AI RESEARCH
ALADIN:Attribute-Language Distillation Network for Person Re-Identification
arXiv CS.CV
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ArXi:2603.21482v1 Announce Type: new Recent vision-language models such as CLIP provide strong cross-modal alignment, but current CLIP-guided ReID pipelines rely on global features and fixed prompts. This limits their ability to capture fine-grained attribute cues and adapt to diverse appearances. We propose ALADIN, an attribute-language distillation network that distills knowledge from a frozen CLIP teacher to a lightweight ReID student.