WITHDRAWN: Mixture-of-Experts Vision Transformer for Occluded Person Re-Identification

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Abstract

Abstract Person re-identification (ReID) is crucial for cross-camera target retrieval, yet it faces significant challenges due to occlusion. We introduce the Occlusion-Aware Mixture-of-Experts Vision Transformer (Occ-MoE), a dual-branch architecture that processes both original and occluded images. Occ-MoE incorporates an Occlusion-Aware Routing (OAR) mechanism and a Dual-Path Experts (DPE) module to emphasize robust identity-related features while suppressing occluded or non-pedestrian regions. Our Expert-based Restoration Module (ERM) facilitates dynamic interaction between occlusion stream features and predicted occlusion masks. Experiments on several benchmark datasets demonstrate superior ReID performance, improving both mean Average Precision (mAP) and Rank-1 accuracy, validating our design's effectiveness in mitigating feature corruption and performing occlusion feature recovery. Code is available at: https://github.com/BestTao/occ-moe.
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WITHDRAWN: Occ-MoE: Occlusion-Aware Mixture of Experts for Occluded Person Re-Identification | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article WITHDRAWN: Occ-MoE: Occlusion-Aware Mixture of Experts for Occluded Person Re-Identification Hongtao Zuo, Guoqing Zhang, Yuhui Zheng, Jianwei Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7822757/v3 This work is licensed under a CC BY 4.0 License Status: Posted Version 3 posted You are reading this latest preprint version Show more versions Editorial Note The full text of this preprint has been withdrawn by the authors in order to comply with an institutional policy on preprints. Therefore, the authors do not wish this work to be cited as a reference. Editorial notes are used to provide important context regarding the topic of a preprint or to alert readers to potential issues concerning that preprint or a downstream publication associated with it. For more information on editorial notes, see our Editorial Policies . Abstract The full text of this preprint has been withdrawn by the authors in order to comply with an institutional policy on preprints. Therefore, the authors do not wish this work to be cited as a reference. Artificial Intelligence and Machine Learning Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 3 posted You are reading this latest preprint version Show more versions Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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