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English(EN) MINER: Modeling Inter- and Intra-Person Variance for Reidentification

MINER框架通过建模个体间和个体内部方差来增强人员重新识别能力

作者 PulseAugur 编辑部 · [1 个来源] ·

研究人员推出MINER，一个旨在改进换衣人员重新识别（CC-ReID）的新颖框架。这种模型无关的方法通过关注个体间方差和个体内部方差来应对剧烈的外观变化挑战，而以往的方法主要增强个体间方差。MINER通过对比困难样本并自适应地平衡数据集依赖的方差来抑制服装偏差。实验表明，MINER在四个CC-ReID基准测试中取得了最先进的性能，并在TVID RGB-Depth ReID挑战赛中名列前茅。 AI

影响 这项研究可能带来更强大的人员重新识别系统，对监控和安全应用产生潜在影响。

排序理由 该集群包含一篇详细介绍新模型及其在基准测试中性能的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CV 阅读 →

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MINER框架通过建模个体间和个体内部方差来增强人员重新识别能力

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该集群包含一篇详细介绍新模型及其在基准测试中性能的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

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

    MINER：建模个体间和个体内部方差以用于重新识别

    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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