AI RESEARCH
Dynamic Visual-semantic Alignment for Zero-shot Learning with Ambiguous Labels
arXiv CS.CV
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ArXi:2604.17710v1 Announce Type: new 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 ambiguity, which degrades performance. To bridge this gap, we propose the Dynamic Visual-semantic Alignment (DVSA), a robust ZSL framework for learning from ambiguous labels.