AI RESEARCH
Noise-aware few-shot learning through bi-directional multi-view prompt alignment
arXiv CS.CV
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ArXi:2603.11617v1 Announce Type: new Vision-language models offer strong few-shot capability through prompt tuning but remain vulnerable to noisy labels, which can corrupt prompts and degrade cross-modal alignment. Existing approaches struggle because they often lack the ability to model fine-grained semantic cues and to adaptively separate clean from noisy signals. To address these challenges, we propose NA-MVP, a framework for Noise-Aware few-shot learning through bi-directional Multi-View Prompt alignment.