{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,28]],"date-time":"2025-03-28T07:59:13Z","timestamp":1743148753257,"version":"3.40.3"},"publisher-location":"Singapore","reference-count":17,"publisher":"Springer Nature Singapore","isbn-type":[{"type":"print","value":"9789819908554"},{"type":"electronic","value":"9789819908561"}],"license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"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":[[2023]]},"DOI":"10.1007\/978-981-99-0856-1_2","type":"book-chapter","created":{"date-parts":[[2023,3,9]],"date-time":"2023-03-09T11:03:36Z","timestamp":1678359816000},"page":"16-29","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Pedestrian Attribute Recognition Method Based on Multi-source Teacher Model Fusion"],"prefix":"10.1007","author":[{"given":"Zhengyan","family":"Ding","sequence":"first","affiliation":[]},{"given":"Yanfeng","family":"Shang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,3,10]]},"reference":[{"key":"2_CR1","doi-asserted-by":"crossref","unstructured":"Li, D., Chen, X., Huang, K.: Multi-attribute learning for pedestrian attribute recognition in surveillance scenarios. In: Proceedings of the 2015 Asian Conference on Pattern Recognition. Piscataway: IEEE, pp. 111\u2013115 (2015)","DOI":"10.1109\/ACPR.2015.7486476"},{"key":"2_CR2","doi-asserted-by":"crossref","unstructured":"Zeng, H., Ai, H., Zhuang, Z., et al.: Multi-task learning via co-attentive sharing for pedestrian attribute recognition. In: 2020 IEEE International Conference on Multimedia and Expo (ICME). IEEE, pp. 1\u20136 (2020)","DOI":"10.1109\/ICME46284.2020.9102757"},{"key":"2_CR3","doi-asserted-by":"crossref","unstructured":"Moghaddam, M., Charmi, M., Hassanpoor, H.: Jointly human semantic parsing and attribute recognition with feature pyramid structure in EfficientNets. IET Image Processing (2021)","DOI":"10.1049\/ipr2.12195"},{"key":"2_CR4","doi-asserted-by":"crossref","unstructured":"Liu, X., Zhao, H., Tian, M., et al.: Hydraplus-net: Attentive deep features for pedestrian analysis. In: Proceedings of the IEEE international conference on computer vision, pp. 350\u2013359 (2017)","DOI":"10.1109\/ICCV.2017.46"},{"key":"2_CR5","doi-asserted-by":"crossref","unstructured":"Deng, Y., Luo, P., Loy, C.C., et al.: Pedestrian attribute recognition at far distance. In: Proceedings of the 22nd ACM international conference on Multimedia, pp. 789\u2013792 (2014)","DOI":"10.1145\/2647868.2654966"},{"key":"2_CR6","unstructured":"Li, D., Zhang, Z., Chen, X., et al.: A richly annotated dataset for pedestrian attribute recognition (2016). arXiv preprint arXiv:1603.07054"},{"issue":"4","key":"2_CR7","doi-asserted-by":"publisher","first-page":"1575","DOI":"10.1109\/TIP.2018.2878349","volume":"28","author":"D Li","year":"2018","unstructured":"Li, D., Zhang, Z., Chen, X., et al.: A richly annotated pedestrian dataset for person retrieval in real surveillance scenarios. IEEE transactions on image processing 28(4), 1575\u20131590 (2018)","journal-title":"IEEE transactions on image processing"},{"key":"2_CR8","unstructured":"Jia, J., Huang, H., Yang, W., et al.: Rethinking of pedestrian attribute recognition: Realistic datasets with efficient method (2020). arXiv preprint arXiv:2005.11909"},{"key":"2_CR9","doi-asserted-by":"crossref","unstructured":"Lin, T.Y., Doll\u00e1r, P., Girshick, R., et al.: Feature pyramid networks for object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 2117\u20132125 (2017)","DOI":"10.1109\/CVPR.2017.106"},{"key":"2_CR10","unstructured":"Bagherinezhad, H., Horton, M., Rastegari, M., et al.: Label refinery: Improving imagenet classification through label progression (2018). arXiv preprint arXiv:1805.02641"},{"key":"2_CR11","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., et al.: 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":"2_CR12","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., et al.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE conference on computer vision and pattern recognition, 2818\u20132826 (2016)","DOI":"10.1109\/CVPR.2016.308"},{"key":"2_CR13","doi-asserted-by":"crossref","unstructured":"Li, D., Hu, J., Wang, C., et al.: Involution: Inverting the inherence of convolution for visual recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 12321\u201312330 (2021)","DOI":"10.1109\/CVPR46437.2021.01214"},{"key":"2_CR14","unstructured":"Dosovitskiy, A., Beyer, L., Kolesnikov, A., et al.: An image is worth 16x16 words: Transformers for image recognition at scale (2020). arXiv preprint arXiv:2010.11929"},{"key":"2_CR15","doi-asserted-by":"crossref","unstructured":"He, K., Chen, X., Xie, S., et al.: Masked autoencoders are scalable vision learners. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, 16000\u201316009 (2022).","DOI":"10.1109\/CVPR52688.2022.01553"},{"key":"2_CR16","unstructured":"Cui, C., Guo, R., Du, Y., et al.: Beyond Self-Supervision: A Simple Yet Effective Network Distillation Alternative to Improve Backbones (2021). arXiv preprint arXiv:2103.05959"},{"key":"2_CR17","doi-asserted-by":"crossref","unstructured":"Choi, J., Elezi, I., Lee, H.J., et al.: Active learning for deep object detection via probabilistic modeling. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10264\u201310273 (2021)","DOI":"10.1109\/ICCV48922.2021.01010"}],"container-title":["Communications in Computer and Information Science","Digital Multimedia Communications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-981-99-0856-1_2","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,3,14]],"date-time":"2023-03-14T11:12:06Z","timestamp":1678792326000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-981-99-0856-1_2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9789819908554","9789819908561"],"references-count":17,"URL":"https:\/\/doi.org\/10.1007\/978-981-99-0856-1_2","relation":{},"ISSN":["1865-0929","1865-0937"],"issn-type":[{"type":"print","value":"1865-0929"},{"type":"electronic","value":"1865-0937"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"10 March 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"IFTC","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Forum on Digital TV and Wireless Multimedia Communications","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Shanghai","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":"2022","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"8 December 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"9 December 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"19","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"iftc2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.siga.org.cn\/xshd\/iftc2022.html","order":11,"name":"conference_url","label":"Conference URL","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Single-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":"112","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":"40","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":"36% - 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","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":"6","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)"}}]}}