28,629 research outputs found
Domain Adaptation for Semantic Segmentation via Class-Balanced Self-Training
Recent deep networks achieved state of the art performance on a variety of
semantic segmentation tasks. Despite such progress, these models often face
challenges in real world `wild tasks' where large difference between labeled
training/source data and unseen test/target data exists. In particular, such
difference is often referred to as `domain gap', and could cause significantly
decreased performance which cannot be easily remedied by further increasing the
representation power. Unsupervised domain adaptation (UDA) seeks to overcome
such problem without target domain labels. In this paper, we propose a novel
UDA framework based on an iterative self-training procedure, where the problem
is formulated as latent variable loss minimization, and can be solved by
alternatively generating pseudo labels on target data and re-training the model
with these labels. On top of self-training, we also propose a novel
class-balanced self-training framework to avoid the gradual dominance of large
classes on pseudo-label generation, and introduce spatial priors to refine
generated labels. Comprehensive experiments show that the proposed methods
achieve state of the art semantic segmentation performance under multiple major
UDA settings.Comment: Accepted to ECCV 201
Feature Selection: A Data Perspective
Feature selection, as a data preprocessing strategy, has been proven to be
effective and efficient in preparing data (especially high-dimensional data)
for various data mining and machine learning problems. The objectives of
feature selection include: building simpler and more comprehensible models,
improving data mining performance, and preparing clean, understandable data.
The recent proliferation of big data has presented some substantial challenges
and opportunities to feature selection. In this survey, we provide a
comprehensive and structured overview of recent advances in feature selection
research. Motivated by current challenges and opportunities in the era of big
data, we revisit feature selection research from a data perspective and review
representative feature selection algorithms for conventional data, structured
data, heterogeneous data and streaming data. Methodologically, to emphasize the
differences and similarities of most existing feature selection algorithms for
conventional data, we categorize them into four main groups: similarity based,
information theoretical based, sparse learning based and statistical based
methods. To facilitate and promote the research in this community, we also
present an open-source feature selection repository that consists of most of
the popular feature selection algorithms
(\url{http://featureselection.asu.edu/}). Also, we use it as an example to show
how to evaluate feature selection algorithms. At the end of the survey, we
present a discussion about some open problems and challenges that require more
attention in future research
Multiple Object Tracking: A Literature Review
Multiple Object Tracking (MOT) is an important computer vision problem which
has gained increasing attention due to its academic and commercial potential.
Although different kinds of approaches have been proposed to tackle this
problem, it still remains challenging due to factors like abrupt appearance
changes and severe object occlusions. In this work, we contribute the first
comprehensive and most recent review on this problem. We inspect the recent
advances in various aspects and propose some interesting directions for future
research. To the best of our knowledge, there has not been any extensive review
on this topic in the community. We endeavor to provide a thorough review on the
development of this problem in recent decades. The main contributions of this
review are fourfold: 1) Key aspects in a multiple object tracking system,
including formulation, categorization, key principles, evaluation of an MOT are
discussed. 2) Instead of enumerating individual works, we discuss existing
approaches according to various aspects, in each of which methods are divided
into different groups and each group is discussed in detail for the principles,
advances and drawbacks. 3) We examine experiments of existing publications and
summarize results on popular datasets to provide quantitative comparisons. We
also point to some interesting discoveries by analyzing these results. 4) We
provide a discussion about issues of MOT research, as well as some interesting
directions which could possibly become potential research effort in the future
Controllable Invariance through Adversarial Feature Learning
Learning meaningful representations that maintain the content necessary for a
particular task while filtering away detrimental variations is a problem of
great interest in machine learning. In this paper, we tackle the problem of
learning representations invariant to a specific factor or trait of data. The
representation learning process is formulated as an adversarial minimax game.
We analyze the optimal equilibrium of such a game and find that it amounts to
maximizing the uncertainty of inferring the detrimental factor given the
representation while maximizing the certainty of making task-specific
predictions. On three benchmark tasks, namely fair and bias-free
classification, language-independent generation, and lighting-independent image
classification, we show that the proposed framework induces an invariant
representation, and leads to better generalization evidenced by the improved
performance.Comment: NIPS 201
Transfer Adaptation Learning: A Decade Survey
The world we see is ever-changing and it always changes with people, things,
and the environment. Domain is referred to as the state of the world at a
certain moment. A research problem is characterized as transfer adaptation
learning (TAL) when it needs knowledge correspondence between different
moments/domains. Conventional machine learning aims to find a model with the
minimum expected risk on test data by minimizing the regularized empirical risk
on the training data, which, however, supposes that the training and test data
share similar joint probability distribution. TAL aims to build models that can
perform tasks of target domain by learning knowledge from a semantic related
but distribution different source domain. It is an energetic research filed of
increasing influence and importance, which is presenting a blowout publication
trend. This paper surveys the advances of TAL methodologies in the past decade,
and the technical challenges and essential problems of TAL have been observed
and discussed with deep insights and new perspectives. Broader solutions of
transfer adaptation learning being created by researchers are identified, i.e.,
instance re-weighting adaptation, feature adaptation, classifier adaptation,
deep network adaptation and adversarial adaptation, which are beyond the early
semi-supervised and unsupervised split. The survey helps researchers rapidly
but comprehensively understand and identify the research foundation, research
status, theoretical limitations, future challenges and under-studied issues
(universality, interpretability, and credibility) to be broken in the field
toward universal representation and safe applications in open-world scenarios.Comment: 26 pages, 4 figure
Transfer Metric Learning: Algorithms, Applications and Outlooks
Distance metric learning (DML) aims to find an appropriate way to reveal the
underlying data relationship. It is critical in many machine learning, pattern
recognition and data mining algorithms, and usually require large amount of
label information (such as class labels or pair/triplet constraints) to achieve
satisfactory performance. However, the label information may be insufficient in
real-world applications due to the high-labeling cost, and DML may fail in this
case. Transfer metric learning (TML) is able to mitigate this issue for DML in
the domain of interest (target domain) by leveraging knowledge/information from
other related domains (source domains). Although achieved a certain level of
development, TML has limited success in various aspects such as selective
transfer, theoretical understanding, handling complex data, big data and
extreme cases. In this survey, we present a systematic review of the TML
literature. In particular, we group TML into different categories according to
different settings and metric transfer strategies, such as direct metric
approximation, subspace approximation, distance approximation, and distribution
approximation. A summarization and insightful discussion of the various TML
approaches and their applications will be presented. Finally, we indicate some
challenges and provide possible future directions.Comment: 14 pages, 5 figure
Multiple kernel multivariate performance learning using cutting plane algorithm
In this paper, we propose a multi-kernel classifier learning algorithm to
optimize a given nonlinear and nonsmoonth multivariate classifier performance
measure. Moreover, to solve the problem of kernel function selection and kernel
parameter tuning, we proposed to construct an optimal kernel by weighted linear
combination of some candidate kernels. The learning of the classifier parameter
and the kernel weight are unified in a single objective function considering to
minimize the upper boundary of the given multivariate performance measure. The
objective function is optimized with regard to classifier parameter and kernel
weight alternately in an iterative algorithm by using cutting plane algorithm.
The developed algorithm is evaluated on two different pattern classification
methods with regard to various multivariate performance measure optimization
problems. The experiment results show the proposed algorithm outperforms the
competing methods
Discriminative Supervised Hashing for Cross-Modal similarity Search
With the advantage of low storage cost and high retrieval efficiency, hashing
techniques have recently been an emerging topic in cross-modal similarity
search. As multiple modal data reflect similar semantic content, many
researches aim at learning unified binary codes. However, discriminative
hashing features learned by these methods are not adequate. This results in
lower accuracy and robustness. We propose a novel hashing learning framework
which jointly performs classifier learning, subspace learning and matrix
factorization to preserve class-specific semantic content, termed
Discriminative Supervised Hashing (DSH), to learn the discrimative unified
binary codes for multi-modal data. Besides, reducing the loss of information
and preserving the non-linear structure of data, DSH non-linearly projects
different modalities into the common space in which the similarity among
heterogeneous data points can be measured. Extensive experiments conducted on
the three publicly available datasets demonstrate that the framework proposed
in this paper outperforms several state-of -the-art methods.Comment: 7 pages,3 figures,4 tables;The paper is under consideration at Image
and Vision Computin
JTAV: Jointly Learning Social Media Content Representation by Fusing Textual, Acoustic, and Visual Features
Learning social media content is the basis of many real-world applications,
including information retrieval and recommendation systems, among others. In
contrast with previous works that focus mainly on single modal or bi-modal
learning, we propose to learn social media content by fusing jointly textual,
acoustic, and visual information (JTAV). Effective strategies are proposed to
extract fine-grained features of each modality, that is, attBiGRU and DCRNN. We
also introduce cross-modal fusion and attentive pooling techniques to integrate
multi-modal information comprehensively. Extensive experimental evaluation
conducted on real-world datasets demonstrates our proposed model outperforms
the state-of-the-art approaches by a large margin
A Survey of Deep Facial Attribute Analysis
Facial attribute analysis has received considerable attention when deep
learning techniques made remarkable breakthroughs in this field over the past
few years. Deep learning based facial attribute analysis consists of two basic
sub-issues: facial attribute estimation (FAE), which recognizes whether facial
attributes are present in given images, and facial attribute manipulation
(FAM), which synthesizes or removes desired facial attributes. In this paper,
we provide a comprehensive survey of deep facial attribute analysis from the
perspectives of both estimation and manipulation. First, we summarize a general
pipeline that deep facial attribute analysis follows, which comprises two
stages: data preprocessing and model construction. Additionally, we introduce
the underlying theories of this two-stage pipeline for both FAE and FAM.
Second, the datasets and performance metrics commonly used in facial attribute
analysis are presented. Third, we create a taxonomy of state-of-the-art methods
and review deep FAE and FAM algorithms in detail. Furthermore, several
additional facial attribute related issues are introduced, as well as relevant
real-world applications. Finally, we discuss possible challenges and promising
future research directions.Comment: submitted to International Journal of Computer Vision (IJCV
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