In PRAISE of Attributes: Grounding Retrieval Geometry with Query-Conditioned Subspaces for Person Re-Identification

Person re-identification (ReID) ranks an image gallery using either an image query or a textual query. In image-to-image (I2I) ReID, viewpoint, illumination, occlusion, and background changes can make distractors appear more similar than the true match. In text-to-image (T2I) ReID, incomplete or ambiguous descriptions may omit key visual evidence. We study this problem for vision-language model (VLM) based ReID, where attributes such as clothing color and carried objects provide useful fine-grained matching cues. However, directly using attribute-prompt similarities as retrieval scores is unreliable because templated prompts can be biased, correlated, and poorly calibrated. We therefore propose $\mathbf{\text{PR}}$ompt-guided $\mathbf{\text{A}}$ttribute-based $\mathbf{\text{I}}$nference-time $\mathbf{\text{S}}$imilarity $\mathbf{\text{E}}$nhancement ($\mathtt{\text{PRAISE}}$), a query-side test-time adaptation framework for VLM-based ReID. Instead of treating prompts as scores, $\mathtt{\text{PRAISE}}$ uses a transferable attribute-prompt bank to construct an \emph{Attribute-Conditioned Subspace} (ACS): a query-dependent low-rank subspace induced by the most compatible attribute prompts. ACS provides a controlled attribute-conditioned geometry for reshaping query–gallery affinities and guiding lightweight inference-time updates without identity or attribute labels. Experiments across multiple I2I- and T2I-ReID benchmarks show that $\mathtt{\text{PRAISE}}$ consistently improves VLM retrieval backbones and outperforms both generic and ReID-specific TTA baselines.