9,602 research outputs found
cvpaper.challenge in 2016: Futuristic Computer Vision through 1,600 Papers Survey
The paper gives futuristic challenges disscussed in the cvpaper.challenge. In
2015 and 2016, we thoroughly study 1,600+ papers in several
conferences/journals such as CVPR/ICCV/ECCV/NIPS/PAMI/IJCV
Deep Facial Expression Recognition: A Survey
With the transition of facial expression recognition (FER) from
laboratory-controlled to challenging in-the-wild conditions and the recent
success of deep learning techniques in various fields, deep neural networks
have increasingly been leveraged to learn discriminative representations for
automatic FER. Recent deep FER systems generally focus on two important issues:
overfitting caused by a lack of sufficient training data and
expression-unrelated variations, such as illumination, head pose and identity
bias. In this paper, we provide a comprehensive survey on deep FER, including
datasets and algorithms that provide insights into these intrinsic problems.
First, we describe the standard pipeline of a deep FER system with the related
background knowledge and suggestions of applicable implementations for each
stage. We then introduce the available datasets that are widely used in the
literature and provide accepted data selection and evaluation principles for
these datasets. For the state of the art in deep FER, we review existing novel
deep neural networks and related training strategies that are designed for FER
based on both static images and dynamic image sequences, and discuss their
advantages and limitations. Competitive performances on widely used benchmarks
are also summarized in this section. We then extend our survey to additional
related issues and application scenarios. Finally, we review the remaining
challenges and corresponding opportunities in this field as well as future
directions for the design of robust deep FER systems
Efficient Face Alignment via Locality-constrained Representation for Robust Recognition
Practical face recognition has been studied in the past decades, but still
remains an open challenge. Current prevailing approaches have already achieved
substantial breakthroughs in recognition accuracy. However, their performance
usually drops dramatically if face samples are severely misaligned. To address
this problem, we propose a highly efficient misalignment-robust
locality-constrained representation (MRLR) algorithm for practical real-time
face recognition. Specifically, the locality constraint that activates the most
correlated atoms and suppresses the uncorrelated ones, is applied to construct
the dictionary for face alignment. Then we simultaneously align the warped face
and update the locality-constrained dictionary, eventually obtaining the final
alignment. Moreover, we make use of the block structure to accelerate the
derived analytical solution. Experimental results on public data sets show that
MRLR significantly outperforms several state-of-the-art approaches in terms of
efficiency and scalability with even better performance
Context-Aware Deep Spatio-Temporal Network for Hand Pose Estimation from Depth Images
As a fundamental and challenging problem in computer vision, hand pose
estimation aims to estimate the hand joint locations from depth images.
Typically, the problem is modeled as learning a mapping function from images to
hand joint coordinates in a data-driven manner. In this paper, we propose
Context-Aware Deep Spatio-Temporal Network (CADSTN), a novel method to jointly
model the spatio-temporal properties for hand pose estimation. Our proposed
network is able to learn the representations of the spatial information and the
temporal structure from the image sequences. Moreover, by adopting adaptive
fusion method, the model is capable of dynamically weighting different
predictions to lay emphasis on sufficient context. Our method is examined on
two common benchmarks, the experimental results demonstrate that our proposed
approach achieves the best or the second-best performance with state-of-the-art
methods and runs in 60fps.Comment: IEEE Transactions On Cybernetic
High-Resolution Representations for Labeling Pixels and Regions
High-resolution representation learning plays an essential role in many
vision problems, e.g., pose estimation and semantic segmentation. The
high-resolution network (HRNet)~\cite{SunXLW19}, recently developed for human
pose estimation, maintains high-resolution representations through the whole
process by connecting high-to-low resolution convolutions in \emph{parallel}
and produces strong high-resolution representations by repeatedly conducting
fusions across parallel convolutions.
In this paper, we conduct a further study on high-resolution representations
by introducing a simple yet effective modification and apply it to a wide range
of vision tasks. We augment the high-resolution representation by aggregating
the (upsampled) representations from all the parallel convolutions rather than
only the representation from the high-resolution convolution as done
in~\cite{SunXLW19}. This simple modification leads to stronger representations,
evidenced by superior results. We show top results in semantic segmentation on
Cityscapes, LIP, and PASCAL Context, and facial landmark detection on AFLW,
COFW, W, and WFLW. In addition, we build a multi-level representation from
the high-resolution representation and apply it to the Faster R-CNN object
detection framework and the extended frameworks. The proposed approach achieves
superior results to existing single-model networks on COCO object detection.
The code and models have been publicly available at
\url{https://github.com/HRNet}
An Automatic System for Unconstrained Video-Based Face Recognition
Although deep learning approaches have achieved performance surpassing humans
for still image-based face recognition, unconstrained video-based face
recognition is still a challenging task due to large volume of data to be
processed and intra/inter-video variations on pose, illumination, occlusion,
scene, blur, video quality, etc. In this work, we consider challenging
scenarios for unconstrained video-based face recognition from multiple-shot
videos and surveillance videos with low-quality frames. To handle these
problems, we propose a robust and efficient system for unconstrained
video-based face recognition, which is composed of modules for face/fiducial
detection, face association, and face recognition. First, we use multi-scale
single-shot face detectors to efficiently localize faces in videos. The
detected faces are then grouped respectively through carefully designed face
association methods, especially for multi-shot videos. Finally, the faces are
recognized by the proposed face matcher based on an unsupervised subspace
learning approach and a subspace-to-subspace similarity metric. Extensive
experiments on challenging video datasets, such as Multiple Biometric Grand
Challenge (MBGC), Face and Ocular Challenge Series (FOCS), IARPA Janus
Surveillance Video Benchmark (IJB-S) for low-quality surveillance videos and
IARPA JANUS Benchmark B (IJB-B) for multiple-shot videos, demonstrate that the
proposed system can accurately detect and associate faces from unconstrained
videos and effectively learn robust and discriminative features for
recognition
Face Recognition: From Traditional to Deep Learning Methods
Starting in the seventies, face recognition has become one of the most
researched topics in computer vision and biometrics. Traditional methods based
on hand-crafted features and traditional machine learning techniques have
recently been superseded by deep neural networks trained with very large
datasets. In this paper we provide a comprehensive and up-to-date literature
review of popular face recognition methods including both traditional
(geometry-based, holistic, feature-based and hybrid methods) and deep learning
methods
Face Recognition: A Novel Multi-Level Taxonomy based Survey
In a world where security issues have been gaining growing importance, face
recognition systems have attracted increasing attention in multiple application
areas, ranging from forensics and surveillance to commerce and entertainment.
To help understanding the landscape and abstraction levels relevant for face
recognition systems, face recognition taxonomies allow a deeper dissection and
comparison of the existing solutions. This paper proposes a new, more
encompassing and richer multi-level face recognition taxonomy, facilitating the
organization and categorization of available and emerging face recognition
solutions; this taxonomy may also guide researchers in the development of more
efficient face recognition solutions. The proposed multi-level taxonomy
considers levels related to the face structure, feature support and feature
extraction approach. Following the proposed taxonomy, a comprehensive survey of
representative face recognition solutions is presented. The paper concludes
with a discussion on current algorithmic and application related challenges
which may define future research directions for face recognition.Comment: This paper is a preprint of a paper submitted to IET Biometrics. If
accepted, the copy of record will be available at the IET Digital Librar
Deep Learning for Generic Object Detection: A Survey
Object detection, one of the most fundamental and challenging problems in
computer vision, seeks to locate object instances from a large number of
predefined categories in natural images. Deep learning techniques have emerged
as a powerful strategy for learning feature representations directly from data
and have led to remarkable breakthroughs in the field of generic object
detection. Given this period of rapid evolution, the goal of this paper is to
provide a comprehensive survey of the recent achievements in this field brought
about by deep learning techniques. More than 300 research contributions are
included in this survey, covering many aspects of generic object detection:
detection frameworks, object feature representation, object proposal
generation, context modeling, training strategies, and evaluation metrics. We
finish the survey by identifying promising directions for future research.Comment: IJCV Mino
Object Detection in 20 Years: A Survey
Object detection, as of one the most fundamental and challenging problems in
computer vision, has received great attention in recent years. Its development
in the past two decades can be regarded as an epitome of computer vision
history. If we think of today's object detection as a technical aesthetics
under the power of deep learning, then turning back the clock 20 years we would
witness the wisdom of cold weapon era. This paper extensively reviews 400+
papers of object detection in the light of its technical evolution, spanning
over a quarter-century's time (from the 1990s to 2019). A number of topics have
been covered in this paper, including the milestone detectors in history,
detection datasets, metrics, fundamental building blocks of the detection
system, speed up techniques, and the recent state of the art detection methods.
This paper also reviews some important detection applications, such as
pedestrian detection, face detection, text detection, etc, and makes an in-deep
analysis of their challenges as well as technical improvements in recent years.Comment: This work has been submitted to the IEEE TPAMI for possible
publicatio
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