{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T02:14:37Z","timestamp":1760148877479,"version":"build-2065373602"},"reference-count":37,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2023,6,14]],"date-time":"2023-06-14T00:00:00Z","timestamp":1686700800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Recently, hybrid Convolution-Transformer architectures have become popular due to their ability to capture both local and global image features and the advantage of lower computational cost over pure Transformer models. However, directly embedding a Transformer can result in the loss of convolution-based features, particularly fine-grained features. Therefore, using these architectures as the backbone of a re-identification task is not an effective approach. To address this challenge, we propose a feature fusion gate unit that dynamically adjusts the ratio of local and global features. The feature fusion gate unit fuses the convolution and self-attentive branches of the network with dynamic parameters based on the input information. This unit can be integrated into different layers or multiple residual blocks, which will have varying effects on the accuracy of the model. Using feature fusion gate units, we propose a simple and portable model called the dynamic weighting network or DWNet, which supports two backbones, ResNet and OSNet, called DWNet-R and DWNet-O, respectively. DWNet significantly improves re-identification performance over the original baseline, while maintaining reasonable computational consumption and number of parameters. Finally, our DWNet-R achieves an mAP of 87.53%, 79.18%, 50.03%, on the Market1501, DukeMTMC-reID, and MSMT17 datasets. Our DWNet-O achieves an mAP of 86.83%, 78.68%, 55.66%, on the Market1501, DukeMTMC-reID, and MSMT17 datasets.<\/jats:p>","DOI":"10.3390\/s23125579","type":"journal-article","created":{"date-parts":[[2023,6,15]],"date-time":"2023-06-15T02:28:56Z","timestamp":1686796136000},"page":"5579","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Dynamic Weighting Network for Person Re-Identification"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-6630-3492","authenticated-orcid":false,"given":"Guang","family":"Li","sequence":"first","affiliation":[{"name":"School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China"},{"name":"Yangzhong Intelligent Electric Research Center, North China Electric Power University, Yangzhong 212211, China"}]},{"given":"Peng","family":"Liu","sequence":"additional","affiliation":[{"name":"Yangzhong Intelligent Electric Research Center, North China Electric Power University, Yangzhong 212211, China"},{"name":"School of Electronic and Information Engineering, Changchun University of Science and Technology, Changchun 130012, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-4950-0266","authenticated-orcid":false,"given":"Xiaofan","family":"Cao","sequence":"additional","affiliation":[{"name":"School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China"},{"name":"Yangzhong Intelligent Electric Research Center, North China Electric Power University, Yangzhong 212211, China"}]},{"given":"Chunguang","family":"Liu","sequence":"additional","affiliation":[{"name":"Yangzhong Intelligent Electric Research Center, North China Electric Power University, Yangzhong 212211, China"}]}],"member":"1968","published-online":{"date-parts":[[2023,6,14]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Khorramshahi, P., Peri, N., Chen, J.C., and Chellappa, R. 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