{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,27]],"date-time":"2025-03-27T21:15:48Z","timestamp":1743110148138,"version":"3.40.3"},"publisher-location":"Cham","reference-count":22,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031500688"},{"type":"electronic","value":"9783031500695"}],"license":[{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,1,1]],"date-time":"2024-01-01T00:00:00Z","timestamp":1704067200000},"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\/978-3-031-50069-5_7","type":"book-chapter","created":{"date-parts":[[2024,1,19]],"date-time":"2024-01-19T06:02:34Z","timestamp":1705644154000},"page":"69-80","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["COCCI: Context-Driven Clothing Classification Network"],"prefix":"10.1007","author":[{"given":"Minghua","family":"Jiang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shuqing","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yankang","family":"Shi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chenghu","family":"Du","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Guangyu","family":"Tang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Li","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Peng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xinrong","family":"Hu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Feng","family":"Yu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2024,1,20]]},"reference":[{"key":"7_CR1","unstructured":"Dosovitskiy, A., et al.: An image is worth 16\u00a0$$\\times $$\u00a016 words: transformers for image recognition at scale. In: ICLR, pp. 1\u201312 (2021)"},{"key":"7_CR2","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":"7_CR3","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"7_CR4","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4700\u20134708 (2017)","DOI":"10.1109\/CVPR.2017.243"},{"key":"7_CR5","doi-asserted-by":"crossref","unstructured":"Lan, S., Li, J., Hu, S., Fan, H., Pan, Z.: A neighbourhood feature-based local binary pattern for texture classification. Vis. Comput. 1\u201325 (2023)","DOI":"10.1007\/s00371-023-03041-3"},{"issue":"3","key":"7_CR6","doi-asserted-by":"publisher","first-page":"897","DOI":"10.1007\/s00371-021-02058-w","volume":"38","author":"Y Liu","year":"2022","unstructured":"Liu, Y., Dou, Y., Jin, R., Li, R., Qiao, P.: Hierarchical learning with backtracking algorithm based on the visual confusion label tree for large-scale image classification. Vis. Comput. 38(3), 897\u2013917 (2022)","journal-title":"Vis. Comput."},{"key":"7_CR7","doi-asserted-by":"crossref","unstructured":"Liu, Z., et al.: Swin transformer: hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision (ICCV), pp. 10012\u201310022, October 2021","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"7_CR8","doi-asserted-by":"crossref","unstructured":"Liu, Z., Mao, H., Wu, C.Y., Feichtenhofer, C., Darrell, T., Xie, S.: A ConvNet for the 2020s. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11976\u201311986 (2022)","DOI":"10.1109\/CVPR52688.2022.01167"},{"key":"7_CR9","doi-asserted-by":"crossref","unstructured":"Liu, Z., Luo, P., Qiu, S., Wang, X., Tang, X.: DeepFashion: powering robust clothes recognition and retrieval with rich annotations. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1096\u20131104 (2016)","DOI":"10.1109\/CVPR.2016.124"},{"issue":"11","key":"7_CR10","doi-asserted-by":"publisher","first-page":"3551","DOI":"10.1007\/s00371-021-02178-3","volume":"38","author":"M Shajini","year":"2022","unstructured":"Shajini, M., Ramanan, A.: A knowledge-sharing semi-supervised approach for fashion clothes classification and attribute prediction. Vis. Comput. 38(11), 3551\u20133561 (2022)","journal-title":"Vis. Comput."},{"key":"7_CR11","unstructured":"Tan, M., Le, Q.: EfficientNet: rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105\u20136114. PMLR (2019)"},{"key":"7_CR12","doi-asserted-by":"crossref","unstructured":"Wang, Q., Wu, B., Zhu, P., Li, P., Zuo, W., Hu, Q.: ECA-Net: efficient channel attention for deep convolutional neural networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 11534\u201311542 (2020)","DOI":"10.1109\/CVPR42600.2020.01155"},{"key":"7_CR13","doi-asserted-by":"crossref","unstructured":"Wang, W., Xu, Y., Shen, J., Zhu, S.C.: Attentive fashion grammar network for fashion landmark detection and clothing category classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 4271\u20134280 (2018)","DOI":"10.1109\/CVPR.2018.00449"},{"key":"7_CR14","doi-asserted-by":"crossref","unstructured":"Woo, S., et al.: ConvNeXt V2: co-designing and scaling ConvNets with masked autoencoders. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 16133\u201316142, June 2023","DOI":"10.1109\/CVPR52729.2023.01548"},{"key":"7_CR15","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-030-01234-2_1","volume-title":"Computer Vision \u2013 ECCV 2018","author":"S Woo","year":"2018","unstructured":"Woo, S., Park, J., Lee, J.-Y., Kweon, I.S.: CBAM: convolutional block attention module. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 3\u201319. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_1"},{"issue":"2","key":"7_CR16","doi-asserted-by":"publisher","first-page":"2839","DOI":"10.1007\/s11042-022-13395-w","volume":"82","author":"TE Xia","year":"2023","unstructured":"Xia, T.E., Zhang, J.Y.: Clothing classification using transfer learning with squeeze and excitation block. Multimedia Tools Appl. 82(2), 2839\u20132856 (2023)","journal-title":"Multimedia Tools Appl."},{"key":"7_CR17","unstructured":"Xiao, T., Xia, T., Yang, Y., Huang, C., Wang, X.: Learning from massive noisy labeled data for image classification. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 2691\u20132699 (2015)"},{"issue":"5","key":"7_CR18","doi-asserted-by":"publisher","first-page":"66","DOI":"10.2478\/ftee-2022-0046","volume":"30","author":"J Xu","year":"2022","unstructured":"Xu, J., Wei, Y., Wang, A., Zhao, H., Lefloch, D.: Analysis of clothing image classification models: a comparison study between traditional machine learning and deep learning models. Fibres Text. East. Eur. 30(5), 66\u201378 (2022)","journal-title":"Fibres Text. East. Eur."},{"issue":"22","key":"7_CR19","doi-asserted-by":"publisher","first-page":"11024","DOI":"10.3390\/app112211024","volume":"11","author":"F Yu","year":"2021","unstructured":"Yu, F., et al.: EnCaps: clothing image classification based on enhanced capsule network. Appl. Sci. 11(22), 11024 (2021)","journal-title":"Appl. Sci."},{"key":"7_CR20","unstructured":"Zeghoud, S., et al.: Real-time spatial normalization for dynamic gesture classification. Vis. Comput. 1\u201313 (2022)"},{"key":"7_CR21","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Zhang, P., Yuan, C., Wang, Z.: Texture and shape biased two-stream networks for clothing classification and attribute recognition. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 13538\u201313547 (2020)","DOI":"10.1109\/CVPR42600.2020.01355"},{"issue":"1","key":"7_CR22","doi-asserted-by":"publisher","first-page":"2190188","DOI":"10.1080\/15440478.2023.2190188","volume":"20","author":"Z Zhou","year":"2023","unstructured":"Zhou, Z., Liu, M., Deng, W., Wang, Y., Zhu, Z.: Clothing image classification with DenseNet201 network and optimized regularized random vector functional link. J. Nat. Fibers 20(1), 2190188 (2023)","journal-title":"J. Nat. Fibers"}],"container-title":["Lecture Notes in Computer Science","Advances in Computer Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-50069-5_7","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,1,19]],"date-time":"2024-01-19T06:04:30Z","timestamp":1705644270000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-50069-5_7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024]]},"ISBN":["9783031500688","9783031500695"],"references-count":22,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-50069-5_7","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2024]]},"assertion":[{"value":"20 January 2024","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"CGI","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Computer Graphics International Conference","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"cgi2023","order":10,"name":"conference_id","label":"Conference ID","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":"385","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":"149","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":"39% - 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":"3","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)"}}]}}