6 papers
GenPrior: Unleashing Text-to-Motion Generative Priors for Zero-Shot Skeleton-based Action Recognition
Jidong Kuang, Hongsong Wang, Jie Gui
Zero-shot skeleton-based action recognition (ZSAR) aims to recognize unseen action categories by aligning skeleton features with textual semantics. However, existing methods rely o…
Marrying Text-to-Motion Generation with Skeleton-Based Action Recognition
Jidong Kuang, Hongsong Wang, Jie Gui
Human action recognition and motion generation are two active research problems in human-centric computer vision, both aiming to align motion with textual semantics. However, most…
Toward Universal Skeleton-Based Action Recognition across Heterogeneous Skeletons and Open Vocabularies
Jidong Kuang, Hongsong Wang, Jie Gui +2
Skeleton data used for action recognition are acquired from a wide range of sources, including depth sensors, marker-based motion capture systems, and 2D/3D pose estimators. These…
Zero-Shot Skeleton-based Action Recognition with Dual Visual-Text Alignment
Jidong Kuang, Hongsong Wang, Chaolei Han +2
Zero-shot action recognition, which addresses the issue of scalability and generalization in action recognition and allows the models to adapt to new and unseen actions dynamically…
Heterogeneous Skeleton-Based Action Representation Learning
Hongsong Wang, Xiaoyan Ma, Jidong Kuang +1
Skeleton-based human action recognition has received widespread attention in recent years due to its diverse range of application scenarios. Due to the different sources of human s…
Training-Free Zero-Shot Temporal Action Detection with Vision-Language Models
Chaolei Han, Hongsong Wang, Jidong Kuang +2
Existing zero-shot temporal action detection (ZSTAD) methods predominantly use fully supervised or unsupervised strategies to recognize unseen activities. However, these training-b…