{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,25]],"date-time":"2025-03-25T16:12:07Z","timestamp":1742919127231,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":15,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819970247"},{"type":"electronic","value":"9789819970254"}],"license":[{"start":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T00:00:00Z","timestamp":1699574400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T00:00:00Z","timestamp":1699574400000},"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\/978-981-99-7025-4_21","type":"book-chapter","created":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T00:02:57Z","timestamp":1699574577000},"page":"240-246","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Credible Dual-X Modality Learning for\u00a0Visible and\u00a0Infrared Person Re-Identification"],"prefix":"10.1007","author":[{"given":"Wen","family":"Zhang","sequence":"first","affiliation":[]},{"given":"Zili","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Lu","family":"Gong","sequence":"additional","affiliation":[]},{"given":"Jiali","family":"Zhang","sequence":"additional","affiliation":[]},{"given":"Mingkang","family":"Li","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,11,10]]},"reference":[{"key":"21_CR1","doi-asserted-by":"crossref","unstructured":"Dai, P., Ji, R., Wang, H., Wu, Q., Huang, Y.: Cross-modality person re-identification with generative adversarial training. In: IJCAI, p. 6 (2018)","DOI":"10.24963\/ijcai.2018\/94"},{"key":"21_CR2","unstructured":"Han, Z., Zhang, C., Fu, H., Zhou, J.T.: Trusted multi-view classification. arXiv preprint arXiv:2102.02051 (2021)"},{"key":"21_CR3","doi-asserted-by":"crossref","unstructured":"Hao, X., Zhao, S., Ye, M., Shen, J.: Cross-modality person re-identification via modality confusion and center aggregation. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 16403\u201316412 (2021)","DOI":"10.1109\/ICCV48922.2021.01609"},{"key":"21_CR4","doi-asserted-by":"crossref","unstructured":"Huang, Z., Liu, J., Li, L., Zheng, K., Zha, Z.J.: Modality-adaptive mixup and invariant decomposition for rgb-infrared person re-identification. arXiv preprint arXiv:2203.01735 (2022)","DOI":"10.1609\/aaai.v36i1.19987"},{"key":"21_CR5","doi-asserted-by":"crossref","unstructured":"Li, D., Wei, X., Hong, X., Gong, Y.: Infrared-visible cross-modal person re-identification with an x modality. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 4610\u20134617 (2020)","DOI":"10.1609\/aaai.v34i04.5891"},{"key":"21_CR6","doi-asserted-by":"crossref","unstructured":"Lu, Y., et al.: Cross-modality person re-identification with shared-specific feature transfer. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13379\u201313389 (2020)","DOI":"10.1109\/CVPR42600.2020.01339"},{"key":"21_CR7","unstructured":"Sensoy, M., Kaplan, L., Kandemir, M.: Evidential deep learning to quantify classification uncertainty. In: Advances in Neural Information Processing Systems, pp. 3183\u20133193 (2018)"},{"key":"21_CR8","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"501","DOI":"10.1007\/978-3-030-01225-0_30","volume-title":"Computer Vision \u2013 ECCV 2018","author":"Y Sun","year":"2018","unstructured":"Sun, Y., Zheng, L., Yang, Y., Tian, Q., Wang, S.: Beyond part models: person retrieval with refined part pooling (and a strong convolutional baseline). In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11208, pp. 501\u2013518. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01225-0_30"},{"key":"21_CR9","doi-asserted-by":"crossref","unstructured":"Tan, B., Song, Y., Zhong, E., Yang, Q.: Transitive transfer learning. In: Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 1155\u20131164 (2015)","DOI":"10.1145\/2783258.2783295"},{"key":"21_CR10","doi-asserted-by":"crossref","unstructured":"Wang, G., Zhang, T., Cheng, J., Liu, S., Yang, Y., Hou, Z.: Rgb-infrared cross-modality person re-identification via joint pixel and feature alignment. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 3623\u20133632 (2019)","DOI":"10.1109\/ICCV.2019.00372"},{"key":"21_CR11","doi-asserted-by":"crossref","unstructured":"Wang, Z., Wang, Z., Zheng, Y., Chuang, Y.Y., Satoh, S.: Learning to reduce dual-level discrepancy for infrared-visible person re-identification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 618\u2013626 (2019)","DOI":"10.1109\/CVPR.2019.00071"},{"key":"21_CR12","doi-asserted-by":"crossref","unstructured":"Ye, M., Lan, X., Li, J., Yuen, P.: Hierarchical discriminative learning for visible thermal person re-identification. In: Proceedings of the AAAI Conference on Artificial Intelligence, pp. 7501\u20137508 (2018)","DOI":"10.1609\/aaai.v32i1.12293"},{"key":"21_CR13","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"229","DOI":"10.1007\/978-3-030-58520-4_14","volume-title":"Computer Vision \u2013 ECCV 2020","author":"M Ye","year":"2020","unstructured":"Ye, M., Shen, J., J Crandall, D., Shao, L., Luo, J.: Dynamic dual-attentive aggregation learning for visible-infrared person re-identification. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12362, pp. 229\u2013247. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58520-4_14"},{"issue":"6","key":"21_CR14","doi-asserted-by":"publisher","first-page":"2872","DOI":"10.1109\/TPAMI.2021.3054775","volume":"44","author":"M Ye","year":"2021","unstructured":"Ye, M., Shen, J., Lin, G., Xiang, T., Shao, L., Hoi, S.C.: Deep learning for person re-identification: a survey and outlook. IEEE Trans. Pattern Anal. Mach. Intell. 44(6), 2872\u20132893 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"21_CR15","doi-asserted-by":"crossref","unstructured":"Zhang, Q., Lai, C., Liu, J., Huang, N., Han, J.: Fmcnet: feature-level modality compensation for visible-infrared person re-identification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 7349\u20137358 (2022)","DOI":"10.1109\/CVPR52688.2022.00720"}],"container-title":["Lecture Notes in Computer Science","PRICAI 2023: Trends in Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-7025-4_21","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,11,10]],"date-time":"2023-11-10T00:05:53Z","timestamp":1699574753000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-7025-4_21"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,11,10]]},"ISBN":["9789819970247","9789819970254"],"references-count":15,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-7025-4_21","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023,11,10]]},"assertion":[{"value":"10 November 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"PRICAI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Pacific Rim International Conference on Artificial Intelligence","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Jakarta","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Indonesia","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":"15 November 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19 November 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"20","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"pricai2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/www.pricai.org\/2023\/","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":"EasyChair","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"422","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":"95","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":"36","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":"23% - 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":"3.4","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":"3.1","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":"Yes","order":9,"name":"external_reviewers_involved","label":"External Reviewers Involved","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}