{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/core-reid-comprehensive-optimization-and","title":"CORE-ReID: Comprehensive Optimization and Refinement through Ensemble Fusion in Domain Adaptation for Person Re-Identification","arxiv_id":null,"date":"2024-06-03","proceeding":"Software 2024 6","authors":["Trinh Quoc Nguyen","Oky Dicky Ardiansyah Prima","Katsuyoshi Hotta"],"abstract":"This study introduces a novel framework, “Comprehensive Optimization and Refinement through Ensemble Fusion in Domain Adaptation for Person Re-identification (CORE-ReID)”, to address an Unsupervised Domain Adaptation (UDA) for Person Re-identification (ReID). The framework utilizes CycleGAN to generate diverse data that harmonize differences in image characteristics from different camera sources in the pre-training stage. In the fine-tuning stage, based on a pair of teacher–student networks, the framework integrates multi-view features for multi-level clustering to derive diverse pseudo-labels. A learnable Ensemble Fusion component that focuses on fine grained local information within global features is introduced to enhance learning comprehensiveness and avoid ambiguity associated with multiple pseudo-labels. Experimental results on three common UDAs in Person ReID demonstrated significant performance gains over state-of-the-art approaches. Additional enhancements, such as Efficient Channel Attention Block and Bidirectional Mean Feature Normalization mitigate deviation effects and the adaptive fusion of global and local features using the ResNet-based model, further strengthening the framework. The proposed framework ensures clarity in fusion features, avoids ambiguity, and achieves high accuracy in terms of Mean Average Precision, Top-1, Top-5, and Top-10, positioning it as an advanced and effective solution for UDA in Person ReID.","url_abs":"https://www.mdpi.com/2674-113X/3/2/12","url_pdf":"https://www.mdpi.com/2674-113X/3/2/12","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"core-reid-comprehensive-optimization-and","repo_url":"https://github.com/TrinhQuocNguyen/CORE-ReID","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":0,"framework":"pytorch","reach":null}],"tasks":[{"task_slug":"domain-adaptation","task_name":"Domain Adaptation"},{"task_slug":"person-re-identification","task_name":"Person Re-Identification"},{"task_slug":"unsupervised-domain-adaptation","task_name":"Unsupervised Domain Adaptation"},{"task_slug":"unsupervised-person-re-identification","task_name":"Unsupervised Person Re-Identification"}],"methods":[{"method_slug":"1x1-convolution","method_name":"1x1 Convolution"},{"method_slug":"average-pooling","method_name":"Average Pooling"},{"method_slug":"batch-normalization","method_name":"Batch Normalization"},{"method_slug":"convolution","method_name":"Convolution"},{"method_slug":"cycle-consistency-loss","method_name":"Cycle Consistency Loss"},{"method_slug":"ecanet","method_name":"Efficient Channel Attention"},{"method_slug":"gan-least-squares-loss","method_name":"GAN Least Squares Loss"},{"method_slug":"global-average-pooling","method_name":"Global Average Pooling"},{"method_slug":"instance-normalization","method_name":"Instance Normalization"},{"method_slug":"patchgan","method_name":"PatchGAN"},{"method_slug":"relu","method_name":"ReLU"},{"method_slug":"residual-block","method_name":"Residual Block"},{"method_slug":"residual-connection","method_name":"Residual Connection"},{"method_slug":"sigmoid-activation","method_name":"Sigmoid Activation"},{"method_slug":"tanh-activation","method_name":"Tanh Activation"}],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/unsupervised-domain-adaptation-on-cuhk03-to","task":"Unsupervised Domain Adaptation","dataset":"CUHK03 to MSMT","model":"CORE-ReID","rank_in_archive_order":2,"of":7,"metrics":{"R1":"67.3","R10":"83.1","R5":"79.0","mAP":"40.4"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-domain-adaptation-on-cuhk03-to-1","task":"Unsupervised Domain Adaptation","dataset":"CUHK03 to Market","model":"CORE-ReID","rank_in_archive_order":2,"of":9,"metrics":{"R1":"93.6","R10":"98.7","R5":"97.3","mAP":"83.6"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-domain-adaptation-on-duke-to-1","task":"Unsupervised Domain Adaptation","dataset":"Duke to 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Duke","model":"CORE-ReID","rank_in_archive_order":1,"of":25,"metrics":{"mAP":"74.8","rank-1":"84.8","rank-10":"94.4","rank-5":"92.4"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-domain-adaptation-on-market-to-1","task":"Unsupervised Domain Adaptation","dataset":"Market to MSMT","model":"CORE-ReID","rank_in_archive_order":2,"of":17,"metrics":{"mAP":"41.9","rank-1":"69.5","rank-10":"84.4","rank-5":"80.3"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-person-re-identification-on-3","task":"Unsupervised Person Re-Identification","dataset":"DukeMTMC-reID->MSMT17","model":"CORE-ReID","rank_in_archive_order":1,"of":7,"metrics":{"Rank-1":"72.2","Rank-10":"86.3","Rank-5":"82.9","mAP":"45.2"},"uses_additional_data":false},{"leaderboard":"/sota/unsupervised-person-re-identification-on-1","task":"Unsupervised Person 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