{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T00:58:42Z","timestamp":1774400322532,"version":"3.50.1"},"reference-count":0,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2024,6,3]],"date-time":"2024-06-03T00:00:00Z","timestamp":1717372800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Software"],"abstract":"<jats:p>This study introduces a novel framework, \u201cComprehensive Optimization and Refinement through Ensemble Fusion in Domain Adaptation for Person Re-identification (CORE-ReID)\u201d, 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\u2013student 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.<\/jats:p>","DOI":"10.3390\/software3020012","type":"journal-article","created":{"date-parts":[[2024,6,3]],"date-time":"2024-06-03T12:29:47Z","timestamp":1717417787000},"page":"227-249","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["CORE-ReID: Comprehensive Optimization and Refinement through Ensemble Fusion in Domain Adaptation for Person Re-Identification"],"prefix":"10.3390","volume":"3","author":[{"given":"Trinh Quoc","family":"Nguyen","sequence":"first","affiliation":[{"name":"Graduate School of Software and Information Science, Iwate Prefectural University, Takizawa-shi 020-0693, Iwate, Japan"},{"name":"CyberCore Co., Ltd., Morioka-shi 020-0045, Iwate, Japan"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8545-6434","authenticated-orcid":false,"given":"Oky Dicky Ardiansyah","family":"Prima","sequence":"additional","affiliation":[{"name":"Graduate School of Software and Information Science, Iwate Prefectural University, Takizawa-shi 020-0693, Iwate, Japan"}]},{"given":"Katsuyoshi","family":"Hotta","sequence":"additional","affiliation":[{"name":"CyberCore Co., Ltd., Morioka-shi 020-0045, Iwate, Japan"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,3]]},"container-title":["Software"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2674-113X\/3\/2\/12\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T14:53:06Z","timestamp":1760107986000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2674-113X\/3\/2\/12"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,3]]},"references-count":0,"journal-issue":{"issue":"2","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["software3020012"],"URL":"https:\/\/doi.org\/10.3390\/software3020012","relation":{},"ISSN":["2674-113X"],"issn-type":[{"value":"2674-113X","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,6,3]]}}}