4,044 research outputs found
Recent Progress in Image Deblurring
This paper comprehensively reviews the recent development of image
deblurring, including non-blind/blind, spatially invariant/variant deblurring
techniques. Indeed, these techniques share the same objective of inferring a
latent sharp image from one or several corresponding blurry images, while the
blind deblurring techniques are also required to derive an accurate blur
kernel. Considering the critical role of image restoration in modern imaging
systems to provide high-quality images under complex environments such as
motion, undesirable lighting conditions, and imperfect system components, image
deblurring has attracted growing attention in recent years. From the viewpoint
of how to handle the ill-posedness which is a crucial issue in deblurring
tasks, existing methods can be grouped into five categories: Bayesian inference
framework, variational methods, sparse representation-based methods,
homography-based modeling, and region-based methods. In spite of achieving a
certain level of development, image deblurring, especially the blind case, is
limited in its success by complex application conditions which make the blur
kernel hard to obtain and be spatially variant. We provide a holistic
understanding and deep insight into image deblurring in this review. An
analysis of the empirical evidence for representative methods, practical
issues, as well as a discussion of promising future directions are also
presented.Comment: 53 pages, 17 figure
Second-order Temporal Pooling for Action Recognition
Deep learning models for video-based action recognition usually generate
features for short clips (consisting of a few frames); such clip-level features
are aggregated to video-level representations by computing statistics on these
features. Typically zero-th (max) or the first-order (average) statistics are
used. In this paper, we explore the benefits of using second-order statistics.
Specifically, we propose a novel end-to-end learnable feature aggregation
scheme, dubbed temporal correlation pooling that generates an action descriptor
for a video sequence by capturing the similarities between the temporal
evolution of clip-level CNN features computed across the video. Such a
descriptor, while being computationally cheap, also naturally encodes the
co-activations of multiple CNN features, thereby providing a richer
characterization of actions than their first-order counterparts. We also
propose higher-order extensions of this scheme by computing correlations after
embedding the CNN features in a reproducing kernel Hilbert space. We provide
experiments on benchmark datasets such as HMDB-51 and UCF-101, fine-grained
datasets such as MPII Cooking activities and JHMDB, as well as the recent
Kinetics-600. Our results demonstrate the advantages of higher-order pooling
schemes that when combined with hand-crafted features (as is standard practice)
achieves state-of-the-art accuracy.Comment: Accepted in the International Journal of Computer Vision (IJCV
Sparse Coding on Symmetric Positive Definite Manifolds using Bregman Divergences
This paper introduces sparse coding and dictionary learning for Symmetric
Positive Definite (SPD) matrices, which are often used in machine learning,
computer vision and related areas. Unlike traditional sparse coding schemes
that work in vector spaces, in this paper we discuss how SPD matrices can be
described by sparse combination of dictionary atoms, where the atoms are also
SPD matrices. We propose to seek sparse coding by embedding the space of SPD
matrices into Hilbert spaces through two types of Bregman matrix divergences.
This not only leads to an efficient way of performing sparse coding, but also
an online and iterative scheme for dictionary learning. We apply the proposed
methods to several computer vision tasks where images are represented by region
covariance matrices. Our proposed algorithms outperform state-of-the-art
methods on a wide range of classification tasks, including face recognition,
action recognition, material classification and texture categorization
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