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

Key points

• In image-to-image (I2I) ReID, viewpoint, illumination, occlusion, and background changes can make distractors appear more similar than the true match.1
• 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.1
• We therefore propose PRompt-guided Attribute-based Inference-time Similarity Enhancement (PRAISE), a query-side test-time adaptation framework for VLM-based ReID.1
• ACS provides a controlled attribute-conditioned geometry for reshaping query--gallery affinities and guiding lightweight inference-time updates without identity or attribute labels.1

Sources (1)

• [1]In PRAISE of Attributes: Grounding Retrieval Geometry with Query-Conditioned Subspaces for Person Re-Identification
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 09:52 AM

  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.

Extractive summary: sentences quoted from the sources.