Learning Instance-Adaptive Low-Rank Orthogonal Subspaces for Clothes-Changing Person Re-Identification

• URL: http://arxiv.org/abs/2606.11661v1
• Date: Wed, 10 Jun 2026 05:07:45 GMT
• Title: Learning Instance-Adaptive Low-Rank Orthogonal Subspaces for Clothes-Changing Person Re-Identification
• Abstract summary: CC-ReID aims to recognize individuals despite drastic appearance changes caused by clothing variation.<n>We propose Ortho-ReID, which explicitly models a low-rank clothing subspace from VLM text descriptions.<n>A critical component is our transformer-based Basis Maker, which refines a shared, low-dimensional clothing.
• Score: 19.06001747435738
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Clothes-changing person re-identification (CC-ReID) aims to recognize individuals despite drastic appearance changes caused by clothing variation. While existing methods rely on adversarial learning to disentangle clothing features, we propose Ortho-ReID, which explicitly models a low-rank clothing subspace from VLM text descriptions and extracts clothing-invariant representations via direct geometric constraints. A critical component is our transformer-based Basis Maker, which refines a shared, low-dimensional clothing prior into an instance-adaptive low-rank subspace through cross-attention with image patches, enabling robust clothing feature extraction even under varying visibility conditions. This instance-adaptive subspace is supervised via alignment with clothing text embeddings, while identity features are extracted via a learnable projection head and geometrically constrained to be strictly orthogonal to it. Extensive experiments demonstrate state-of-the-art performance on PRCC (+5.9% top-1), Celeb-reID-light (+3.5%), and LaST (+5.3%), with competitive results on LTCC.

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