3 papers
cs.CV2026
Knowledge-guided Disentanglement with Atomic Actions for Action Recognition
Tianci Wu, Siqi Cao, Guangming Zhu +6
The paper introduces a framework that uses large language models to break down action labels into atomic actions and injects this semantic knowledge into video features to improve…
cs.CV2026
Sketch and Text Synergy: Fusing Structural Contours and Descriptive Attributes for Fine-Grained Image Retrieval
Siyuan Wang, Hanchen Gao, Guangming Zhu +5
Fine-grained image retrieval via hand-drawn sketches or textual descriptions remains a critical challenge due to inherent modality gaps. While hand-drawn sketches capture complex s…
cs.CV2025
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…