{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,13]],"date-time":"2026-01-13T04:08:51Z","timestamp":1768277331520,"version":"3.49.0"},"publisher-location":"Singapore","reference-count":34,"publisher":"Springer Nature Singapore","isbn-type":[{"value":"9789819985548","type":"print"},{"value":"9789819985555","type":"electronic"}],"license":[{"start":{"date-parts":[[2023,12,28]],"date-time":"2023-12-28T00:00:00Z","timestamp":1703721600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,12,28]],"date-time":"2023-12-28T00:00:00Z","timestamp":1703721600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024]]},"DOI":"10.1007\/978-981-99-8555-5_21","type":"book-chapter","created":{"date-parts":[[2023,12,27]],"date-time":"2023-12-27T07:02:36Z","timestamp":1703660556000},"page":"265-277","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Ped-Mix: Mix Pedestrians for\u00a0Occluded Person Re-identification"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8785-7311","authenticated-orcid":false,"given":"Shang","family":"Gao","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9813-3164","authenticated-orcid":false,"given":"Chenyang","family":"Yu","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1206-1444","authenticated-orcid":false,"given":"Pingping","family":"Zhang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6668-9758","authenticated-orcid":false,"given":"Huchuan","family":"Lu","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,12,28]]},"reference":[{"key":"21_CR1","doi-asserted-by":"publisher","unstructured":"Abnar, S., Zuidema, W.: Quantifying attention flow in transformers. In: Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, January 2020. https:\/\/doi.org\/10.18653\/v1\/2020.acl-main.385","DOI":"10.18653\/v1\/2020.acl-main.385"},{"key":"21_CR2","doi-asserted-by":"crossref","unstructured":"Cheng, X., Jia, M., Wang, Q., Zhang, J.: More is better: multi-source dynamic parsing attention for occluded person re-identification. In: Proceedings of the 30th ACM International Conference on Multimedia, pp. 6840\u20136849 (2022)","DOI":"10.1145\/3503161.3547819"},{"key":"21_CR3","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: 2009 IEEE Conference on Computer Vision and Pattern Recognition, pp. 248\u2013255. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"21_CR4","doi-asserted-by":"crossref","unstructured":"Deng, J., Guo, J., Xue, N., Zafeiriou, S.: Arcface: additive angular margin loss for deep face recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4690\u20134699 (2019)","DOI":"10.1109\/CVPR.2019.00482"},{"key":"21_CR5","unstructured":"DeVries, T., Taylor, G.W.: Improved regularization of convolutional neural networks with cutout. arXiv preprint arXiv:1708.04552 (2017)"},{"key":"21_CR6","unstructured":"Dosovitskiy, A., et al.: An image is worth 16x16 words: transformers for image recognition at scale. arxiv 2020. arXiv preprint arXiv:2010.11929 (2010)"},{"key":"21_CR7","doi-asserted-by":"crossref","unstructured":"Gao, S., Wang, J., Lu, H., Liu, Z.: Pose-guided visible part matching for occluded person ReID. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11744\u201311752 (2020)","DOI":"10.1109\/CVPR42600.2020.01176"},{"key":"21_CR8","unstructured":"Gray, D., Brennan, S., Tao, H.: Evaluating appearance models for recognition, reacquisition, and tracking. In: Proc. IEEE International Workshop on Performance Evaluation for Tracking and Surveillance (PETS), vol. 3, pp. 1\u20137 (2007)"},{"key":"21_CR9","doi-asserted-by":"publisher","unstructured":"He, K., Chen, X., Xie, S., Li, Y., Dollar, P., Girshick, R.: Masked autoencoders are scalable vision learners. In: 2022 IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), June 2022. https:\/\/doi.org\/10.1109\/cvpr52688.2022.01553","DOI":"10.1109\/cvpr52688.2022.01553"},{"key":"21_CR10","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"21_CR11","doi-asserted-by":"crossref","unstructured":"He, L., Liang, J., Li, H., Sun, Z.: Deep spatial feature reconstruction for partial person re-identification: alignment-free approach. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7073\u20137082 (2018)","DOI":"10.1109\/CVPR.2018.00739"},{"key":"21_CR12","doi-asserted-by":"crossref","unstructured":"He, S., Luo, H., Wang, P., Wang, F., Li, H., Jiang, W.: TransReID: transformer-based object re-identification. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 15013\u201315022 (2021)","DOI":"10.1109\/ICCV48922.2021.01474"},{"key":"21_CR13","unstructured":"Hermans, A., Beyer, L., Leibe, B.: In defense of the triplet loss for person re-identification. arXiv preprint arXiv:1703.07737 (2017)"},{"issue":"9","key":"21_CR14","first-page":"4894","volume":"44","author":"R Hou","year":"2021","unstructured":"Hou, R., Ma, B., Chang, H., Gu, X., Shan, S., Chen, X.: Feature completion for occluded person re-identification. IEEE Trans. Pattern Anal. Mach. Intell. 44(9), 4894\u20134912 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"21_CR15","doi-asserted-by":"crossref","unstructured":"Huang, H., Li, D., Zhang, Z., Chen, X., Huang, K.: Adversarially occluded samples for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5098\u20135107 (2018)","DOI":"10.1109\/CVPR.2018.00535"},{"key":"21_CR16","doi-asserted-by":"crossref","unstructured":"Jia, M., Cheng, X., Lu, S., Zhang, J.: Learning disentangled representation implicitly via transformer for occluded person re-identification. IEEE Trans. Multimedia (2022)","DOI":"10.1109\/TMM.2022.3141267"},{"key":"21_CR17","doi-asserted-by":"crossref","unstructured":"Li, Y., He, J., Zhang, T., Liu, X., Zhang, Y., Wu, F.: Diverse part discovery: occluded person re-identification with part-aware transformer. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2898\u20132907 (2021)","DOI":"10.1109\/CVPR46437.2021.00292"},{"key":"21_CR18","doi-asserted-by":"crossref","unstructured":"Miao, J., Wu, Y., Liu, P., Ding, Y., Yang, Y.: Pose-guided feature alignment for occluded person re-identification. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 542\u2013551 (2019)","DOI":"10.1109\/ICCV.2019.00063"},{"key":"21_CR19","doi-asserted-by":"crossref","unstructured":"Somers, V., De Vleeschouwer, C., Alahi, A.: Body part-based representation learning for occluded person re-identification. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 1613\u20131623 (2023)","DOI":"10.1109\/WACV56688.2023.00166"},{"key":"21_CR20","doi-asserted-by":"crossref","unstructured":"Tan, L., Dai, P., Ji, R., Wu, Y.: Dynamic prototype mask for occluded person re-identification. In: Proceedings of the 30th ACM International Conference on Multimedia, pp. 531\u2013540 (2022)","DOI":"10.1145\/3503161.3547764"},{"key":"21_CR21","doi-asserted-by":"crossref","unstructured":"Wang, G., et al.: High-order information matters: learning relation and topology for occluded person re-identification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 6449\u20136458 (2020)","DOI":"10.1109\/CVPR42600.2020.00648"},{"issue":"10","key":"21_CR22","doi-asserted-by":"publisher","first-page":"3349","DOI":"10.1109\/TPAMI.2020.2983686","volume":"43","author":"J Wang","year":"2020","unstructured":"Wang, J., et al.: Deep high-resolution representation learning for visual recognition. IEEE Trans. Pattern Anal. Mach. Intell. 43(10), 3349\u20133364 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"21_CR23","doi-asserted-by":"crossref","unstructured":"Wang, T., Liu, H., Song, P., Guo, T., Shi, W.: Pose-guided feature disentangling for occluded person re-identification based on transformer. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 2540\u20132549 (2022)","DOI":"10.1609\/aaai.v36i3.20155"},{"key":"21_CR24","doi-asserted-by":"crossref","unstructured":"Wang, Z., Zhu, F., Tang, S., Zhao, R., He, L., Song, J.: Feature erasing and diffusion network for occluded person re-identification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4754\u20134763 (2022)","DOI":"10.1109\/CVPR52688.2022.00471"},{"key":"21_CR25","doi-asserted-by":"crossref","unstructured":"Wei, L., Zhang, S., Gao, W., Tian, Q.: Person transfer GAN to bridge domain gap for person re-identification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 79\u201388 (2018)","DOI":"10.1109\/CVPR.2018.00016"},{"key":"21_CR26","doi-asserted-by":"crossref","unstructured":"Xia, J., Tan, L., Dai, P., Zhao, M., Wu, Y., Ji, R.: Attention disturbance and dual-path constraint network for occluded person re-identification. arXiv preprint arXiv:2303.10976 (2023)","DOI":"10.1609\/aaai.v38i6.28437"},{"key":"21_CR27","unstructured":"Ye, Y., Zhou, H., Yu, J., Hu, Q., Yang, W.: Dynamic feature pruning and consolidation for occluded person re-identification. arXiv preprint arXiv:2211.14742 (2022)"},{"key":"21_CR28","doi-asserted-by":"crossref","unstructured":"Yun, S., Han, D., Oh, S.J., Chun, S., Choe, J., Yoo, Y.: Cutmix: regularization strategy to train strong classifiers with localizable features. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6023\u20136032 (2019)","DOI":"10.1109\/ICCV.2019.00612"},{"key":"21_CR29","doi-asserted-by":"publisher","first-page":"4212","DOI":"10.1109\/TIP.2021.3070182","volume":"30","author":"C Zhao","year":"2021","unstructured":"Zhao, C., Lv, X., Dou, S., Zhang, S., Wu, J., Wang, L.: Incremental generative occlusion adversarial suppression network for person ReID. IEEE Trans. Image Process. 30, 4212\u20134224 (2021)","journal-title":"IEEE Trans. Image Process."},{"key":"21_CR30","doi-asserted-by":"crossref","unstructured":"Zheng, L., Shen, L., Tian, L., Wang, S., Wang, J., Tian, Q.: Scalable person re-identification: a benchmark. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 1116\u20131124 (2015)","DOI":"10.1109\/ICCV.2015.133"},{"key":"21_CR31","doi-asserted-by":"crossref","unstructured":"Zheng, Z., Zheng, L., Yang, Y.: Unlabeled samples generated by GAN improve the person re-identification baseline in vitro. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 3754\u20133762 (2017)","DOI":"10.1109\/ICCV.2017.405"},{"key":"21_CR32","doi-asserted-by":"crossref","unstructured":"Zhong, Z., Zheng, L., Kang, G., Li, S., Yang, Y.: Random erasing data augmentation. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 34, pp. 13001\u201313008 (2020)","DOI":"10.1609\/aaai.v34i07.7000"},{"key":"21_CR33","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"346","DOI":"10.1007\/978-3-030-58580-8_21","volume-title":"Computer Vision \u2013 ECCV 2020","author":"K Zhu","year":"2020","unstructured":"Zhu, K., Guo, H., Liu, Z., Tang, M., Wang, J.: Identity-guided human semantic parsing for person re-identification. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020, Part III. LNCS, vol. 12348, pp. 346\u2013363. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58580-8_21"},{"key":"21_CR34","doi-asserted-by":"crossref","unstructured":"Zhuo, J., Chen, Z., Lai, J., Wang, G.: Occluded person re-identification. In: 2018 IEEE International Conference on Multimedia and Expo (ICME), pp. 1\u20136. IEEE (2018)","DOI":"10.1109\/ICME.2018.8486568"}],"container-title":["Lecture Notes in Computer Science","Pattern Recognition and Computer Vision"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-8555-5_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,6]],"date-time":"2024-11-06T22:08:57Z","timestamp":1730930937000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-8555-5_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,12,28]]},"ISBN":["9789819985548","9789819985555"],"references-count":34,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-8555-5_21","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,12,28]]},"assertion":[{"value":"28 December 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Chinese Conference on Pattern Recognition and Computer Vision (PRCV)","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Xiamen","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"China","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"13 October 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15 October 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"6","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/prcv2023.xmu.edu.cn\/","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Double-blind","order":1,"name":"type","label":"Type","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"Microsoft CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"1420","order":3,"name":"number_of_submissions_sent_for_review","label":"Number of Submissions Sent for Review","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"532","order":4,"name":"number_of_full_papers_accepted","label":"Number of Full Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"0","order":5,"name":"number_of_short_papers_accepted","label":"Number of Short Papers Accepted","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"37% - The value is computed by the equation \"Number of Full Papers Accepted \/ Number of Submissions Sent for Review * 100\" and then rounded to a whole number.","order":6,"name":"acceptance_rate_of_full_papers","label":"Acceptance Rate of Full Papers","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3,78","order":7,"name":"average_number_of_reviews_per_paper","label":"Average Number of Reviews per Paper","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"3,69","order":8,"name":"average_number_of_papers_per_reviewer","label":"Average Number of Papers per Reviewer","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"No","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}