1 citations · 1 across the 2 of their papers we have counts for
5 papers
SkelHCC: A Hyperbolic CLIP-Driven Cache Adaptation Framework for Skeleton-based One-Shot Action Recognition
Yanan Liu, Anqi Zhu, Jingmin Zhu +6
Skeleton-based action recognition aims to understand human behaviors from body joint sequences and is especially challenging in the one-shot setting, where only a single labeled ex…
DynaPURLS: Dynamic Refinement of Part-Aware Representations for Skeleton-Based Zero-Shot Action Recognition
Jingmin Zhu, Anqi Zhu, James Bailey +5
Zero-shot skeleton-based action recognition (ZS-SAR) is fundamentally constrained by prevailing approaches that rely on aligning skeleton features with static, class-level semantic…
Boosting Skeleton-based Zero-Shot Action Recognition with Training-Free Test-Time Adaptation
Jingmin Zhu, Anqi Zhu, Hossein Rahmani +3
We introduce Skeleton-Cache, the first training-free test-time adaptation framework for skeleton-based zero-shot action recognition (SZAR), aimed at improving model generalization…
Skeleton-OOD: An End-to-End Skeleton-Based Model for Robust Out-of-Distribution Human Action Detection
Jing Xu, Anqi Zhu, Jingyu Lin +2
Human action recognition is crucial in computer vision systems. However, in real-world scenarios, human actions often fall outside the distribution of training data, requiring a mo…
Part-aware Unified Representation of Language and Skeleton for Zero-shot Action Recognition
Anqi Zhu, Qiuhong Ke, Mingming Gong +1
While remarkable progress has been made on supervised skeleton-based action recognition, the challenge of zero-shot recognition remains relatively unexplored. In this paper, we arg…