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MINER framework enhances person re-identification by modeling inter- and intra-person variance

By PulseAugur Editorial · [1 sources] ·

Researchers have introduced MINER, a novel framework designed to improve Clothes-Changing Person Re-Identification (CC-ReID). This model-agnostic approach addresses the challenge of drastic appearance shifts by focusing on both inter-person variance and intra-person variance, unlike previous methods that primarily enhanced inter-person variance. MINER suppresses garment bias by contrasting hard examples and adaptively balancing dataset-dependent variances. Experiments demonstrate that MINER achieves state-of-the-art performance on four CC-ReID benchmarks and is a top contender in the TVRID RGB-Depth ReID challenge. AI

IMPACT This research could lead to more robust person re-identification systems, potentially impacting surveillance and security applications.

RANK_REASON The cluster contains an academic paper detailing a new model and its performance on benchmarks. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CV →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

MINER framework enhances person re-identification by modeling inter- and intra-person variance

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The cluster contains an academic paper detailing a new model and its performance on benchmarks. [lever_c_demoted from research: ic=1 ai=1.0]
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COVERAGE [1]

  1. arXiv cs.CV TIER_1 English(EN) · Oron Nir, Eliyahu Strugo, Ariel Shamir ·

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

    arXiv:2610.28941v1 Announce Type: new 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 se…

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