4 papers
CV-DCLR: Causal-Visual Dynamic Label Refinement for Robust Zero-Shot Learning
Can Wang, Jiangnan Li, Mingyu Li +4
Zero-Shot Learning (ZSL) facilitates knowledge transfer via shared semantic spaces. However, a critical bottleneck in this paradigm is Semantic Entanglement, where visual represent…
Dynamic Visual-semantic Alignment for Zero-shot Learning with Ambiguous Labels
Jiangnan Li, Linqing Huang, Xiaowen Yan +3
Zero-shot learning (ZSL) aims to recognize unseen classes without visual instances. However, existing methods usually assume clean labels, overlooking real-world label noise and am…
CLIP-driven Zero-shot Learning with Ambiguous Labels
Jinfu Fan, Jiangnan Li, Xiaowen Yan +3
Zero-shot learning (ZSL) aims to recognize unseen classes by leveraging semantic information from seen classes, but most existing methods assume accurate class labels for training…
Diffusion Disambiguation Models for Partial Label Learning
Jinfu Fan, Xiaohui Zhong, Kangrui Ren +4
Learning from ambiguous labels is a long-standing problem in practical machine learning applications. The purpose of \emph{partial label learning} (PLL) is to identify the ground-t…