6 papers · 1 filter
Knowledge-guided Disentanglement with Atomic Actions for Action Recognition
Tianci Wu, Siqi Cao, Guangming Zhu +6
Action recognition in complex scenes often involves multiple concurrent fine-grained actions, making it challenging to model internal action structures. Most existing methods rely…
SkeletonContext: Skeleton-side Context Prompt Learning for Zero-Shot Skeleton-based Action Recognition
Ning Wang, Tieyue Wu, Naeha Sharif +5
Zero-shot skeleton-based action recognition aims to recognize unseen actions by transferring knowledge from seen categories through semantic descriptions. Most existing methods typ…
Multi-Granularity Mutual Refinement Network for Zero-Shot Learning
Ning Wang, Long Yu, Cong Hua +5
Zero-shot learning (ZSL) aims to recognize unseen classes with zero samples by transferring semantic knowledge from seen classes. Current approaches typically correlate global visu…
Prompt-guided Disentangled Representation for Action Recognition
Tianci Wu, Guangming Zhu, Jiang Lu +4
Action recognition is a fundamental task in video understanding. Existing methods typically extract unified features to process all actions in one video, which makes it challenging…
Language Model Guided Interpretable Video Action Reasoning
Ning Wang, Guangming Zhu, HS Li +3
While neural networks have excelled in video action recognition tasks, their black-box nature often obscures the understanding of their decision-making processes. Recent approaches…
Image classification network enhancement methods based on knowledge injection
Yishuang Tian, Ning Wang, Liang Zhang
The current deep neural network algorithm still stays in the end-to-end training supervision method like Image-Label pairs, which makes traditional algorithm is difficult to explai…