{"ID":2874547,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2509.04050","arxiv_id":"2509.04050","title":"A Re-ranking Method using K-nearest Weighted Fusion for Person Re-identification","abstract":"In person re-identification, re-ranking is a crucial step to enhance the overall accuracy by refining the initial ranking of retrieved results. Previous studies have mainly focused on features from single-view images, which can cause view bias and issues like pose variation, viewpoint changes, and occlusions. Using multi-view features to present a person can help reduce view bias. In this work, we present an efficient re-ranking method that generates multi-view features by aggregating neighbors' features using K-nearest Weighted Fusion (KWF) method. Specifically, we hypothesize that features extracted from re-identification models are highly similar when representing the same identity. Thus, we select K neighboring features in an unsupervised manner to generate multi-view features. Additionally, this study explores the weight selection strategies during feature aggregation, allowing us to identify an effective strategy. Our re-ranking approach does not require model fine-tuning or extra annotations, making it applicable to large-scale datasets. We evaluate our method on the person re-identification datasets Market1501, MSMT17, and Occluded-DukeMTMC. The results show that our method significantly improves Rank@1 and mAP when re-ranking the top M candidates from the initial ranking results. Specifically, compared to the initial results, our re-ranking method achieves improvements of 9.8%/22.0% in Rank@1 on the challenging datasets: MSMT17 and Occluded-DukeMTMC, respectively. Furthermore, our approach demonstrates substantial enhancements in computational efficiency compared to other re-ranking methods. Code is available at https://github.com/chequanghuy/Enhancing-Person-Re-Identification-via-UFFM-and-AMC.","short_abstract":"In person re-identification, re-ranking is a crucial step to enhance the overall accuracy by refining the initial ranking of retrieved results. Previous studies have mainly focused on features from single-view images, which can cause view bias and issues like pose variation, viewpoint changes, and occlusions. Using mul...","url_abs":"https://arxiv.org/abs/2509.04050","url_pdf":"https://arxiv.org/pdf/2509.04050v2","authors":"[\"Huy Che\",\"Le-Chuong Nguyen\",\"Gia-Nghia Tran\",\"Dinh-Duy Phan\",\"Vinh-Tiep Nguyen\"]","published":"2025-09-04T09:29:25Z","proceeding":"cs.CV","tasks":"[\"cs.CV\"]","methods":"[]","has_code":false,"code_links":[{"ID":610155,"CreatedAt":"2026-06-01T04:54:23.091178241Z","UpdatedAt":"2026-06-01T04:54:23.091178241Z","DeletedAt":null,"paper_id":2874547,"paper_url":"https://arxiv.org/abs/2509.04050","paper_title":"A Re-ranking Method using K-nearest Weighted Fusion for Person Re-identification","repo_url":"https://github.com/chequanghuy/Enhancing-Person-Re-Identification-via-UFFM-and-AMC","is_official":false,"mentioned_in_paper":false,"mentioned_in_github":true,"github_stars":0}]}