{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T05:02:54Z","timestamp":1750309374263,"version":"3.41.0"},"publisher-location":"New York, NY, USA","reference-count":23,"publisher":"ACM","license":[{"start":{"date-parts":[[2024,6,21]],"date-time":"2024-06-21T00:00:00Z","timestamp":1718928000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.acm.org\/publications\/policies\/copyright_policy#Background"}],"content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2024,6,21]]},"DOI":"10.1145\/3690407.3690550","type":"proceedings-article","created":{"date-parts":[[2024,10,24]],"date-time":"2024-10-24T18:55:28Z","timestamp":1729796128000},"page":"861-867","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A Lightweight Approach to Optimizing Computational Efficiency in Multi-source Domain Adaptation for Pedestrian Re-identification"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5289-6863","authenticated-orcid":false,"given":"Xiaofeng","family":"Zhang","sequence":"first","affiliation":[{"name":"Beijing Institute of Technology, Zhuhai, Zhuhai, Guangdong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-4791-9984","authenticated-orcid":false,"given":"Jia","family":"He","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology, Zhuhai, Zhuhai, Guangdong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4382-4988","authenticated-orcid":false,"given":"Tong","family":"Xu","sequence":"additional","affiliation":[{"name":"Harbin Engineering University, Harbin, Heilongjiang, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-4039-3985","authenticated-orcid":false,"given":"Mingchao","family":"Zhu","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology, Zhuhai, Zhuhai, Guangdong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2912-8994","authenticated-orcid":false,"given":"Kejun","family":"Wang","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology, Zhuhai, Zhuhai, Guangdong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-7379-4901","authenticated-orcid":false,"given":"Bo","family":"Jiang","sequence":"additional","affiliation":[{"name":"SGSG Science &amp; Technology Co.,Ltd., Zhuhai, Guangdong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0001-8960-2272","authenticated-orcid":false,"given":"Xia","family":"Liu","sequence":"additional","affiliation":[{"name":"Beijing Institute of Technology, Zhuhai, Zhuhai, Guangdong, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"320","published-online":{"date-parts":[[2024,10,24]]},"reference":[{"key":"e_1_3_3_1_1_2","doi-asserted-by":"crossref","unstructured":"Jahan Meskat et al. \"Unsupervised Person Re-Identification: A Review of Recent Works.\" Neurocomputing (2023): 127193.","DOI":"10.1016\/j.neucom.2023.127193"},{"key":"e_1_3_3_1_2_2","doi-asserted-by":"crossref","unstructured":"Zahra Asmat et al. \"Person re-identification: A retrospective on domain specific open challenges and future trends.\" Pattern Recognition (2023): 109669.","DOI":"10.1016\/j.patcog.2023.109669"},{"key":"e_1_3_3_1_3_2","doi-asserted-by":"publisher","DOI":"10.1109\/TIV.2024.3350669"},{"key":"e_1_3_3_1_4_2","volume-title":"An entropy-based pruning method for cnn compression[J]. arXiv preprint arXiv:1706.05791","author":"Luo J H","year":"2017","unstructured":"Luo J H, Wu J. An entropy-based pruning method for cnn compression[J]. arXiv preprint arXiv:1706.05791, 2017."},{"key":"e_1_3_3_1_5_2","doi-asserted-by":"publisher","DOI":"10.1109\/TNNLS.2021.3084206"},{"volume-title":"Simple and deterministic matrix sketching[C]\/\/Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. 2013: 581-588","author":"Liberty E.","key":"e_1_3_3_1_6_2","unstructured":"Liberty E. Simple and deterministic matrix sketching[C]\/\/Proceedings of the 19th ACM SIGKDD international conference on Knowledge discovery and data mining. 2013: 581-588."},{"key":"e_1_3_3_1_7_2","doi-asserted-by":"crossref","unstructured":"Luo J H Wu J Lin W. Thinet: A filter level pruning method for deep neural network compression[C]\/\/Proceedings of the IEEE international conference on computer vision. 2017: 5058-5066.","DOI":"10.1109\/ICCV.2017.541"},{"key":"e_1_3_3_1_8_2","doi-asserted-by":"crossref","unstructured":"Chavan A Shen Z Liu Z et al. Vision transformer slimming: Multi-dimension searching in continuous optimization space[C]\/\/Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition.2022:4931-4941.","DOI":"10.1109\/CVPR52688.2022.00488"},{"key":"e_1_3_3_1_9_2","first-page":"9010","article-title":"Savit: Structure-aware vision transformer pruning via collaborative optimization","volume":"35","author":"Zheng","year":"2022","unstructured":"Zheng, Chuanyang, et al. \"Savit: Structure-aware vision transformer pruning via collaborative optimization.\" Advances in Neural Information Processing Systems 35 (2022): 9010-9023.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_3_1_10_2","volume-title":"Depgraph: Towards any structural pruning.\" Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Fang","year":"2023","unstructured":"Fang, Gongfan, et al. \"Depgraph: Towards any structural pruning.\" Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2023."},{"key":"e_1_3_3_1_11_2","doi-asserted-by":"crossref","unstructured":"Wu H Xiao B Codella N et al. Cvt: Introducing convolutions to vision transformers[C]\/\/Proceedings of the IEEE\/CVF International Conference on Computer Vision. 2021: 22-31.","DOI":"10.1109\/ICCV48922.2021.00009"},{"key":"e_1_3_3_1_12_2","doi-asserted-by":"crossref","unstructured":"Guo J Han K Wu H et al. Cmt: Convolutional neural networks meet vision transformers[C]\/\/Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2022: 12175-12185.","DOI":"10.1109\/CVPR52688.2022.01186"},{"volume-title":"PMLR","author":"D'Ascoli S","key":"e_1_3_3_1_13_2","unstructured":"D'Ascoli S, Touvron H, Leavitt M L, et al. Convit: Improving vision transformers with soft convolutional inductive biases[C]\/\/International Conference on Machine Learning. PMLR, 2021: 2286-2296."},{"key":"e_1_3_3_1_14_2","volume-title":"Mobilevit: light-weight, general-purpose, and mobile-friendly vision transformer[J]. arXiv preprint arXiv:2110.02178","author":"Mehta S","year":"2021","unstructured":"Mehta S, Rastegari M. Mobilevit: light-weight, general-purpose, and mobile-friendly vision transformer[J]. arXiv preprint arXiv:2110.02178, 2021."},{"key":"e_1_3_3_1_15_2","doi-asserted-by":"crossref","unstructured":"Chen Y Dai X Chen D et al. Mobile-former: Bridging mobilenet and transformer[C]\/\/Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2022: 5270-5279.","DOI":"10.1109\/CVPR52688.2022.00520"},{"key":"e_1_3_3_1_16_2","first-page":"12934","article-title":"Efficient former: Vision transformers at mobilenet speed[J]","volume":"35","author":"Li Y","year":"2022","unstructured":"Li Y, Yuan G, Wen Y, et al. Efficient former: Vision transformers at mobilenet speed[J]. Advances in Neural Information Processing Systems, 2022, 35: 12934-12949.","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_3_1_17_2","volume-title":"Repvit: Revisiting mobile cnn from vit perspective.\" Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition","author":"Wang","year":"2024","unstructured":"Wang, Ao, et al. \"Repvit: Revisiting mobile cnn from vit perspective.\" Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition. 2024."},{"key":"e_1_3_3_1_18_2","doi-asserted-by":"crossref","unstructured":"Zheng L Shen L Tian L et al. Scalable person re-identification: A benchmark[C]\/\/Proceedings of the IEEE international conference on computer vision. 2015: 1116-1124.","DOI":"10.1109\/ICCV.2015.133"},{"volume-title":"Proceedings, Part II. Cham: Springer International Publishing","author":"Ristani E","key":"e_1_3_3_1_19_2","unstructured":"Ristani E, Solera F, Zou R, et al. Performance measures and a data set for multi-target, multi-camera tracking[C]\/\/Computer Vision\u2013ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8-10 and 15-16, 2016, Proceedings, Part II. Cham: Springer International Publishing, 2016: 17-35."},{"key":"e_1_3_3_1_20_2","doi-asserted-by":"crossref","unstructured":"Li W Zhao R Xiao T et al. Deepreid: Deep filter pairing neural network for person re-identification[C]\/\/Proceedings of the IEEE conference on computer vision and pattern recognition. 2014: 152-159.","DOI":"10.1109\/CVPR.2014.27"},{"key":"e_1_3_3_1_21_2","doi-asserted-by":"crossref","unstructured":"Wei L Zhang S Gao W et al. Person transfer gan to bridge domain gap for person re-identification[C]\/\/Proceedings of the IEEE conference on computer vision and pattern recognition. 2018: 79-88.","DOI":"10.1109\/CVPR.2018.00016"},{"key":"e_1_3_3_1_22_2","volume-title":"Unsupervised domain adaptation through synthesis for person re-identification[C]\/\/2020 IEEE International Conference on Multimedia and Expo (ICME)","author":"Xiang S","year":"2020","unstructured":"Xiang S, Fu Y, You G, et al. Unsupervised domain adaptation through synthesis for person re-identification[C]\/\/2020 IEEE International Conference on Multimedia and Expo (ICME). IEEE, 2020: 1-6."},{"key":"e_1_3_3_1_23_2","doi-asserted-by":"crossref","unstructured":"He J Zhang X Xu T et al. Unsupervised Multi-Source Domain Adaptation for Pedestrian Re-identification?? A Study in Noise-Resilient Learning [C]\/\/Proceedings of the 2024 6th International Conference on Image Processing and Machine Vision. 2024: 62-67.","DOI":"10.1145\/3645259.3645270"}],"event":{"name":"CAIBDA 2024: 2024 4th International Conference on Artificial Intelligence, Big Data and Algorithms","acronym":"CAIBDA 2024","location":"Zhengzhou China"},"container-title":["Proceedings of the 2024 4th International Conference on Artificial Intelligence, Big Data and Algorithms"],"original-title":[],"link":[{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3690407.3690550","content-type":"unspecified","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/dl.acm.org\/doi\/pdf\/10.1145\/3690407.3690550","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,19]],"date-time":"2025-06-19T00:06:21Z","timestamp":1750291581000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3690407.3690550"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,6,21]]},"references-count":23,"alternative-id":["10.1145\/3690407.3690550","10.1145\/3690407"],"URL":"https:\/\/doi.org\/10.1145\/3690407.3690550","relation":{},"subject":[],"published":{"date-parts":[[2024,6,21]]},"assertion":[{"value":"2024-10-24","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}