Human pose estimation aims to locate the human body parts and build human body
representation (eg, body skeleton) from input data such as images and videos. It has drawn …

Estimation of the human pose from a monocular camera has been an emerging research
topic in the computer vision community with many applications. Recently, benefiting from the …

In this paper, we are interested in the human pose estimation problem with a focus on
learning reliable high-resolution representations. Most existing methods recover high …

We propose a new end-to-end single image dehazing method, called Densely Connected
Pyramid Dehazing Network (DCPDN), which can jointly learn the transmission map …

In this paper, we are interested in the bottom-up paradigm of estimating human poses from
an image. We study the dense keypoint regression framework that is previously inferior to …

Severe weather conditions, such as rain and snow, adversely affect the visual quality of
images captured under such conditions, thus rendering them useless for further usage and …

Robot hands with tactile perception can improve the safety of object manipulation and also
improve the accuracy of object identification. Here, we report the integration of quadruple …

Adversarial training (AT) is always formulated as a minimax problem, of which the
performance depends on the inner optimization that involves the generation of adversarial …

We are concerned with a worst-case scenario in model generalization, in the sense that a
model aims to perform well on many unseen domains while there is only one single domain …

In this paper, we study the problem of learning Graph Convolutional Networks (GCNs) for
regression. Current architectures of GCNs are limited to the small receptive field of …