ALADIN:Attribute-Language Distillation Network for Person Re-Identification

TL;DR

ALADIN introduces a novel attribute-language distillation approach for person re-identification, enhancing fine-grained attribute understanding and robustness by leveraging CLIP and multimodal LLMs.

Contribution

It proposes a new attribute-local alignment and distillation framework that improves ReID performance and interpretability over existing global feature-based methods.

Findings

Significant performance gains on Market-1501, DukeMTMC-reID, and MSMT17 datasets.

Enhanced robustness under occlusions through attribute-local distillation.

Better generalization and interpretability compared to CNN, Transformer, and CLIP-based methods.

Abstract

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. ALADIN introduces fine-grained attribute-local alignment to establish adaptive text-visual correspondence and robust representation learning. A Scene-Aware Prompt Generator produces image-specific soft prompts to facilitate adaptive alignment. Attribute-local distillation enforces consistency between textual attributes and local visual features, significantly enhancing robustness under occlusions. Furthermore, we employ cross-modal contrastive and relation distillation to preserve the inherent structural relationships among attributes. To provide precise supervision, we leverage Multimodal LLMs to generate structured attribute descriptions, which are then converted into localized attention maps via CLIP. At inference, only the student is used. Experiments on Market-1501, DukeMTMC-reID, and MSMT17 show improvements over CNN-, Transformer-, and CLIP-based methods, with better generalization and interpretability.