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English(EN) Structural-Semantic Reciprocal Learning for Unsupervised Visible-Infrared Person Re-Identification

新的SSRL框架增强了无监督行人重识别能力

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了一种名为结构-语义互学习（SSRL）的新框架，以改进无监督可见光-红外图像行人重识别。该方法通过采用细粒度结构解耦提取可靠的身体部位锚点，并通过闭环语义校准机制过滤噪声，从而解决了模态差异和噪声伪标签等挑战。SSRL旨在创建一个自纠正系统，以实现鲁棒的跨模态表示，并在基准数据集上取得了有竞争力的结果。 AI

影响 这项研究可能带来在复杂条件下更准确、更鲁棒的行人重识别系统。

排序理由 这是一篇详细介绍特定计算机视觉任务新方法的学术论文。

在 arXiv cs.CV 阅读 →

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新的SSRL框架增强了无监督行人重识别能力

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报道来源 [2]

  1. arXiv cs.CV TIER_1 English(EN) · Moyao Tian, Shijia Liu, Yan Yang, Xin Yuan, Minshi Chen, Wei Wang, Xiao Wang ·

    面向无监督可见光-红外行人重识别的结构-语义互学习

    arXiv:2607.15220v1 Announce Type: new Abstract: Unsupervised visible-infrared person re-identification (USVI-ReID) is challenging due to the large modality gap and the lack of cross-modal identity annotations. Progressive association paradigms have been proposed to gradually brid…

  2. arXiv cs.CV TIER_1 English(EN) · Xiao Wang ·

    面向无监督可见光-红外行人重识别的结构-语义互学习

    Unsupervised visible-infrared person re-identification (USVI-ReID) is challenging due to the large modality gap and the lack of cross-modal identity annotations. Progressive association paradigms have been proposed to gradually bridge the gap, but they suffer from two critical bo…

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