difficult. Therefore, we propose an augmented reality
technology to generate the training data in semi-virtual reality 3D space. The
key component of our method is a multi-task CNN architecture that can
simultaneously predicts the 3D object segmentation and 6-DOF pose estimation in
pure 3D point clouds.
For experimental evaluation, we generate expanded training data for two
state-of-the-arts 3D object datasets \cite{PLCHF}\cite{TLINEMOD} by using
Augmented Reality technology (AR). We evaluate our proposed method on the two
datasets. The results show that our method can be well generalized into
multiple scenarios and provide performance comparable to or better than the
state-of-the-arts. | [
"cs.CV"
]
|
Continuous action policy search is currently the focus of intensive research,
driven both by the recent success of deep reinforcement learning algorithms and
the emergence of competitors based on evolutionary algorithms. In this paper,
we present a broad survey of policy search methods, providing a unified
perspective on very different approaches, including also Bayesian Optimization
and directed exploration methods. The main message of this overview is in the
relationship between the families of methods, but we also outline some factors
underlying sample efficiency properties of the various approaches. | [
"cs.LG"
]
|
Within the context of autonomous driving a model-based reinforcement learning
algorithm is proposed for the design of neural network-parameterized
controllers. Classical model-based control methods, which include sampling- and
lattice-based algorithms and model predictive control, suffer from the
trade-off between model complexity and computational burden required for the
online solution of expensive optimization or search problems at every short
sampling time. To circumvent this trade-off, a 2-step procedure is motivated:
first learning of a controller during offline training based on an arbitrarily
complicated mathematical system model, before online fast feedforward
evaluation of the trained controller. The contribution of this paper is the
proposition of a simple gradient-free and model-based algorithm for deep
reinforcement learning using task separation with hill climbing (TSHC). In
particular, (i) simultaneous training on separate deterministic tasks with the
purpose of encoding many motion primitives in a neural network, and (ii) the
employment of maximally sparse rewards in combination with virtual velocity
constraints (VVCs) in setpoint proximity are advocated. | [
"cs.LG",
"cs.RO"
]
|
Statistical analysis of a graph often starts with embedding, the process of
representing its nodes as points in space. How to choose the embedding
dimension is a nuanced decision in practice, but in theory a notion of true
dimension is often available. In spectral embedding, this dimension may be very
high. However, this paper shows that existing random graph models, including
graphon and other latent position models, predict the data should live near a
much lower-dimensional set. One may therefore circumvent the curse of
dimensionality by employing methods which exploit hidden manifold structure. | [
"stat.ML",
"cs.LG"
]
|
We apply a temporal edge prediction model for weighted dynamic graphs to
predict time-dependent changes in molecular structure. Each molecule is
represented as a complete graph in which each atom is a vertex and all vertex
pairs are connected by an edge weighted by the Euclidean distance between atom
pairs. We ingest a sequence of complete molecular graphs into a dynamic graph
neural network (GNN) to predict the graph at the next time step. Our dynamic
GNN predicts atom-to-atom distances with a mean absolute error of 0.017 \r{A},
which is considered ``chemically accurate'' for molecular simulations. We also
explored the transferability of a trained network to new molecular systems and
found that finetuning with less than 10% of the total trajectory provides a
mean absolute error of the same order of magnitude as that when training from
scratch on the full molecular trajectory. | [
"cs.LG",
"physics.chem-ph"
]
|
Mail privacy protection aims to prevent unauthorized access to hidden content
within an envelope since normal paper envelopes are not as safe as we think. In
this paper, for the first time, we show that with a well designed deep learning
model, the hidden content may be largely recovered without opening the
envelope. We start by modeling deep learning-based privacy attacks on physical
mail content as learning the mapping from the camera-captured envelope front
face image to the hidden content, then we explicitly model the mapping as a
combination of perspective transformation, image dehazing and denoising using a
deep convolutional neural network, named Neural-STE (See-Through-Envelope). We
show experimentally that hidden content details, such as texture and image
structure, can be clearly recovered. Finally, our formulation and model allow
us to design envelopes that can counter deep learning-based privacy attacks on
physical mail. | [
"cs.CV",
"cs.CR",
"eess.IV"
]
|
We discuss promising recent contributions on quantifying feature relevance
using Shapley values, where we observed some confusion on which probability
distribution is the right one for dropped features. We argue that the confusion
is based on not carefully distinguishing between observational and
interventional conditional probabilities and try a clarification based on
Pearl's seminal work on causality. We conclude that unconditional rather than
conditional expectations provide the right notion of dropping features in
contradiction to the theoretical justification of the software package SHAP.
Parts of SHAP are unaffected because unconditional expectations (which we argue
to be conceptually right) are used as approximation for the conditional ones,
which encouraged others to `improve' SHAP in a way that we believe to be
flawed. | [
"stat.ML",
"cs.LG"
]
|
Generative adversarial networks (GANs) have been extensively studied in the
past few years. Arguably their most significant impact has been in the area of
computer vision where great advances have been made in challenges such as
plausible image generation, image-to-image translation, facial attribute
manipulation and similar domains. Despite the significant successes achieved to
date, applying GANs to real-world problems still poses significant challenges,
three of which we focus on here. These are: (1) the generation of high quality
images, (2) diversity of image generation, and (3) stable training. Focusing on
the degree to which popular GAN technologies have made progress against these
challenges, we provide a detailed review of the state of the art in GAN-related
research in the published scientific literature. We further structure this
review through a convenient taxonomy we have adopted based on variations in GAN
architectures and loss functions. While several reviews for GANs have been
presented to date, none have considered the status of this field based on their
progress towards addressing practical challenges relevant to computer vision.
Accordingly, we review and critically discuss the most popular
architecture-variant, and loss-variant GANs, for tackling these challenges. Our
objective is to provide an overview as well as a critical analysis of the
status of GAN research in terms of relevant progress towards important computer
vision application requirements. As we do this we also discuss the most
compelling applications in computer vision in which GANs have demonstrated
considerable success along with some suggestions for future research
directions. Code related to GAN-variants studied in this work is summarized on
https://github.com/sheqi/GAN_Review. | [
"cs.LG",
"cs.CV"
]
|
Object detection in optical remote sensing images, being a fundamental but
challenging problem in the field of aerial and satellite image analysis, plays
an important role for a wide range of applications and is receiving significant
attention in recent years. While enormous methods exist, a deep review of the
literature concerning generic object detection is still lacking. This paper
aims to provide a review of the recent progress in this field. Different from
several previously published surveys that focus on a specific object class such
as building and road, we concentrate on more generic object categories
including, but are not limited to, road, building, tree, vehicle, ship,
airport, urban-area. Covering about 270 publications we survey 1) template
matching-based object detection methods, 2) knowledge-based object detection
methods, 3) object-based image analysis (OBIA)-based object detection methods,
4) machine learning-based object detection methods, and 5) five publicly
available datasets and three standard evaluation metrics. We also discuss the
challenges of current studies and propose two promising research directions,
namely deep learning-based feature representation and weakly supervised
learning-based geospatial object detection. It is our hope that this survey
will be beneficial for the researchers to have better understanding of this
research field. | [
"cs.CV"
]
|
We study Policy-extended Value Function Approximator (PeVFA) in Reinforcement
Learning (RL), which extends conventional value function approximator (VFA) to
take as input not only the state (and action) but also an explicit policy
representation. Such an extension enables PeVFA to preserve values of multiple
policies at the same time and brings an appealing characteristic, i.e.,
\emph{value generalization among policies}. We formally analyze the value
generalization under Generalized Policy Iteration (GPI). From theoretical and
empirical lens, we show that generalized value estimates offered by PeVFA may
have lower initial approximation error to true values of successive policies,
which is expected to improve consecutive value approximation during GPI. Based
on above clues, we introduce a new form of GPI with PeVFA which leverages the
value generalization along policy improvement path. Moreover, we propose a
representation learning framework for RL policy, providing several approaches
to learn effective policy embeddings from policy network parameters or
state-action pairs. In our experiments, we evaluate the efficacy of value
generalization offered by PeVFA and policy representation learning in several
OpenAI Gym continuous control tasks. For a representative instance of algorithm
implementation, Proximal Policy Optimization (PPO) re-implemented under the
paradigm of GPI with PeVFA achieves about 40\% performance improvement on its
vanilla counterpart in most environments. | [
"cs.LG",
"cs.AI"
]
|
Recently, two methods have shown outstanding performance for clustering
images and jointly learning the feature representation. The first, called
Information Maximiz-ing Self-Augmented Training (IMSAT), maximizes the mutual
information between input and clusters while using a regularization term based
on virtual adversarial examples. The second, named Invariant Information
Clustering (IIC), maximizes the mutual information between the clustering of a
sample and its geometrically transformed version. These methods use mutual
information in distinct ways and leverage different kinds of transformations.
This work proposes a comprehensive analysis of transformation and losses for
deep clustering, where we compare numerous combinations of these two components
and evaluate how they interact with one another. Results suggest that mutual
information between a sample and its transformed representation leads to
state-of-the-art performance for deep clustering, especially when used jointly
with geometrical and adversarial transformations. | [
"cs.CV"
]
|
We study the adversarial multi-armed bandit problem where partial
observations are available and where, in addition to the loss incurred for each
action, a \emph{switching cost} is incurred for shifting to a new action. All
previously known results incur a factor proportional to the independence number
of the feedback graph. We give a new algorithm whose regret guarantee depends
only on the domination number of the graph. We further supplement that result
with a lower bound. Finally, we also give a new algorithm with improved policy
regret bounds when partial counterfactual feedback is available. | [
"cs.LG",
"stat.ML"
]
|
Deep learning models, such as convolutional neural networks, have long been
applied to image and multi-media tasks, particularly those with structured
data. More recently, there has been more attention to unstructured data that
can be represented via graphs. These types of data are often found in health
and medicine, social networks, and research data repositories. Graph
convolutional neural networks have recently gained attention in the field of
deep learning that takes advantage of graph-based data representation with
automatic feature extraction via convolutions. Given the popularity of these
methods in a wide range of applications, robust uncertainty quantification is
vital. This remains a challenge for large models and unstructured datasets.
Bayesian inference provides a principled approach to uncertainty quantification
of model parameters for deep learning models. Although Bayesian inference has
been used extensively elsewhere, its application to deep learning remains
limited due to the computational requirements of the Markov Chain Monte Carlo
(MCMC) methods. Recent advances in parallel computing and advanced proposal
schemes in MCMC sampling methods has opened the path for Bayesian deep
learning. In this paper, we present Bayesian graph convolutional neural
networks that employ tempered MCMC sampling with Langevin-gradient proposal
distribution implemented via parallel computing. Our results show that the
proposed method can provide accuracy similar to advanced optimisers while
providing uncertainty quantification for key benchmark problems. | [
"cs.LG"
]
|
Supervised training a deep neural network aims to "teach" the network to
mimic human visual perception that is represented by image-and-label pairs in
the training data. Superpixelized (SP) images are visually perceivable to
humans, but a conventionally trained deep learning model often performs poorly
when working on SP images. To better mimic human visual perception, we think it
is desirable for the deep learning model to be able to perceive not only raw
images but also SP images. In this paper, we propose a new superpixel-based
data augmentation (SPDA) method for training deep learning models for
biomedical image segmentation. Our method applies a superpixel generation
scheme to all the original training images to generate superpixelized images.
The SP images thus obtained are then jointly used with the original training
images to train a deep learning model. Our experiments of SPDA on four
biomedical image datasets show that SPDA is effective and can consistently
improve the performance of state-of-the-art fully convolutional networks for
biomedical image segmentation in 2D and 3D images. Additional studies also
demonstrate that SPDA can practically reduce the generalization gap. | [
"cs.CV",
"cs.AI",
"cs.LG"
]
|
Recently, many methods have been proposed for object detection. They cannot
detect objects by semantic features, adaptively. In this work, according to
channel and spatial attention mechanisms, we mainly analyze that different
methods detect objects adaptively. Some state-of-the-art detectors combine
different feature pyramids with many mechanisms to enhance multi-level semantic
information. However, they require more cost. This work addresses that by an
anchor-free detector with shared encoder-decoder with attention mechanism,
extracting shared features. We consider features of different levels from
backbone (e.g., ResNet-50) as the basis features. Then, we feed the features
into a simple module, followed by a detector header to detect objects.
Meantime, we use the semantic features to revise geometric locations, and the
detector is a pixel-semantic revising of position. More importantly, this work
analyzes the impact of different pooling strategies (e.g., mean, maximum or
minimum) on multi-scale objects, and finds the minimum pooling improve
detection performance on small objects better. Compared with state-of-the-art
MNC based on ResNet-101 for the standard MSCOCO 2014 baseline, our method
improves detection AP of 3.8%. | [
"cs.CV",
"cs.LG",
"eess.IV"
]
|
We study the global convergence of generative adversarial imitation learning
for linear quadratic regulators, which is posed as minimax optimization. To
address the challenges arising from non-convex-concave geometry, we analyze the
alternating gradient algorithm and establish its Q-linear rate of convergence
to a unique saddle point, which simultaneously recovers the globally optimal
policy and reward function. We hope our results may serve as a small step
towards understanding and taming the instability in imitation learning as well
as in more general non-convex-concave alternating minimax optimization that
arises from reinforcement learning and generative adversarial learning. | [
"cs.LG",
"cs.AI",
"math.OC",
"stat.ML"
]
|
Automated Machine Learning(Auto-ML) pruning methods aim at searching a
pruning strategy automatically to reduce the computational complexity of deep
Convolutional Neural Networks(deep CNNs). However, some previous work found
that the results of many Auto-ML pruning methods cannot even surpass the
results of the uniformly pruning method. In this paper, the ineffectiveness of
Auto-ML pruning which is caused by unfull and unfair training of the supernet
is shown. A deep supernet suffers from unfull training because it contains too
many candidates. To overcome the unfull training, a stage-wise pruning(SWP)
method is proposed, which splits a deep supernet into several stage-wise
supernets to reduce the candidate number and utilize inplace distillation to
supervise the stage training. Besides, A wide supernet is hit by unfair
training since the sampling probability of each channel is unequal. Therefore,
the fullnet and the tinynet are sampled in each training iteration to ensure
each channel can be overtrained. Remarkably, the proxy performance of the
subnets trained with SWP is closer to the actual performance than that of most
of the previous Auto-ML pruning work. Experiments show that SWP achieves the
state-of-the-art on both CIFAR-10 and ImageNet under the mobile setting. | [
"cs.CV"
]
|
A natural approach to generative modeling of videos is to represent them as a
composition of moving objects. Recent works model a set of 2D sprites over a
slowly-varying background, but without considering the underlying 3D scene that
gives rise to them. We instead propose to model a video as the view seen while
moving through a scene with multiple 3D objects and a 3D background. Our model
is trained from monocular videos without any supervision, yet learns to
generate coherent 3D scenes containing several moving objects. We conduct
detailed experiments on two datasets, going beyond the visual complexity
supported by state-of-the-art generative approaches. We evaluate our method on
depth-prediction and 3D object detection -- tasks which cannot be addressed by
those earlier works -- and show it out-performs them even on 2D instance
segmentation and tracking. | [
"cs.CV",
"cs.LG",
"stat.ML"
]
|
In supervised learning, it is known that overparameterized neural networks
with one hidden layer provably and efficiently learn and generalize, when
trained using stochastic gradient descent with sufficiently small learning rate
and suitable initialization. In contrast, the benefit of overparameterization
in unsupervised learning is not well understood. Normalizing flows (NFs)
constitute an important class of models in unsupervised learning for sampling
and density estimation. In this paper, we theoretically and empirically analyze
these models when the underlying neural network is one-hidden-layer
overparameterized network. Our main contributions are two-fold: (1) On the one
hand, we provide theoretical and empirical evidence that for a class of NFs
containing most of the existing NF models, overparametrization hurts training.
(2) On the other hand, we prove that unconstrained NFs, a recently introduced
model, can efficiently learn any reasonable data distribution under minimal
assumptions when the underlying network is overparametrized. | [
"cs.LG",
"cs.AI"
]
|
We introduce a new task, Video-and-Language Inference, for joint multimodal
understanding of video and text. Given a video clip with aligned subtitles as
premise, paired with a natural language hypothesis based on the video content,
a model needs to infer whether the hypothesis is entailed or contradicted by
the given video clip. A new large-scale dataset, named Violin
(VIdeO-and-Language INference), is introduced for this task, which consists of
95,322 video-hypothesis pairs from 15,887 video clips, spanning over 582 hours
of video. These video clips contain rich content with diverse temporal
dynamics, event shifts, and people interactions, collected from two sources:
(i) popular TV shows, and (ii) movie clips from YouTube channels. In order to
address our new multimodal inference task, a model is required to possess
sophisticated reasoning skills, from surface-level grounding (e.g., identifying
objects and characters in the video) to in-depth commonsense reasoning (e.g.,
inferring causal relations of events in the video). We present a detailed
analysis of the dataset and an extensive evaluation over many strong baselines,
providing valuable insights on the challenges of this new task. | [
"cs.CV",
"cs.AI",
"cs.CL"
]
|
Source code (Context) and its parsed abstract syntax tree (AST; Structure)
are two complementary representations of the same computer program.
Traditionally, designers of machine learning models have relied predominantly
either on Structure or Context. We propose a new model, which jointly learns on
Context and Structure of source code. In contrast to previous approaches, our
model uses only language-agnostic features, i.e., source code and features that
can be computed directly from the AST. Besides obtaining state-of-the-art on
monolingual code summarization on all five programming languages considered in
this work, we propose the first multilingual code summarization model. We show
that jointly training on non-parallel data from multiple programming languages
improves results on all individual languages, where the strongest gains are on
low-resource languages. Remarkably, multilingual training only from Context
does not lead to the same improvements, highlighting the benefits of combining
Structure and Context for representation learning on code. | [
"cs.LG",
"cs.SE"
]
|
Autonomous driving is a multi-task problem requiring a deep understanding of
the visual environment. End-to-end autonomous systems have attracted increasing
interest as a method of learning to drive without exhaustively programming
behaviours for different driving scenarios. When humans drive, they rely on a
finely tuned sensory system which enables them to quickly acquire the
information they need while filtering unnecessary details. This ability to
identify task-specific high-interest regions within an image could be
beneficial to autonomous driving agents and machine learning systems in
general. To create a system capable of imitating human gaze patterns and visual
attention, we collect eye movement data from human drivers in a virtual reality
environment. We use this data to train deep neural networks predicting where
humans are most likely to look when driving. We then use the outputs of this
trained network to selectively mask driving images using a variety of masking
techniques. Finally, autonomous driving agents are trained using these masked
images as input. Upon comparison, we found that a dual-branch architecture
which processes both raw and attention-masked images substantially outperforms
all other models, reducing error in control signal predictions by 25.5\%
compared to a standard end-to-end model trained only on raw images. | [
"cs.CV",
"cs.RO"
]
|
Automatic captioning of images is a task that combines the challenges of
image analysis and text generation. One important aspect in captioning is the
notion of attention: How to decide what to describe and in which order.
Inspired by the successes in text analysis and translation, previous work have
proposed the \textit{transformer} architecture for image captioning. However,
the structure between the \textit{semantic units} in images (usually the
detected regions from object detection model) and sentences (each single word)
is different. Limited work has been done to adapt the transformer's internal
architecture to images. In this work, we introduce the \textbf{\textit{image
transformer}}, which consists of a modified encoding transformer and an
implicit decoding transformer, motivated by the relative spatial relationship
between image regions. Our design widen the original transformer layer's inner
architecture to adapt to the structure of images. With only regions feature as
inputs, our model achieves new state-of-the-art performance on both MSCOCO
offline and online testing benchmarks. | [
"cs.CV"
]
|
The exponential spread of COVID-19 in over 215 countries has led WHO to
recommend face masks and gloves for a safe return to school or work. We used
artificial intelligence and deep learning algorithms for automatic face masks
and gloves detection in public areas. We investigated and assessed the efficacy
of two popular deep learning algorithms of YOLO (You Only Look Once) and SSD
MobileNet for the detection and proper wearing of face masks and gloves trained
over a data set of 8250 images imported from the internet. YOLOv3 is
implemented using the DarkNet framework, and the SSD MobileNet algorithm is
applied for the development of accurate object detection. The proposed models
have been developed to provide accurate multi-class detection (Mask vs. No-Mask
vs. Gloves vs. No-Gloves vs. Improper). When people wear their masks
improperly, the method detects them as an improper class. The introduced models
provide accuracies of (90.6% for YOLO and 85.5% for SSD) for multi-class
detection. The systems' results indicate the efficiency and validity of
detecting people who do not wear masks and gloves in public. | [
"cs.CV",
"eess.IV"
]
|
Transformers have achieved great success in many artificial intelligence
fields, such as natural language processing, computer vision, and audio
processing. Therefore, it is natural to attract lots of interest from academic
and industry researchers. Up to the present, a great variety of Transformer
variants (a.k.a. X-formers) have been proposed, however, a systematic and
comprehensive literature review on these Transformer variants is still missing.
In this survey, we provide a comprehensive review of various X-formers. We
first briefly introduce the vanilla Transformer and then propose a new taxonomy
of X-formers. Next, we introduce the various X-formers from three perspectives:
architectural modification, pre-training, and applications. Finally, we outline
some potential directions for future research. | [
"cs.LG",
"cs.AI",
"cs.CL"
]
|
This paper proposes a client-server decision tree learning method for
outsourced private data. The privacy model is anatomization/fragmentation: the
server sees data values, but the link between sensitive and identifying
information is encrypted with a key known only to clients. Clients have limited
processing and storage capability. Both sensitive and identifying information
thus are stored on the server. The approach presented also retains most
processing at the server, and client-side processing is amortized over
predictions made by the clients. Experiments on various datasets show that the
method produces decision trees approaching the accuracy of a non-private
decision tree, while substantially reducing the client's computing resource
requirements. | [
"cs.LG",
"cs.CR",
"cs.DB",
"H.2.8; H.2.7"
]
|
We introduce a new, efficient, principled and backpropagation-compatible
algorithm for learning a probability distribution on the weights of a neural
network, called Bayes by Backprop. It regularises the weights by minimising a
compression cost, known as the variational free energy or the expected lower
bound on the marginal likelihood. We show that this principled kind of
regularisation yields comparable performance to dropout on MNIST
classification. We then demonstrate how the learnt uncertainty in the weights
can be used to improve generalisation in non-linear regression problems, and
how this weight uncertainty can be used to drive the exploration-exploitation
trade-off in reinforcement learning. | [
"stat.ML",
"cs.LG"
]
|
Automatic detection of colonic polyps is still an unsolved problem due to the
large variation of polyps in terms of shape, texture, size, and color, and the
existence of various polyp-like mimics during colonoscopy. In this study, we
apply a recent region based convolutional neural network (CNN) approach for the
automatic detection of polyps in images and videos obtained from colonoscopy
examinations. We use a deep-CNN model (Inception Resnet) as a transfer learning
scheme in the detection system. To overcome the polyp detection obstacles and
the small number of polyp images, we examine image augmentation strategies for
training deep networks. We further propose two efficient post-learning methods
such as, automatic false positive learning and off-line learning, both of which
can be incorporated with the region based detection system for reliable polyp
detection. Using the large size of colonoscopy databases, experimental results
demonstrate that the suggested detection systems show better performance
compared to other systems in the literature. Furthermore, we show improved
detection performance using the proposed post-learning schemes for colonoscopy
videos. | [
"cs.CV",
"cs.AI"
]
|
The first step toward Seed Phenotyping i.e. the comprehensive assessment of
complex seed traits such as growth, development, tolerance, resistance,
ecology, yield, and the measurement of pa-rameters that form more complex
traits is the identification of seed type. Generally, a plant re-searcher
inspects the visual attributes of a seed such as size, shape, area, color and
texture to identify the seed type, a process that is tedious and
labor-intensive. Advances in the areas of computer vision and deep learning
have led to the development of convolutional neural networks (CNN) that aid in
classification using images. While they classify efficiently, a key bottleneck
is the need for an extensive amount of labelled data to train the CNN before it
can be put to the task of classification. The work leverages the concepts of
Contrastive Learning and Domain Randomi-zation in order to achieve the same.
Briefly, domain randomization is the technique of applying models trained on
images containing simulated objects to real-world objects. The use of synthetic
images generated from a representational sample crop of real-world images
alleviates the need for a large volume of test subjects. As part of the work,
synthetic image datasets of five different types of seed images namely, canola,
rough rice, sorghum, soy and wheat are applied to three different
self-supervised learning frameworks namely, SimCLR, Momentum Contrast (MoCo)
and Build Your Own Latent (BYOL) where ResNet-50 is used as the backbone in
each of the networks. When the self-supervised models are fine-tuned with only
5% of the labels from the synthetic dataset, results show that MoCo, the model
that yields the best performance of the self-supervised learning frameworks in
question, achieves an accuracy of 77% on the test dataset which is only ~13%
less than the accuracy of 90% achieved by ResNet-50 trained on 100% of the
labels. | [
"cs.CV",
"cs.AI",
"cs.LG"
]
|
Few-shot learning is a problem of high interest in the evolution of deep
learning. In this work, we consider the problem of few-shot object detection
(FSOD) in a real-world, class-imbalanced scenario. For our experiments, we
utilize the India Driving Dataset (IDD), as it includes a class of
less-occurring road objects in the image dataset and hence provides a setup
suitable for few-shot learning. We evaluate both metric-learning and
meta-learning based FSOD methods, in two experimental settings: (i)
representative (same-domain) splits from IDD, that evaluates the ability of a
model to learn in the context of road images, and (ii) object classes with
less-occurring object samples, similar to the open-set setting in real-world.
From our experiments, we demonstrate that the metric-learning method
outperforms meta-learning on the novel classes by (i) 11.2 mAP points on the
same domain, and (ii) 1.0 mAP point on the open-set. We also show that our
extension of object classes in a real-world open dataset offers a rich ground
for few-shot learning studies. | [
"cs.CV"
]
|
A Generative Adversarial Network (GAN) with generator $G$ trained to model
the prior of images has been shown to perform better than sparsity-based
regularizers in ill-posed inverse problems. Here, we propose a new method of
deploying a GAN-based prior to solve linear inverse problems using projected
gradient descent (PGD). Our method learns a network-based projector for use in
the PGD algorithm, eliminating expensive computation of the Jacobian of $G$.
Experiments show that our approach provides a speed-up of $60\text{-}80\times$
over earlier GAN-based recovery methods along with better accuracy. Our main
theoretical result is that if the measurement matrix is moderately conditioned
on the manifold range($G$) and the projector is $\delta$-approximate, then the
algorithm is guaranteed to reach $O(\delta)$ reconstruction error in
$O(log(1/\delta))$ steps in the low noise regime. Additionally, we propose a
fast method to design such measurement matrices for a given $G$. Extensive
experiments demonstrate the efficacy of this method by requiring
$5\text{-}10\times$ fewer measurements than random Gaussian measurement
matrices for comparable recovery performance. Because the learning of the GAN
and projector is decoupled from the measurement operator, our GAN-based
projector and recovery algorithm are applicable without retraining to all
linear inverse problems, as confirmed by experiments on compressed sensing,
super-resolution, and inpainting. | [
"cs.LG",
"eess.IV",
"stat.ML"
]
|
With recent advances in RGB-D sensing technologies as well as improvements in
machine learning and fusion techniques, RGB-D facial recognition has become an
active area of research. A novel attention aware method is proposed to fuse two
image modalities, RGB and depth, for enhanced RGB-D facial recognition. The
proposed method first extracts features from both modalities using a
convolutional feature extractor. These features are then fused using a
two-layer attention mechanism. The first layer focuses on the fused feature
maps generated by the feature extractor, exploiting the relationship between
feature maps using LSTM recurrent learning. The second layer focuses on the
spatial features of those maps using convolution. The training database is
preprocessed and augmented through a set of geometric transformations, and the
learning process is further aided using transfer learning from a pure 2D RGB
image training process. Comparative evaluations demonstrate that the proposed
method outperforms other state-of-the-art approaches, including both
traditional and deep neural network-based methods, on the challenging
CurtinFaces and IIIT-D RGB-D benchmark databases, achieving classification
accuracies over 98.2% and 99.3% respectively. The proposed attention mechanism
is also compared with other attention mechanisms, demonstrating more accurate
results. | [
"cs.CV",
"cs.AI"
]
|
In recent years, kernel-based sparse coding (K-SRC) has received particular
attention due to its efficient representation of nonlinear data structures in
the feature space. Nevertheless, the existing K-SRC methods suffer from the
lack of consistency between their training and test optimization frameworks. In
this work, we propose a novel confident K-SRC and dictionary learning algorithm
(CKSC) which focuses on the discriminative reconstruction of the data based on
its representation in the kernel space. CKSC focuses on reconstructing each
data sample via weighted contributions which are confident in its corresponding
class of data. We employ novel discriminative terms to apply this scheme to
both training and test frameworks in our algorithm. This specific design
increases the consistency of these optimization frameworks and improves the
discriminative performance in the recall phase. In addition, CKSC directly
employs the supervised information in its dictionary learning framework to
enhance the discriminative structure of the dictionary. For empirical
evaluations, we implement our CKSC algorithm on multivariate time-series
benchmarks such as DynTex++ and UTKinect. Our claims regarding the superior
performance of the proposed algorithm are justified throughout comparing its
classification results to the state-of-the-art K-SRC algorithms. | [
"cs.LG",
"stat.ML"
]
|
Background and motivation: Deep Reinforcement Learning (Deep RL) is a rapidly
developing field. Historically most application has been made to games (such as
chess, Atari games, and go). Deep RL is now reaching the stage where it may
offer value in real world problems, including optimisation of healthcare
systems. One such problem is where to locate ambulances between calls in order
to minimise time from emergency call to ambulance on-scene. This is known as
the Ambulance Location problem.
Aim: To develop an OpenAI Gym-compatible framework and simulation environment
for testing Deep RL agents.
Methods: A custom ambulance dispatch simulation environment was developed
using OpenAI Gym and SimPy. Deep RL agents were built using PyTorch. The
environment is a simplification of the real world, but allows control over the
number of clusters of incident locations, number of possible dispatch
locations, number of hospitals, and creating incidents that occur at different
locations throughout each day.
Results: A range of Deep RL agents based on Deep Q networks were tested in
this custom environment. All reduced time to respond to emergency calls
compared with random allocation to dispatch points. Bagging Noisy Duelling Deep
Q networks gave the most consistence performance. All methods had a tendency to
lose performance if trained for too long, and so agents were saved at their
optimal performance (and tested on independent simulation runs).
Conclusions: Deep RL agents, developed using simulated environments, have the
potential to offer a novel approach to optimise the Ambulance Location problem.
Creating open simulation environments should allow more rapid progress in this
field. | [
"cs.LG"
]
|
Color distortion can introduce a significant damage in visual quality
perception, however, most of existing reduced-reference quality measures are
designed for grayscale images. In this paper, we consider a basic extension of
well-known image-statistics based quality assessment measures to color images.
In order to evaluate the impact of color information on the measures
efficiency, two color spaces are investigated: RGB and CIELAB. Results of an
extensive evaluation using TID 2013 benchmark demonstrates that significant
improvement can be achieved for a great number of distortion type when the
CIELAB color representation is used. | [
"cs.CV"
]
|
We propose a local-to-global representation learning algorithm for 3D point
cloud data, which is appropriate to handle various geometric transformations,
especially rotation, without explicit data augmentation with respect to the
transformations. Our model takes advantage of multi-level abstraction based on
graph convolutional neural networks, which constructs a descriptor hierarchy to
encode rotation-invariant shape information of an input object in a bottom-up
manner. The descriptors in each level are obtained from a neural network based
on a graph via stochastic sampling of 3D points, which is effective in making
the learned representations robust to the variations of input data. The
proposed algorithm presents the state-of-the-art performance on the
rotation-augmented 3D object recognition and segmentation benchmarks, and we
further analyze its characteristics through comprehensive ablative experiments. | [
"cs.CV"
]
|
Graph Neural Networks (GNNs) achieve an impressive performance on structured
graphs by recursively updating the representation vector of each node based on
its neighbors, during which parameterized transformation matrices should be
learned for the node feature updating. However, existing propagation schemes
are far from being optimal since they do not fully utilize the relational
information between nodes. We propose the information maximizing graph neural
networks (IGNN), which maximizes the mutual information between edge states and
transform parameters. We reformulate the mutual information as a differentiable
objective via a variational approach. We compare our model against several
recent variants of GNNs and show that our model achieves the state-of-the-art
performance on multiple tasks including quantum chemistry regression on QM9
dataset, generalization capability from QM9 to larger molecular graphs, and
prediction of molecular bioactivities relevant for drug discovery. The IGNN
model is based on an elegant and fundamental idea in information theory as
explained in the main text, and it could be easily generalized beyond the
contexts of molecular graphs considered in this work. To encourage more future
work in this area, all datasets and codes used in this paper will be released
for public access. | [
"cs.LG",
"stat.ML"
]
|
Previous work on adversarially robust neural networks for image
classification requires large training sets and computationally expensive
training procedures. On the other hand, few-shot learning methods are highly
vulnerable to adversarial examples. The goal of our work is to produce networks
which both perform well at few-shot classification tasks and are simultaneously
robust to adversarial examples. We develop an algorithm, called Adversarial
Querying (AQ), for producing adversarially robust meta-learners, and we
thoroughly investigate the causes for adversarial vulnerability. Moreover, our
method achieves far superior robust performance on few-shot image
classification tasks, such as Mini-ImageNet and CIFAR-FS, than robust transfer
learning. | [
"cs.LG",
"stat.ML"
]
|
Convolutional Neural Networks (CNNs) are now a well-established tool for
solving computational imaging problems. Modern CNN-based algorithms obtain
state-of-the-art performance in diverse image restoration problems.
Furthermore, it has been recently shown that, despite being highly
overparameterized, networks trained with a single corrupted image can still
perform as well as fully trained networks. We introduce a formal link between
such networks through their neural tangent kernel (NTK), and well-known
non-local filtering techniques, such as non-local means or BM3D. The filtering
function associated with a given network architecture can be obtained in closed
form without need to train the network, being fully characterized by the random
initialization of the network weights. While the NTK theory accurately predicts
the filter associated with networks trained using standard gradient descent,
our analysis shows that it falls short to explain the behaviour of networks
trained using the popular Adam optimizer. The latter achieves a larger change
of weights in hidden layers, adapting the non-local filtering function during
training. We evaluate our findings via extensive image denoising experiments. | [
"cs.CV",
"eess.IV",
"eess.SP",
"68T07"
]
|
More and more companies have deployed machine learning (ML) clusters, where
deep learning (DL) models are trained for providing various AI-driven services.
Efficient resource scheduling is essential for maximal utilization of expensive
DL clusters. Existing cluster schedulers either are agnostic to ML workload
characteristics, or use scheduling heuristics based on operators' understanding
of particular ML framework and workload, which are less efficient or not
general enough. In this paper, we show that DL techniques can be adopted to
design a generic and efficient scheduler. DL2 is a DL-driven scheduler for DL
clusters, targeting global training job expedition by dynamically resizing
resources allocated to jobs. DL2 advocates a joint supervised learning and
reinforcement learning approach: a neural network is warmed up via offline
supervised learning based on job traces produced by the existing cluster
scheduler; then the neural network is plugged into the live DL cluster,
fine-tuned by reinforcement learning carried out throughout the training
progress of the DL jobs, and used for deciding job resource allocation in an
online fashion. By applying past decisions made by the existing cluster
scheduler in the preparatory supervised learning phase, our approach enables a
smooth transition from existing scheduler, and renders a high-quality scheduler
in minimizing average training completion time. We implement DL2 on Kubernetes
and enable dynamic resource scaling in DL jobs on MXNet. Extensive evaluation
shows that DL2 outperforms fairness scheduler (i.e., DRF) by 44.1% and expert
heuristic scheduler (i.e., Optimus) by 17.5% in terms of average job completion
time. | [
"cs.LG",
"cs.DC",
"stat.ML"
]
|
Advances in object recognition flourished in part because of the availability
of high-quality datasets and associated benchmarks. However, these
benchmarks---such as ILSVRC---are relatively task-specific, focusing
predominately on predicting class labels. We introduce a publicly-available
dataset that embodies the task-general capabilities of human perception and
reasoning. The Human Similarity Judgments extension to ImageNet (ImageNet-HSJ)
is composed of human similarity judgments that supplement the ILSVRC validation
set. The new dataset supports a range of task and performance metrics,
including the evaluation of unsupervised learning algorithms. We demonstrate
two methods of assessment: using the similarity judgments directly and using a
psychological embedding trained on the similarity judgments. This embedding
space contains an order of magnitude more points (i.e., images) than previous
efforts based on human judgments. Scaling to the full 50,000 image set was made
possible through a selective sampling process that used variational Bayesian
inference and model ensembles to sample aspects of the embedding space that
were most uncertain. This methodological innovation not only enables scaling,
but should also improve the quality of solutions by focusing sampling where it
is needed. To demonstrate the utility of ImageNet-HSJ, we used the similarity
ratings and the embedding space to evaluate how well several popular models
conform to human similarity judgments. One finding is that more complex models
that perform better on task-specific benchmarks do not better conform to human
semantic judgments. In addition to the human similarity judgments, pre-trained
psychological embeddings and code for inferring variational embeddings are made
publicly available. Collectively, ImageNet-HSJ assets support the appraisal of
internal representations and the development of more human-like models. | [
"cs.CV",
"cs.LG"
]
|
Increasing volume of user-generated human-centric video content and their
applications, such as video retrieval and browsing, require compact
representations that are addressed by the video summarization literature.
Current supervised studies formulate video summarization as a
sequence-to-sequence learning problem and the existing solutions often neglect
the surge of human-centric view, which inherently contains affective content.
In this study, we investigate the affective-information enriched supervised
video summarization task for human-centric videos. First, we train a visual
input-driven state-of-the-art continuous emotion recognition model (CER-NET) on
the RECOLA dataset to estimate emotional attributes. Then, we integrate the
estimated emotional attributes and the high-level representations from the
CER-NET with the visual information to define the proposed affective video
summarization architectures (AVSUM). In addition, we investigate the use of
attention to improve the AVSUM architectures and propose two new architectures
based on temporal attention (TA-AVSUM) and spatial attention (SA-AVSUM). We
conduct video summarization experiments on the TvSum database. The proposed
AVSUM-GRU architecture with an early fusion of high level GRU embeddings and
the temporal attention based TA-AVSUM architecture attain competitive video
summarization performances by bringing strong performance improvements for the
human-centric videos compared to the state-of-the-art in terms of F-score and
self-defined face recall metrics. | [
"cs.CV",
"cs.HC"
]
|
This paper presents a new approach for assembling graph neural networks based
on framelet transforms. The latter provides a multi-scale representation for
graph-structured data. We decompose an input graph into low-pass and high-pass
frequencies coefficients for network training, which then defines a
framelet-based graph convolution. The framelet decomposition naturally induces
a graph pooling strategy by aggregating the graph feature into low-pass and
high-pass spectra, which considers both the feature values and geometry of the
graph data and conserves the total information. The graph neural networks with
the proposed framelet convolution and pooling achieve state-of-the-art
performance in many node and graph prediction tasks. Moreover, we propose
shrinkage as a new activation for the framelet convolution, which thresholds
high-frequency information at different scales. Compared to ReLU, shrinkage
activation improves model performance on denoising and signal compression:
noises in both node and structure can be significantly reduced by accurately
cutting off the high-pass coefficients from framelet decomposition, and the
signal can be compressed to less than half its original size with
well-preserved prediction performance. | [
"cs.LG",
"cs.AI",
"cs.NA",
"math.NA",
"68T07, 05C85, 42C40",
"I.2.4; I.2.6"
]
|
This paper presents the learned techniques during the Video Analysis Module
of the Master in Computer Vision from the Universitat Aut\`onoma de Barcelona,
used to solve the third track of the AI-City Challenge. This challenge aims to
track vehicles across multiple cameras placed in multiple intersections spread
out over a city. The methodology followed focuses first in solving
multi-tracking in a single camera and then extending it to multiple cameras.
The qualitative results of the implemented techniques are presented using
standard metrics for video analysis such as mAP for object detection and IDF1
for tracking. The source code is publicly available at:
https://github.com/mcv-m6-video/mcv-m6-2021-team4. | [
"cs.CV"
]
|
3D object detectors based only on LiDAR point clouds hold the
state-of-the-art on modern street-view benchmarks. However, LiDAR-based
detectors poorly generalize across domains due to domain shift. In the case of
LiDAR, in fact, domain shift is not only due to changes in the environment and
in the object appearances, as for visual data from RGB cameras, but is also
related to the geometry of the point clouds (e.g., point density variations).
This paper proposes SF-UDA$^{3D}$, the first Source-Free Unsupervised Domain
Adaptation (SF-UDA) framework to domain-adapt the state-of-the-art PointRCNN 3D
detector to target domains for which we have no annotations (unsupervised),
neither we hold images nor annotations of the source domain (source-free).
SF-UDA$^{3D}$ is novel on both aspects. Our approach is based on
pseudo-annotations, reversible scale-transformations and motion coherency.
SF-UDA$^{3D}$ outperforms both previous domain adaptation techniques based on
features alignment and state-of-the-art 3D object detection methods which
additionally use few-shot target annotations or target annotation statistics.
This is demonstrated by extensive experiments on two large-scale datasets,
i.e., KITTI and nuScenes. | [
"cs.CV"
]
|
This paper aims to provide a thorough study on the effectiveness of the
transformation-based ensemble defence for image classification and its reasons.
It has been empirically shown that they can enhance the robustness against
evasion attacks, while there is little analysis on the reasons. In particular,
it is not clear whether the robustness improvement is a result of
transformation or ensemble. In this paper, we design two adaptive attacks to
better evaluate the transformation-based ensemble defence. We conduct
experiments to show that 1) the transferability of adversarial examples exists
among the models trained on data records after different reversible
transformations; 2) the robustness gained through transformation-based ensemble
is limited; 3) this limited robustness is mainly from the irreversible
transformations rather than the ensemble of a number of models; and 4) blindly
increasing the number of sub-models in a transformation-based ensemble does not
bring extra robustness gain. | [
"cs.LG",
"cs.AI",
"cs.CR",
"stat.ML"
]
|
Adversarial training, a special case of multi-objective optimization, is an
increasingly prevalent machine learning technique: some of its most notable
applications include GAN-based generative modeling and self-play techniques in
reinforcement learning which have been applied to complex games such as Go or
Poker. In practice, a \emph{single} pair of networks is typically trained in
order to find an approximate equilibrium of a highly nonconcave-nonconvex
adversarial problem. However, while a classic result in game theory states such
an equilibrium exists in concave-convex games, there is no analogous guarantee
if the payoff is nonconcave-nonconvex. Our main contribution is to provide an
approximate minimax theorem for a large class of games where the players pick
neural networks including WGAN, StarCraft II, and Blotto Game. Our findings
rely on the fact that despite being nonconcave-nonconvex with respect to the
neural networks parameters, these games are concave-convex with respect to the
actual models (e.g., functions or distributions) represented by these neural
networks. | [
"stat.ML",
"cs.GT",
"cs.LG"
]
|
Computer-aided detection aims to improve breast cancer screening programs by
helping radiologists to evaluate digital mammography (DM) exams. DM exams are
generated by devices from different vendors, with diverse characteristics
between and even within vendors. Physical properties of these devices and
postprocessing of the images can greatly influence the resulting mammogram.
This results in the fact that a deep learning model trained on data from one
vendor cannot readily be applied to data from another vendor. This paper
investigates the use of tailored transfer learning methods based on adversarial
learning to tackle this problem. We consider a database of DM exams (mostly
bilateral and two views) generated by Hologic and Siemens vendors. We analyze
two transfer learning settings: 1) unsupervised transfer, where Hologic data
with soft lesion annotation at pixel level and Siemens unlabelled data are used
to annotate images in the latter data; 2) weak supervised transfer, where exam
level labels for images from the Siemens mammograph are available. We propose
tailored variants of recent state-of-the-art methods for transfer learning
which take into account the class imbalance and incorporate knowledge provided
by the annotations at exam level. Results of experiments indicate the
beneficial effect of transfer learning in both transfer settings. Notably, at
0.02 false positives per image, we achieve a sensitivity of 0.37, compared to
0.30 of a baseline with no transfer. Results indicate that using exam level
annotations gives an additional increase in sensitivity. | [
"cs.CV"
]
|
Recently there has been a dramatic increase in the performance of recognition
systems due to the introduction of deep architectures for representation
learning and classification. However, the mathematical reasons for this success
remain elusive. This tutorial will review recent work that aims to provide a
mathematical justification for several properties of deep networks, such as
global optimality, geometric stability, and invariance of the learned
representations. | [
"cs.LG",
"cs.CV"
]
|
Interference effects of tall buildings have attracted numerous studies due to
the boom of clusters of tall buildings in megacities. To fully understand the
interference effects of buildings, it often requires a substantial amount of
wind tunnel tests. Limited wind tunnel tests that only cover part of
interference scenarios are unable to fully reveal the interference effects.
This study used machine learning techniques to resolve the conflicting
requirement between limited wind tunnel tests that produce unreliable results
and a completed investigation of the interference effects that is costly and
time-consuming. Four machine learning models including decision tree, random
forest, XGBoost, generative adversarial networks (GANs), were trained based on
30% of a dataset to predict both mean and fluctuating pressure coefficients on
the principal building. The GANs model exhibited the best performance in
predicting these pressure coefficients. A number of GANs models were then
trained based on different portions of the dataset ranging from 10% to 90%. It
was found that the GANs model based on 30% of the dataset is capable of
predicting both mean and fluctuating pressure coefficients under unseen
interference conditions accurately. By using this GANs model, 70% of the wind
tunnel test cases can be saved, largely alleviating the cost of this kind of
wind tunnel testing study. | [
"cs.LG",
"eess.SP",
"stat.ML"
]
|
Techniques for making future predictions based upon the present and past
data, has always been an area with direct application to various real life
problems. We are discussing a similar problem in this paper. The problem
statement is provided by Kaggle, which also serves as an ongoing competition on
the Kaggle platform. In this project, we worked with a challenging time-series
dataset consisting of daily sales data, kindly provided by one of the largest
Russian software firms - 1C Company. The objective is to predict the total
sales for every product and store in the next month given the past data.
In order to perform forecasting for next month, we have deployed eXtreme
Gradient Boosting (XGBoost) and Long Short Term Memory (LSTM) based network
architecture to perform learning task. Root mean squared error (RMSE) between
the actual and predicted target values is used to evaluate the performance, and
make comparisons between the deployed algorithms. It has been found that
XGBoost fared better than LSTM over this dataset which can be attributed to its
relatively higher sparsity. | [
"cs.LG",
"stat.ML"
]
|
Major decisions from governments and other large organizations rely on
measurements of the populace's well-being, but making such measurements at a
broad scale is expensive and thus infrequent in much of the developing world.
We propose an inexpensive, scalable, and interpretable approach to predict key
livelihood indicators from public crowd-sourced street-level imagery. Such
imagery can be cheaply collected and more frequently updated compared to
traditional surveying methods, while containing plausibly relevant information
for a range of livelihood indicators. We propose two approaches to learn from
the street-level imagery: (1) a method that creates multi-household cluster
representations by detecting informative objects and (2) a graph-based approach
that captures the relationships between images. By visualizing what features
are important to a model and how they are used, we can help end-user
organizations understand the models and offer an alternate approach for index
estimation that uses cheaply obtained roadway features. By comparing our
results against ground data collected in nationally-representative household
surveys, we demonstrate the performance of our approach in accurately
predicting indicators of poverty, population, and health and its scalability by
testing in two different countries, India and Kenya. Our code is available at
https://github.com/sustainlab-group/mapillarygcn. | [
"cs.CV",
"I.2; I.4; K.4; E.1"
]
|
Model-based deep reinforcement learning has achieved success in various
domains that require high sample efficiencies, such as Go and robotics.
However, there are some remaining issues, such as planning efficient
explorations to learn more accurate dynamic models, evaluating the uncertainty
of the learned models, and more rational utilization of models. To mitigate
these issues, we present MEEE, a model-ensemble method that consists of
optimistic exploration and weighted exploitation. During exploration, unlike
prior methods directly selecting the optimal action that maximizes the expected
accumulative return, our agent first generates a set of action candidates and
then seeks out the optimal action that takes both expected return and future
observation novelty into account. During exploitation, different discounted
weights are assigned to imagined transition tuples according to their model
uncertainty respectively, which will prevent model predictive error propagation
in agent training. Experiments on several challenging continuous control
benchmark tasks demonstrated that our approach outperforms other model-free and
model-based state-of-the-art methods, especially in sample complexity. | [
"cs.LG",
"cs.AI"
]
|
Scale-invariance, good localization and robustness to noise and distortions
are the main properties that a local feature detector should possess. Most
existing local feature detectors find excessive unstable feature points that
increase the number of keypoints to be matched and the computational time of
the matching step. In this paper, we show that robust and accurate keypoints
exist in the specific scale-space domain. To this end, we first formulate the
superimposition problem into a mathematical model and then derive a closed-form
solution for multiscale analysis. The model is formulated via
difference-of-Gaussian (DoG) kernels in the continuous scale-space domain, and
it is proved that setting the scale-space pyramid's blurring ratio and
smoothness to 2 and 0.627, respectively, facilitates the detection of reliable
keypoints. For the applicability of the proposed model to discrete images, we
discretize it using the undecimated wavelet transform and the cubic spline
function. Theoretically, the complexity of our method is less than 5\% of that
of the popular baseline Scale Invariant Feature Transform (SIFT). Extensive
experimental results show the superiority of the proposed feature detector over
the existing representative hand-crafted and learning-based techniques in
accuracy and computational time. The code and supplementary materials can be
found at~{\url{https://github.com/mogvision/FFD}}. | [
"cs.CV"
]
|
Since the introduction of the GDPR and CCPA legislation, both public and
private facial image datasets are increasingly scrutinized. Several datasets
have been taken offline completely and some have been anonymized. However, it
is unclear how anonymization impacts face detection performance. To our
knowledge, this paper presents the first empirical study on the effect of image
anonymization on supervised training of face detectors. We compare conventional
face anonymizers with three state-of-the-art Generative Adversarial
Network-based (GAN) methods, by training an off-the-shelf face detector on
anonymized data. Our experiments investigate the suitability of anonymization
methods for maintaining face detector performance, the effect of detectors
overtraining on anonymization artefacts, dataset size for training an
anonymizer, and the effect of training time of anonymization GANs. A final
experiment investigates the correlation between common GAN evaluation metrics
and the performance of a trained face detector. Although all tested
anonymization methods lower the performance of trained face detectors, faces
anonymized using GANs cause far smaller performance degradation than
conventional methods. As the most important finding, the best-performing GAN,
DeepPrivacy, removes identifiable faces for a face detector trained on
anonymized data, resulting in a modest decrease from 91.0 to 88.3 mAP. In the
last few years, there have been rapid improvements in realism of GAN-generated
faces. We expect that further progression in GAN research will allow the use of
Deep Fake technology for privacy-preserving Safe Fakes, without any performance
degradation for training face detectors. | [
"cs.CV",
"I.5.4"
]
|
Recently, DETR and Deformable DETR have been proposed to eliminate the need
for many hand-designed components in object detection while demonstrating good
performance as previous complex hand-crafted detectors. However, their
performance on Video Object Detection (VOD) has not been well explored. In this
paper, we present TransVOD, an end-to-end video object detection model based on
a spatial-temporal Transformer architecture. The goal of this paper is to
streamline the pipeline of VOD, effectively removing the need for many
hand-crafted components for feature aggregation, e.g., optical flow, recurrent
neural networks, relation networks. Besides, benefited from the object query
design in DETR, our method does not need complicated post-processing methods
such as Seq-NMS or Tubelet rescoring, which keeps the pipeline simple and
clean. In particular, we present temporal Transformer to aggregate both the
spatial object queries and the feature memories of each frame. Our temporal
Transformer consists of three components: Temporal Deformable Transformer
Encoder (TDTE) to encode the multiple frame spatial details, Temporal Query
Encoder (TQE) to fuse object queries, and Temporal Deformable Transformer
Decoder to obtain current frame detection results. These designs boost the
strong baseline deformable DETR by a significant margin (3%-4% mAP) on the
ImageNet VID dataset. TransVOD yields comparable results performance on the
benchmark of ImageNet VID. We hope our TransVOD can provide a new perspective
for video object detection. Code will be made publicly available at
https://github.com/SJTU-LuHe/TransVOD. | [
"cs.CV"
]
|
Deep learning based object detection has achieved great success. However,
these supervised learning methods are data-hungry and time-consuming. This
restriction makes them unsuitable for limited data and urgent tasks, especially
in the applications of remote sensing. Inspired by the ability of humans to
quickly learn new visual concepts from very few examples, we propose a
training-free, one-shot geospatial object detection framework for remote
sensing images. It consists of (1) a feature extractor with remote sensing
domain knowledge, (2) a multi-level feature fusion method, (3) a novel
similarity metric method, and (4) a 2-stage object detection pipeline.
Experiments on sewage treatment plant and airport detections show that proposed
method has achieved a certain effect. Our method can serve as a baseline for
training-free, one-shot geospatial object detection. | [
"cs.CV"
]
|
One popular approach to interactively segment the foreground object of
interest from an image is to annotate a bounding box that covers the foreground
object. Then, a binary labeling is performed to achieve a refined segmentation.
One major issue of the existing algorithms for such interactive image
segmentation is their preference of an input bounding box that tightly encloses
the foreground object. This increases the annotation burden, and prevents these
algorithms from utilizing automatically detected bounding boxes. In this paper,
we develop a new LooseCut algorithm that can handle cases where the input
bounding box only loosely covers the foreground object. We propose a new Markov
Random Fields (MRF) model for segmentation with loosely bounded boxes,
including a global similarity constraint to better distinguish the foreground
and background, and an additional energy term to encourage consistent labeling
of similar-appearance pixels. This MRF model is then solved by an iterated
max-flow algorithm. In the experiments, we evaluate LooseCut in three
publicly-available image datasets, and compare its performance against several
state-of-the-art interactive image segmentation algorithms. We also show that
LooseCut can be used for enhancing the performance of unsupervised video
segmentation and image saliency detection. | [
"cs.CV"
]
|
Railway systems require regular manual maintenance, a large part of which is
dedicated to inspecting track deformation. Such deformation might severely
impact trains' runtime security, whereas such inspections remain costly for
both finance and human resources. Therefore, a more precise and efficient
approach to detect railway track deformation is in urgent need. In this paper,
we showcase an application framework for predicting vertical track
irregularity, based on a real-world, large-scale dataset produced by several
operating railways in China. We have conducted extensive experiments on various
machine learning & ensemble learning algorithms in an effort to maximize the
model's capability in capturing any irregularity. We also proposed a novel
approach for handling imbalanced data in multivariate time series prediction
tasks with adaptive data sampling and penalized loss. Such an approach has
proven to reduce models' sensitivity to the imbalanced target domain, thus
improving its performance in predicting rare extreme values. | [
"cs.LG"
]
|
Transformers are more and more popular in computer vision, which treat an
image as a sequence of patches and learn robust global features from the
sequence. However, a suitable vehicle re-identification method should consider
both robust global features and discriminative local features. In this paper,
we propose a graph interactive transformer (GiT) for vehicle re-identification.
On the whole, we stack multiple GiT blocks to build a competitive vehicle
re-identification model, in where each GiT block employs a novel local
correlation graph (LCG) module to extract discriminative local features within
patches and uses a transformer layer to extract robust global features among
patches. In detail, in the current GiT block, the LCG module learns local
features from local and global features resulting from the LCG module and
transformer layer of the previous GiT block. Similarly, the transformer layer
learns global features from the global features generated by the transformer
layer of the previous GiT block and the new local features outputted via the
LCG module of the current GiT block. Therefore, LCG modules and transformer
layers are in a coupled status, bringing effective cooperation between local
and global features. This is the first work to combine graphs and transformers
for vehicle re-identification to the best of our knowledge. Extensive
experiments on three large-scale vehicle re-identification datasets demonstrate
that our method is superior to state-of-the-art approaches. The code will be
available soon. | [
"cs.CV"
]
|
We present a novel approach to the detection and 3D pose estimation of
objects in color images. Its main contribution is that it does not require any
training phases nor data for new objects, while state-of-the-art methods
typically require hours of training time and hundreds of training registered
images. Instead, our method relies only on the objects' geometries. Our method
focuses on objects with prominent corners, which covers a large number of
industrial objects. We first learn to detect object corners of various shapes
in images and also to predict their 3D poses, by using training images of a
small set of objects. To detect a new object in a given image, we first
identify its corners from its CAD model; we also detect the corners visible in
the image and predict their 3D poses. We then introduce a RANSAC-like algorithm
that robustly and efficiently detects and estimates the object's 3D pose by
matching its corners on the CAD model with their detected counterparts in the
image. Because we also estimate the 3D poses of the corners in the image,
detecting only 1 or 2 corners is sufficient to estimate the pose of the object,
which makes the approach robust to occlusions. We finally rely on a final check
that exploits the full 3D geometry of the objects, in case multiple objects
have the same corner spatial arrangement. The advantages of our approach make
it particularly attractive for industrial contexts, and we demonstrate our
approach on the challenging T-LESS dataset. | [
"cs.CV"
]
|
Generative adversarial networks (GANs) are a hot research topic recently.
GANs have been widely studied since 2014, and a large number of algorithms have
been proposed. However, there is few comprehensive study explaining the
connections among different GANs variants, and how they have evolved. In this
paper, we attempt to provide a review on various GANs methods from the
perspectives of algorithms, theory, and applications. Firstly, the motivations,
mathematical representations, and structure of most GANs algorithms are
introduced in details. Furthermore, GANs have been combined with other machine
learning algorithms for specific applications, such as semi-supervised
learning, transfer learning, and reinforcement learning. This paper compares
the commonalities and differences of these GANs methods. Secondly, theoretical
issues related to GANs are investigated. Thirdly, typical applications of GANs
in image processing and computer vision, natural language processing, music,
speech and audio, medical field, and data science are illustrated. Finally, the
future open research problems for GANs are pointed out. | [
"cs.LG",
"stat.ML"
]
|
Most few-shot learning techniques are pre-trained on a large, labeled "base
dataset". In problem domains where such large labeled datasets are not
available for pre-training (e.g., X-ray, satellite images), one must resort to
pre-training in a different "source" problem domain (e.g., ImageNet), which can
be very different from the desired target task. Traditional few-shot and
transfer learning techniques fail in the presence of such extreme differences
between the source and target tasks. In this paper, we present a simple and
effective solution to tackle this extreme domain gap: self-training a source
domain representation on unlabeled data from the target domain. We show that
this improves one-shot performance on the target domain by 2.9 points on
average on the challenging BSCD-FSL benchmark consisting of datasets from
multiple domains. Our code is available at https://github.com/cpphoo/STARTUP. | [
"cs.CV",
"cs.AI",
"cs.LG"
]
|
We introduce a method to design a computationally efficient $G$-invariant
neural network that approximates functions invariant to the action of a given
permutation subgroup $G \leq S_n$ of the symmetric group on input data. The key
element of the proposed network architecture is a new $G$-invariant
transformation module, which produces a $G$-invariant latent representation of
the input data. This latent representation is then processed with a multi-layer
perceptron in the network. We prove the universality of the proposed
architecture, discuss its properties and highlight its computational and memory
efficiency. Theoretical considerations are supported by numerical experiments
involving different network configurations, which demonstrate the effectiveness
and strong generalization properties of the proposed method in comparison to
other $G$-invariant neural networks. | [
"cs.LG",
"cs.NE",
"stat.ML",
"I.2.6"
]
|
Traffic prediction is the cornerstone of an intelligent transportation
system. Accurate traffic forecasting is essential for the applications of smart
cities, i.e., intelligent traffic management and urban planning. Although
various methods are proposed for spatio-temporal modeling, they ignore the
dynamic characteristics of correlations among locations on road networks.
Meanwhile, most Recurrent Neural Network (RNN) based works are not efficient
enough due to their recurrent operations. Additionally, there is a severe lack
of fair comparison among different methods on the same datasets. To address the
above challenges, in this paper, we propose a novel traffic prediction
framework, named Dynamic Graph Convolutional Recurrent Network (DGCRN). In
DGCRN, hyper-networks are designed to leverage and extract dynamic
characteristics from node attributes, while the parameters of dynamic filters
are generated at each time step. We filter the node embeddings and then use
them to generate a dynamic graph, which is integrated with a pre-defined static
graph. As far as we know, we are the first to employ a generation method to
model fine topology of dynamic graph at each time step. Further, to enhance
efficiency and performance, we employ a training strategy for DGCRN by
restricting the iteration number of decoder during forward and backward
propagation. Finally, a reproducible standardized benchmark and a brand new
representative traffic dataset are opened for fair comparison and further
research. Extensive experiments on three datasets demonstrate that our model
outperforms 15 baselines consistently. | [
"cs.LG",
"cs.AI"
]
|
Recent research on super-resolution has achieved great success due to the
development of deep convolutional neural networks (DCNNs). However,
super-resolution of arbitrary scale factor has been ignored for a long time.
Most previous researchers regard super-resolution of different scale factors as
independent tasks. They train a specific model for each scale factor which is
inefficient in computing, and prior work only take the super-resolution of
several integer scale factors into consideration. In this work, we propose a
novel method called Meta-SR to firstly solve super-resolution of arbitrary
scale factor (including non-integer scale factors) with a single model. In our
Meta-SR, the Meta-Upscale Module is proposed to replace the traditional upscale
module. For arbitrary scale factor, the Meta-Upscale Module dynamically
predicts the weights of the upscale filters by taking the scale factor as input
and use these weights to generate the HR image of arbitrary size. For any
low-resolution image, our Meta-SR can continuously zoom in it with arbitrary
scale factor by only using a single model. We evaluated the proposed method
through extensive experiments on widely used benchmark datasets on single image
super-resolution. The experimental results show the superiority of our
Meta-Upscale. | [
"cs.CV"
]
|
In this paper, we suggest a framework to make use of mutual information as a
regularization criterion to train Auto-Encoders (AEs). In the proposed
framework, AEs are regularized by minimization of the mutual information
between input and encoding variables of AEs during the training phase. In order
to estimate the entropy of the encoding variables and the mutual information,
we propose a non-parametric method. We also give an information theoretic view
of Variational AEs (VAEs), which suggests that VAEs can be considered as
parametric methods that estimate entropy. Experimental results show that the
proposed non-parametric models have more degree of freedom in terms of
representation learning of features drawn from complex distributions such as
Mixture of Gaussians, compared to methods which estimate entropy using
parametric approaches, such as Variational AEs. | [
"cs.LG",
"cs.IT",
"math.IT",
"stat.ML"
]
|
We consider the problem of operator-valued kernel learning and investigate
the possibility of going beyond the well-known separable kernels. Borrowing
tools and concepts from the field of quantum computing, such as partial trace
and entanglement, we propose a new view on operator-valued kernels and define a
general family of kernels that encompasses previously known operator-valued
kernels, including separable and transformable kernels. Within this framework,
we introduce another novel class of operator-valued kernels called entangled
kernels that are not separable. We propose an efficient two-step algorithm for
this framework, where the entangled kernel is learned based on a novel
extension of kernel alignment to operator-valued kernels. We illustrate our
algorithm with an application to supervised dimensionality reduction, and
demonstrate its effectiveness with both artificial and real data for
multi-output regression. | [
"cs.LG",
"quant-ph",
"stat.ML"
]
|
Data augmentation is often used to enlarge datasets with synthetic samples
generated in accordance with the underlying data distribution. To enable a
wider range of augmentations, we explore negative data augmentation strategies
(NDA)that intentionally create out-of-distribution samples. We show that such
negative out-of-distribution samples provide information on the support of the
data distribution, and can be leveraged for generative modeling and
representation learning. We introduce a new GAN training objective where we use
NDA as an additional source of synthetic data for the discriminator. We prove
that under suitable conditions, optimizing the resulting objective still
recovers the true data distribution but can directly bias the generator towards
avoiding samples that lack the desired structure. Empirically, models trained
with our method achieve improved conditional/unconditional image generation
along with improved anomaly detection capabilities. Further, we incorporate the
same negative data augmentation strategy in a contrastive learning framework
for self-supervised representation learning on images and videos, achieving
improved performance on downstream image classification, object detection, and
action recognition tasks. These results suggest that prior knowledge on what
does not constitute valid data is an effective form of weak supervision across
a range of unsupervised learning tasks. | [
"cs.CV",
"cs.AI"
]
|
Due to the advantage of achieving a better performance under weak
regularization, elastic net has attracted wide attention in statistics, machine
learning, bioinformatics, and other fields. In particular, a variation of the
elastic net, adaptive elastic net (AEN), integrates the adaptive grouping
effect. In this paper, we aim to develop a new algorithm: Adaptive Elastic Net
with Conditional Mutual Information (AEN-CMI) that further improves AEN by
incorporating conditional mutual information into the gene selection process.
We apply this new algorithm to screen significant genes for two kinds of
cancers: colon cancer and leukemia. Compared with other algorithms including
Support Vector Machine, Classic Elastic Net and Adaptive Elastic Net, the
proposed algorithm, AEN-CMI, obtains the best classification performance using
the least number of genes. | [
"stat.ML",
"cs.IT",
"cs.LG",
"math.IT"
]
|
Understanding the implication of point cloud is still challenging to achieve
the goal of classification or segmentation due to the irregular and sparse
structure of point cloud. As we have known, PointNet architecture as a
ground-breaking work for point cloud which can learn efficiently shape features
directly on unordered 3D point cloud and have achieved favorable performance.
However, this model fail to consider the fine-grained semantic information of
local structure for point cloud. Afterwards, many valuable works are proposed
to enhance the performance of PointNet by means of semantic features of local
patch for point cloud. In this paper, a multi-scale receptive fields graph
attention network (named after MRFGAT) for point cloud classification is
proposed. By focusing on the local fine features of point cloud and applying
multi attention modules based on channel affinity, the learned feature map for
our network can well capture the abundant features information of point cloud.
The proposed MRFGAT architecture is tested on ModelNet10 and ModelNet40
datasets, and results show it achieves state-of-the-art performance in shape
classification tasks. | [
"cs.CV"
]
|
Over the last decades, most approaches proposed for handwritten digit string
recognition (HDSR) have resorted to digit segmentation, which is dominated by
heuristics, thereby imposing substantial constraints on the final performance.
Few of them have been based on segmentation-free strategies where each pixel
column has a potential cut location. Recently, segmentation-free strategies has
added another perspective to the problem, leading to promising results.
However, these strategies still show some limitations when dealing with a large
number of touching digits. To bridge the resulting gap, in this paper, we
hypothesize that a string of digits can be approached as a sequence of objects.
We thus evaluate different end-to-end approaches to solve the HDSR problem,
particularly in two verticals: those based on object-detection (e.g., Yolo and
RetinaNet) and those based on sequence-to-sequence representation (CRNN). The
main contribution of this work lies in its provision of a comprehensive
comparison with a critical analysis of the above mentioned strategies on five
benchmarks commonly used to assess HDSR, including the challenging Touching
Pair dataset, NIST SD19, and two real-world datasets (CAR and CVL) proposed for
the ICFHR 2014 competition on HDSR. Our results show that the Yolo model
compares favorably against segmentation-free models with the advantage of
having a shorter pipeline that minimizes the presence of heuristics-based
models. It achieved a 97%, 96%, and 84% recognition rate on the NIST-SD19, CAR,
and CVL datasets, respectively. | [
"cs.CV"
]
|
A recent trend to recognize facial expressions in the real-world scenario is
to deploy attention based convolutional neural networks (CNNs) locally to
signify the importance of facial regions and, combine it with global facial
features and/or other complementary context information for performance gain.
However, in the presence of occlusions and pose variations, different channels
respond differently, and further that the response intensity of a channel
differ across spatial locations. Also, modern facial expression
recognition(FER) architectures rely on external sources like landmark detectors
for defining attention. Failure of landmark detector will have a cascading
effect on FER. Additionally, there is no emphasis laid on the relevance of
features that are input to compute complementary context information.
Leveraging on the aforementioned observations, an end-to-end architecture for
FER is proposed in this work that obtains both local and global attention per
channel per spatial location through a novel spatio-channel attention net
(SCAN), without seeking any information from the landmark detectors. SCAN is
complemented by a complementary context information (CCI) branch. Further,
using efficient channel attention (ECA), the relevance of features input to CCI
is also attended to. The representation learnt by the proposed architecture is
robust to occlusions and pose variations. Robustness and superior performance
of the proposed model is demonstrated on both in-lab and in-the-wild datasets
(AffectNet, FERPlus, RAF-DB, FED-RO, SFEW, CK+, Oulu-CASIA and JAFFE) along
with a couple of constructed face mask datasets resembling masked faces in
COVID-19 scenario. Codes are publicly available at
https://github.com/1980x/SCAN-CCI-FER | [
"cs.CV"
]
|
Single-site Markov Chain Monte Carlo (MCMC) is a variant of MCMC in which a
single coordinate in the state space is modified in each step. Structured
relational models are a good candidate for this style of inference. In the
single-site context, second order methods become feasible because the typical
cubic costs associated with these methods is now restricted to the dimension of
each coordinate. Our work, which we call Newtonian Monte Carlo (NMC), is a
method to improve MCMC convergence by analyzing the first and second order
gradients of the target density to determine a suitable proposal density at
each point. Existing first order gradient-based methods suffer from the problem
of determining an appropriate step size. Too small a step size and it will take
a large number of steps to converge, while a very large step size will cause it
to overshoot the high density region. NMC is similar to the Newton-Raphson
update in optimization where the second order gradient is used to automatically
scale the step size in each dimension. However, our objective is to find a
parameterized proposal density rather than the maxima.
As a further improvement on existing first and second order methods, we show
that random variables with constrained supports don't need to be transformed
before taking a gradient step. We demonstrate the efficiency of NMC on a number
of different domains. For statistical models where the prior is conjugate to
the likelihood, our method recovers the posterior quite trivially in one step.
However, we also show results on fairly large non-conjugate models, where NMC
performs better than adaptive first order methods such as NUTS or other inexact
scalable inference methods such as Stochastic Variational Inference or
bootstrapping. | [
"cs.LG",
"stat.ML"
]
|
Point clouds are a basic data type that is increasingly of interest as 3D
content becomes more ubiquitous. Applications using point clouds include
virtual, augmented, and mixed reality and autonomous driving. We propose a more
efficient deep learning-based encoder architecture for point clouds compression
that incorporates principles from established 3D object detection and image
compression architectures. Through an ablation study, we show that
incorporating the learned activation function from Computational Efficient
Neural Image Compression (CENIC) and designing more parameter-efficient
convolutional blocks yields dramatic gains in efficiency and performance. Our
proposed architecture incorporates Generalized Divisive Normalization
activations and propose a spatially separable InceptionV4-inspired block. We
then evaluate rate-distortion curves on the standard JPEG Pleno 8i Voxelized
Full Bodies dataset to evaluate our model's performance. Our proposed
modifications outperform the baseline approaches by a small margin in terms of
Bjontegard delta rate and PSNR values, yet reduces necessary encoder
convolution operations by 8 percent and reduces total encoder parameters by 20
percent. Our proposed architecture, when considered on its own, has a small
penalty of 0.02 percent in Chamfer's Distance and 0.32 percent increased bit
rate in Point to Plane Distance for the same peak signal-to-noise ratio. | [
"cs.CV",
"cs.GR",
"cs.LG",
"eess.IV"
]
|
AI and Machine Learning can offer powerful tools to help in the fight against
Covid-19. In this paper we present a study and a concrete tool based on machine
learning to predict the prognosis of hospitalised patients with Covid-19. In
particular we address the task of predicting the risk of death of a patient at
different times of the hospitalisation, on the base of some demographic
information, chest X-ray scores and several laboratory findings. Our machine
learning models use ensembles of decision trees trained and tested using data
from more than 2000 patients. An experimental evaluation of the models shows
good performance in solving the addressed task. | [
"cs.LG"
]
|
Transferring knowledges learned from multiple source domains to target domain
is a more practical and challenging task than conventional single-source domain
adaptation. Furthermore, the increase of modalities brings more difficulty in
aligning feature distributions among multiple domains. To mitigate these
problems, we propose a Learning to Combine for Multi-Source Domain Adaptation
(LtC-MSDA) framework via exploring interactions among domains. In the nutshell,
a knowledge graph is constructed on the prototypes of various domains to
realize the information propagation among semantically adjacent
representations. On such basis, a graph model is learned to predict query
samples under the guidance of correlated prototypes. In addition, we design a
Relation Alignment Loss (RAL) to facilitate the consistency of categories'
relational interdependency and the compactness of features, which boosts
features' intra-class invariance and inter-class separability. Comprehensive
results on public benchmark datasets demonstrate that our approach outperforms
existing methods with a remarkable margin. Our code is available at
\url{https://github.com/ChrisAllenMing/LtC-MSDA} | [
"cs.CV"
]
|
It's useful to automatically transform an image from its original form to
some synthetic form (style, partial contents, etc.), while keeping the original
structure or semantics. We define this requirement as the "image-to-image
translation" problem, and propose a general approach to achieve it, based on
deep convolutional and conditional generative adversarial networks (GANs),
which has gained a phenomenal success to learn mapping images from noise input
since 2014. In this work, we develop a two step (unsupervised) learning method
to translate images between different domains by using unlabeled images without
specifying any correspondence between them, so that to avoid the cost of
acquiring labeled data. Compared with prior works, we demonstrated the capacity
of generality in our model, by which variance of translations can be conduct by
a single type of model. Such capability is desirable in applications like
bidirectional translation | [
"cs.CV",
"cs.LG"
]
|
Research on distributed machine learning algorithms has focused primarily on
one of two extremes - algorithms that obey strict concurrency constraints or
algorithms that obey few or no such constraints. We consider an intermediate
alternative in which algorithms optimistically assume that conflicts are
unlikely and if conflicts do arise a conflict-resolution protocol is invoked.
We view this "optimistic concurrency control" paradigm as particularly
appropriate for large-scale machine learning algorithms, particularly in the
unsupervised setting. We demonstrate our approach in three problem areas:
clustering, feature learning and online facility location. We evaluate our
methods via large-scale experiments in a cluster computing environment. | [
"cs.LG",
"cs.AI",
"cs.DC"
]
|
In real-world practice, medical images acquired in different phases possess
complementary information, {\em e.g.}, radiologists often refer to both
arterial and venous scans in order to make the diagnosis. However, in medical
image analysis, fusing prediction from two phases is often difficult, because
(i) there is a domain gap between two phases, and (ii) the semantic labels are
not pixel-wise corresponded even for images scanned from the same patient. This
paper studies organ segmentation in two-phase CT scans. We propose Phase
Collaborative Network (PCN), an end-to-end framework that contains both
generative and discriminative modules. PCN can be mathematically explained to
formulate phase-to-phase and data-to-label relations jointly. Experiments are
performed on a two-phase CT dataset, on which PCN outperforms the baselines
working with one-phase data by a large margin, and we empirically verify that
the gain comes from inter-phase collaboration. Besides, PCN transfers well to
two public single-phase datasets, demonstrating its potential applications. | [
"cs.CV"
]
|
Speech-driven facial video generation has been a complex problem due to its
multi-modal aspects namely audio and video domain. The audio comprises lots of
underlying features such as expression, pitch, loudness, prosody(speaking
style) and facial video has lots of variability in terms of head movement, eye
blinks, lip synchronization and movements of various facial action units along
with temporal smoothness. Synthesizing highly expressive facial videos from the
audio input and static image is still a challenging task for generative
adversarial networks. In this paper, we propose a multi-modal adaptive
normalization(MAN) based architecture to synthesize a talking person video of
arbitrary length using as input: an audio signal and a single image of a
person. The architecture uses the multi-modal adaptive normalization, keypoint
heatmap predictor, optical flow predictor and class activation map[58] based
layers to learn movements of expressive facial components and hence generates a
highly expressive talking-head video of the given person. The multi-modal
adaptive normalization uses the various features of audio and video such as Mel
spectrogram, pitch, energy from audio signals and predicted keypoint
heatmap/optical flow and a single image to learn the respective affine
parameters to generate highly expressive video. Experimental evaluation
demonstrates superior performance of the proposed method as compared to
Realistic Speech-Driven Facial Animation with GANs(RSDGAN) [53], Speech2Vid
[10], and other approaches, on multiple quantitative metrics including: SSIM
(structural similarity index), PSNR (peak signal to noise ratio), CPBD (image
sharpness), WER(word error rate), blinks/sec and LMD(landmark distance).
Further, qualitative evaluation and Online Turing tests demonstrate the
efficacy of our approach. | [
"cs.CV"
]
|
Today's cloud service architectures follow a "one size fits all" deployment
strategy where the same service version instantiation is provided to the end
users. However, consumers are broad and different applications have different
accuracy and responsiveness requirements, which as we demonstrate renders the
"one size fits all" approach inefficient in practice. We use a production-grade
speech recognition engine, which serves several thousands of users, and an open
source computer vision based system, to explain our point. To overcome the
limitations of the "one size fits all" approach, we recommend Tolerance Tiers
where each MLaaS tier exposes an accuracy/responsiveness characteristic, and
consumers can programmatically select a tier. We evaluate our proposal on the
CPU-based automatic speech recognition (ASR) engine and cutting-edge neural
networks for image classification deployed on both CPUs and GPUs. The results
show that our proposed approach provides an MLaaS cloud service architecture
that can be tuned by the end API user or consumer to outperform the
conventional "one size fits all" approach. | [
"cs.LG",
"cs.CV",
"cs.PF"
]
|
This work employs a pre-trained, multi-view Convolutional Neural Network
(CNN) with a spatial attention block to optimise knee injury detection. An
open-source Magnetic Resonance Imaging (MRI) data set with image-level labels
was leveraged for this analysis. As MRI data is acquired from three planes, we
compare our technique using data from a single-plane and multiple planes
(multi-plane). For multi-plane, we investigate various methods of fusing the
planes in the network. This analysis resulted in the novel 'MPFuseNet' network
and state-of-the-art Area Under the Curve (AUC) scores for detecting Anterior
Cruciate Ligament (ACL) tears and Abnormal MRIs, achieving AUC scores of 0.977
and 0.957 respectively. We then developed an objective metric, Penalised
Localisation Accuracy (PLA), to validate the model's localisation ability. This
metric compares binary masks generated from Grad-Cam output and the
radiologist's annotations on a sample of MRIs. We also extracted explainability
features in a model-agnostic approach that were then verified as clinically
relevant by the radiologist. | [
"cs.CV",
"cs.LG"
]
|
Active screening is a common approach in controlling the spread of recurring
infectious diseases such as tuberculosis and influenza. In this approach,
health workers periodically select a subset of population for screening.
However, given the limited number of health workers, only a small subset of the
population can be visited in any given time period. Given the recurrent nature
of the disease and rapid spreading, the goal is to minimize the number of
infections over a long time horizon. Active screening can be formalized as a
sequential combinatorial optimization over the network of people and their
connections. The main computational challenges in this formalization arise from
i) the combinatorial nature of the problem, ii) the need of sequential planning
and iii) the uncertainties in the infectiousness states of the population.
Previous works on active screening fail to scale to large time horizon while
fully considering the future effect of current interventions. In this paper, we
propose a novel reinforcement learning (RL) approach based on Deep Q-Networks
(DQN), with several innovative adaptations that are designed to address the
above challenges. First, we use graph convolutional networks (GCNs) to
represent the Q-function that exploit the node correlations of the underlying
contact network. Second, to avoid solving a combinatorial optimization problem
in each time period, we decompose the node set selection as a sub-sequence of
decisions, and further design a two-level RL framework that solves the problem
in a hierarchical way. Finally, to speed-up the slow convergence of RL which
arises from reward sparseness, we incorporate ideas from curriculum learning
into our hierarchical RL approach. We evaluate our RL algorithm on several
real-world networks. | [
"cs.LG",
"cs.MA"
]
|
An automatic elastic registration method suited for vascularized organs is
proposed. The vasculature in both the preoperative and intra-operative images
is represented as a graph. A typical application of this method is the fusion
of pre-operative information onto the organ during surgery, to compensate for
the limited details provided by the intra-operative imaging modality (e.g.
CBCT) and to cope with changes in the shape of the organ. Due to image
modalities differences and organ deformation, each graph has a different
topology and shape. The Adaptive Compliance Graph Matching (ACGM) method
presented does not require any manual initialization, handles intra-operative
nonrigid deformations of up to 65 mm and computes a complete displacement field
over the organ from only the matched vasculature. ACGM is better than the
previous Biomechanical Graph Matching method 3 (BGM) because it uses an
efficient biomechanical vascularized liver model to compute the organ's
transformation and the vessels bifurcations compliance. This allows to
efficiently find the best graph matches with a novel compliance-based adaptive
search. These contributions are evaluated on ten realistic synthetic and two
real porcine automatically segmented datasets. ACGM obtains better target
registration error (TRE) than BGM, with an average TRE in the real datasets of
4.2 mm compared to 6.5 mm, respectively. It also is up to one order of
magnitude faster, less dependent on the parameters used and more robust to
noise. | [
"cs.CV"
]
|
As Artificial Intelligence as a Service gains popularity, protecting
well-trained models as intellectual property is becoming increasingly
important. Generally speaking, there are two common protection methods:
ownership verification and usage authorization. In this paper, we propose
Non-Transferable Learning (NTL), a novel approach that captures the exclusive
data representation in the learned model and restricts the model generalization
ability to certain domains. This approach provides effective solutions to both
model verification and authorization. For ownership verification, watermarking
techniques are commonly used but are often vulnerable to sophisticated
watermark removal methods. Our NTL-based model verification approach instead
provides robust resistance to state-of-the-art watermark removal methods, as
shown in extensive experiments for four of such methods over the digits,
CIFAR10 & STL10, and VisDA datasets. For usage authorization, prior solutions
focus on authorizing specific users to use the model, but authorized users can
still apply the model to any data without restriction. Our NTL-based
authorization approach instead provides data-centric usage protection by
significantly degrading the performance of usage on unauthorized data. Its
effectiveness is also shown through experiments on a variety of datasets. | [
"cs.LG",
"stat.ML"
]
|
Ubiquitous information access becomes more and more important nowadays and
research is aimed at making it adapted to users. Our work consists in applying
machine learning techniques in order to bring a solution to some of the
problems concerning the acceptance of the system by users. To achieve this, we
propose a fundamental shift in terms of how we model the learning of
recommender system: inspired by models of human reasoning developed in robotic,
we combine reinforcement learning and case-base reasoning to define a
recommendation process that uses these two approaches for generating
recommendations on different context dimensions (social, temporal, geographic).
We describe an implementation of the recommender system based on this
framework. We also present preliminary results from experiments with the system
and show how our approach increases the recommendation quality. | [
"cs.LG",
"cs.IR",
"I.2"
]
|
Classification is widely used technique in the data mining domain, where
scalability and efficiency are the immediate problems in classification
algorithms for large databases. We suggest improvements to the existing C4.5
decision tree algorithm. In this paper attribute oriented induction (AOI) and
relevance analysis are incorporated with concept hierarchys knowledge and
HeightBalancePriority algorithm for construction of decision tree along with
Multi level mining. The assignment of priorities to attributes is done by
evaluating information entropy, at different levels of abstraction for building
decision tree using HeightBalancePriority algorithm. Modified DMQL queries are
used to understand and explore the shortcomings of the decision trees generated
by C4.5 classifier for education dataset and the results are compared with the
proposed approach. | [
"cs.LG"
]
|
Location is key to spatialize internet-of-things (IoT) data. However, it is
challenging to use low-cost IoT devices for robust unsupervised localization
(i.e., localization without training data that have known location labels).
Thus, this paper proposes a deep reinforcement learning (DRL) based
unsupervised wireless-localization method. The main contributions are as
follows. (1) This paper proposes an approach to model a continuous
wireless-localization process as a Markov decision process (MDP) and process it
within a DRL framework. (2) To alleviate the challenge of obtaining rewards
when using unlabeled data (e.g., daily-life crowdsourced data), this paper
presents a reward-setting mechanism, which extracts robust landmark data from
unlabeled wireless received signal strengths (RSS). (3) To ease requirements
for model re-training when using DRL for localization, this paper uses RSS
measurements together with agent location to construct DRL inputs. The proposed
method was tested by using field testing data from multiple Bluetooth 5 smart
ear tags in a pasture. Meanwhile, the experimental verification process
reflected the advantages and challenges for using DRL in wireless localization. | [
"cs.LG",
"eess.SP",
"stat.ML"
]
|
In this paper, we present a novel unsupervised feature learning architecture,
which consists of a multi-clustering integration module and a variant of RBM
termed multi-clustering integration RBM (MIRBM). In the multi-clustering
integration module, we apply three unsupervised K-means, affinity propagation
and spectral clustering algorithms to obtain three different clustering
partitions (CPs) without any background knowledge or label. Then, an unanimous
voting strategy is used to generate a local clustering partition (LCP). The
novel MIRBM model is a core feature encoding part of the proposed unsupervised
feature learning architecture. The novelty of it is that the LCP as an
unsupervised guidance is integrated into one step contrastive divergence (CD1)
learning to guide the distribution of the hidden layer features. For the
instance in the same LCP cluster, the hidden and reconstructed hidden layer
features of the MIRBM model in the proposed architecture tend to constrict
together in the training process. Meanwhile, each LCP center tends to disperse
from each other as much as possible in the hidden and reconstructed hidden
layer during training. The experiments demonstrate that the proposed
unsupervised feature learning architecture has more powerful feature
representation and generalization capability than the state-of-the-art graph
regularized RBM (GraphRBM) for clustering tasks in the Microsoft Research Asia
Multimedia (MSRA-MM)2.0 dataset. | [
"cs.LG",
"stat.ML"
]
|
With state-of-the-art sensing and photogrammetric techniques, Microsoft Bing
Maps team has created over 125 highly detailed 3D cities from 11 different
countries that cover hundreds of thousands of square kilometer areas. The 3D
city models were created using the photogrammetric technique with
high-resolution images that were captured from aircraft-mounted cameras. Such a
large 3D city database has caught the attention of the US Army for creating
virtual simulation environments to support military operations. However, the 3D
city models do not have semantic information such as buildings, vegetation, and
ground and cannot allow sophisticated user-level and system-level interaction.
At I/ITSEC 2019, the authors presented a fully automated data segmentation and
object information extraction framework for creating simulation terrain using
UAV-based photogrammetric data. This paper discusses the next steps in
extending our designed data segmentation framework for segmenting 3D city data.
In this study, the authors first investigated the strengths and limitations of
the existing framework when applied to the Bing data. The main differences
between UAV-based and aircraft-based photogrammetric data are highlighted. The
data quality issues in the aircraft-based photogrammetric data, which can
negatively affect the segmentation performance, are identified. Based on the
findings, a workflow was designed specifically for segmenting Bing data while
considering its characteristics. In addition, since the ultimate goal is to
combine the use of both small unmanned aerial vehicle (UAV) collected data and
the Bing data in a virtual simulation environment, data from these two sources
needed to be aligned and registered together. To this end, the authors also
proposed a data registration workflow that utilized the traditional iterative
closest point (ICP) with the extracted semantic information. | [
"cs.CV"
]
|
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