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MINER: Modeling Inter- and Intra-Person Variance for Reidentification

2026-10-20 21:34:43
Oron Nir, Eliyahu Strugo, Ariel Shamir
arXiv_CV
         
Abstract

Clothes-Changing Person Re-Identification (CC-ReID) remains challenging due to drastic appearance shifts across outfits. Most prior works devise biometric signal-extraction heuristics to enhance inter-person variance, e.g., using segmentation masks, while leaving intra-person variance uncontrolled. We present MINER, a simple model-agnostic framework that suppresses garment bias by contrasting hard examples and adaptively balancing dataset-dependent intra- and inter-person variance. Extensive experiments show that MINER improves retrieval across CC-ReID benchmarks, achieving state-of-the-art performance on four datasets and ranking among the leaders in all three tracks of the open TVRID RGB-Depth ReID challenge.

Abstract (translated)

URL

https://arxiv.org/abs/2610.28941

PDF

https://arxiv.org/pdf/2610.28941.pdf

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