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 …

Human parsing aims to partition humans in image or video into multiple pixel-level semantic
parts. In the last decade, it has gained significantly increased interest in the computer vision …

Abstract Spiking Neural Networks (SNNs) have gained huge attention as a potential energy-
efficient alternative to conventional Artificial Neural Networks (ANNs) due to their inherent …

In this work, we study the challenging problem of instance-aware human body part parsing.
We introduce a new bottom-up regime which achieves the task through learning category …

Human Body Part Semantic Segmentation and Human Pose estimation are considered to
be essential for understanding human behaviours. Both of these tasks are correlated with …

Human sensing and understanding is a key requirement for many intelligent systems, such
as smart monitoring, human-computer interaction, and activity analysis, etc. In this paper, we …

Estimating 3D human poses only from a 2D human pose sequence is thoroughly explored in
recent years. Yet, prior to this, no such work has attempted to unify 2D and 3D pose …

Abstract This paper presents Scalable Semantic Transfer (SST), a novel training paradigm,
to explore how to leverage the mutual benefits of the data from different label domains (ie …

How to estimate the quality of the network output is an important issue, and currently there is
no effective solution in the field of human parsing. To solve this problem, this work proposes …

Abstract Domain shift is a challenge for supervised human pose estimation where the
source data and target data come from different distributions. This is why pose estimation …