Learning Instance-Adaptive Low-Rank Orthogonal Subspaces for Clothes-Changing Person Re-Identification
arXiv:2606.11661v1 Announce Type: cross 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-a
The proliferation of advanced AI in computer vision necessitates more robust and adaptable identification systems, especially for surveillance and security applications where appearance can change frequently.
This development addresses a significant challenge in person re-identification by making systems more resilient to common variations, improving accuracy and applicability in real-world scenarios.
Person re-identification systems will become more effective in 'clothes-changing' scenarios, enhancing surveillance, forensics, and retail analytics by reducing false negatives due to appearance alterations.
- · Security and surveillance companies
- · Smart city developers
- · Retail analytics firms
- · Law enforcement agencies
- · Criminals relying on appearance changes
Improved accuracy in tracking individuals across diverse environments and timeframes.
Increased efficiency in forensic investigations and public safety operations.
Potential for new ethical and privacy concerns regarding persistent identification capabilities.
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Read at arXiv cs.LG