activity
20192022
most citedDifferentiable Hierarchical Graph Grouping for Multi-Person Pose Estimation

15 citations · 50 across the 7 of their papers we have counts for

collaborators

7 papers

cs.CV202210 cited

Not All Tokens Are Equal: Human-centric Visual Analysis via Token Clustering Transformer

Wang Zeng, Sheng Jin, Wentao Liu +4

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 treatin…

cs.CV20228 cited

Pseudo-Labeled Auto-Curriculum Learning for Semi-Supervised Keypoint Localization

Can Wang, Sheng Jin, Yingda Guan +4

Localizing keypoints of an object is a basic visual problem. However, supervised learning of a keypoint localization network often requires a large amount of data, which is expensi…

cs.CV20216 cited

Graph-Based 3D Multi-Person Pose Estimation Using Multi-View Images

Size Wu, Sheng Jin, Wentao Liu +4

This paper studies the task of estimating the 3D human poses of multiple persons from multiple calibrated camera views. Following the top-down paradigm, we decompose the task into…

cs.CV202015 cited

Differentiable Hierarchical Graph Grouping for Multi-Person Pose Estimation

Sheng Jin, Wentao Liu, Enze Xie +4

Multi-person pose estimation is challenging because it localizes body keypoints for multiple persons simultaneously. Previous methods can be divided into two streams, i.e. top-down…

cs.CV20204 cited

Whole-Body Human Pose Estimation in the Wild

Sheng Jin, Lumin Xu, Jin Xu +5

This paper investigates the task of 2D human whole-body pose estimation, which aims to localize dense landmarks on the entire human body including face, hands, body, and feet. As e…

cs.LG2020

RL-Duet: Online Music Accompaniment Generation Using Deep Reinforcement Learning

Nan Jiang, Sheng Jin, Zhiyao Duan +1

This paper presents a deep reinforcement learning algorithm for online accompaniment generation, with potential for real-time interactive human-machine duet improvisation. Differen…