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• W4386071639 abstract "Entangled representation of clothing and identity (ID)-intrinsic clues are potentially concomitant in conventional person Re- IDentification (ReID). Nevertheless, eliminating the negative impact of clothing on ID remains challenging due to the lack of theory and the difficulty of isolating the exact implications. In this paper, a causality-based Auto-Intervention Model, referred to as AIM <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>1</sup> <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>1</sup> Codes will publicly available at https://github.com/BoomShakaY/AIM-CCReID., is first proposed to mitigate clothing bias for robust cloth-changing person ReID (CC-ReID). Specifically, we analyze the effect of clothing on the model inference and adopt a dual-branch model to simulate causal intervention. Progressively, clothing bias is eliminated automatically with model training. AIM is encouraged to learn more discriminative ID clues that are free from clothing bias. Extensive experiments on two standard CC-ReID datasets demonstrate the superiority of the proposed AIM over other state-of-the-art methods." @default.
• W4386071639 created "2023-08-23" @default.
• W4386071639 creator A5000102107 @default.
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• W4386071639 date "2023-06-01" @default.
• W4386071639 modified "2023-10-10" @default.
• W4386071639 title "Good is Bad: Causality Inspired Cloth-debiasing for Cloth-changing Person Re-identification" @default.
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• W4386071639 doi "https://doi.org/10.1109/cvpr52729.2023.00148" @default.
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