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 …

With the advances of data-driven machine learning research, a wide variety of prediction
problems have been tackled. It has become critical to explore how machine learning and …

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 …

Vision transformers have achieved great successes in many computer vision tasks. Most
methods generate vision tokens by splitting an image into a regular and fixed grid and …

Pose estimation plays a critical role in human-centered vision applications. However, it is
difficult to deploy state-of-the-art HRNet-based pose estimation models on resource …

Recent studies on 2D pose estimation have achieved excellent performance on public
benchmarks, yet its application in the industrial community still suffers from heavy model …

In keypoint estimation tasks such as human pose estimation, heatmap-based regression is
the dominant approach despite possessing notable drawbacks: heatmaps intrinsically suffer …

Human action recognition has attracted considerable research attention in the field of
computer vision, especially for classroom environments. However, most relevant studies …

Crowded scenes make it challenging to differentiate persons and locate their pose
keypoints. This paper proposes the Contextual Instance Decoupling (CID), which presents a …

To address the challenging task of instance-aware human part parsing, a new bottom-up
regime is proposed to learn category-level human semantic segmentation as well as multi …