Progressive Semantic Learning for Unsupervised Skeleton-based Action Recognition

Feb 6, 2025·
Hao Qin
Hao Qin
,
Luyuan Chen
,
Ming Kong
,
Zhuoran Zhao
,
Xianzhou Zeng
,
Mengxu Lu
,
Qiang Zhu
· 1 min read
Abstract
ProSL progressively optimizes pseudo-label generation in self-supervised contrastive learning for skeleton-based action recognition. It builds a semantic codebook from clustering and iteratively improves representation learning on multiple downstream tasks.
Type
Publication
Machine Learning
publications

ProSL uses cluster-level semantic information to improve self-supervised skeleton representation learning beyond instance-level contrastive objectives.

Self-Supervised Learning Action Recognition Skeleton-Based Recognition
Hao Qin
Authors
Hao Qin
Ph.D. Student in Artificial Intelligence
I am a Ph.D. student in the College of Computer Science and Technology at Zhejiang University. My research focuses on 3D vision, 3D Gaussian Splatting, 3D-AIGC, and multi-agent systems, with broader interests in self-supervised representation learning and embodied visual content creation.