{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,15]],"date-time":"2026-10-15T16:14:08Z","timestamp":1792080848072,"version":"4.4.0"},"publisher-location":"Cham","reference-count":43,"publisher":"Springer International Publishing","isbn-type":[{"value":"9783030586003","type":"print"},{"value":"9783030586010","type":"electronic"}],"license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"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":[[2020]]},"DOI":"10.1007\/978-3-030-58601-0_13","type":"book-chapter","created":{"date-parts":[[2020,11,27]],"date-time":"2020-11-27T19:02:52Z","timestamp":1606503772000},"page":"205-221","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":54,"title":["Learning Semantic Neural Tree for Human Parsing"],"prefix":"10.1007","author":[{"given":"Ruyi","family":"Ji","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Dawei","family":"Du","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Libo","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Longyin","family":"Wen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yanjun","family":"Wu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chen","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Feiyue","family":"Huang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Siwei","family":"Lyu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2020,11,28]]},"reference":[{"key":"13_CR1","unstructured":"Chen, L., et al.: Searching for efficient multi-scale architectures for dense image prediction. In: NeurIPS, vol, abs\/1809.04184 (2018)"},{"issue":"4","key":"13_CR2","doi-asserted-by":"publisher","first-page":"834","DOI":"10.1109\/TPAMI.2017.2699184","volume":"40","author":"L Chen","year":"2018","unstructured":"Chen, L., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. TPAMI 40(4), 834\u2013848 (2018)","journal-title":"TPAMI"},{"key":"13_CR3","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"833","DOI":"10.1007\/978-3-030-01234-2_49","volume-title":"Computer Vision \u2013 ECCV 2018","author":"L-C Chen","year":"2018","unstructured":"Chen, L.-C., Zhu, Y., Papandreou, G., Schroff, F., Adam, H.: Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11211, pp. 833\u2013851. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01234-2_49"},{"key":"13_CR4","doi-asserted-by":"crossref","unstructured":"Chen, X., Mottaghi, R., Liu, X., Fidler, S., Urtasun, R., Yuille, A.L.: Detect what you can: detecting and representing objects using holistic models and body parts. In: CVPR, pp. 1979\u20131986 (2014)","DOI":"10.1109\/CVPR.2014.254"},{"key":"13_CR5","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L., Li, K., Li, F.: ImageNet: a large-scale hierarchical image database. In: CVPR, pp. 248\u2013255 (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"issue":"2","key":"13_CR6","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham, M., Gool, L.V., Williams, C.K.I., Winn, J.M., Zisserman, A.: The pascal visual object classes (VOC) challenge. IJCV 88(2), 303\u2013338 (2010)","journal-title":"IJCV"},{"key":"13_CR7","doi-asserted-by":"crossref","unstructured":"Fang, H., Lu, G., Fang, X., Xie, J., Tai, Y., Lu, C.: Weakly and semi supervised human body part parsing via pose-guided knowledge transfer. CoRR abs\/1805.04310 (2018)","DOI":"10.1109\/CVPR.2018.00015"},{"key":"13_CR8","doi-asserted-by":"crossref","unstructured":"Gong, K., Gao, Y., Liang, X., Shen, X., Wang, M., Lin, L.: Graphonomy: universal human parsing via graph transfer learning. In: CVPR, pp. 7450\u20137459 (2019)","DOI":"10.1109\/CVPR.2019.00763"},{"key":"13_CR9","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"805","DOI":"10.1007\/978-3-030-01225-0_47","volume-title":"Computer Vision \u2013 ECCV 2018","author":"K Gong","year":"2018","unstructured":"Gong, K., Liang, X., Li, Y., Chen, Y., Yang, M., Lin, L.: Instance-level human parsing via part grouping network. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11208, pp. 805\u2013822. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01225-0_47"},{"key":"13_CR10","doi-asserted-by":"crossref","unstructured":"Gong, K., Liang, X., Zhang, D., Shen, X., Lin, L.: Look into person: self-supervised structure-sensitive learning and a new benchmark for human parsing. In: CVPR, pp. 6757\u20136765 (2017)","DOI":"10.1109\/CVPR.2017.715"},{"key":"13_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"297","DOI":"10.1007\/978-3-319-10584-0_20","volume-title":"Computer Vision \u2013 ECCV 2014","author":"B Hariharan","year":"2014","unstructured":"Hariharan, B., Arbel\u00e1ez, P., Girshick, R., Malik, J.: Simultaneous detection and segmentation. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8695, pp. 297\u2013312. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10584-0_20"},{"key":"13_CR12","doi-asserted-by":"crossref","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.B.: Mask R-CNN. In: ICCV, pp. 2980\u20132988 (2017)","DOI":"10.1109\/ICCV.2017.322"},{"key":"13_CR13","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR, pp. 770\u2013778 (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"13_CR14","doi-asserted-by":"crossref","unstructured":"Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: CVPR, pp. 7132\u20137141 (2018)","DOI":"10.1109\/CVPR.2018.00745"},{"key":"13_CR15","doi-asserted-by":"crossref","unstructured":"Huang, G., Liu, Z., Weinberger, K.Q.: Densely connected convolutional networks. CoRR abs\/1608.06993 (2016)","DOI":"10.1109\/CVPR.2017.243"},{"key":"13_CR16","doi-asserted-by":"publisher","first-page":"2066","DOI":"10.1109\/TIP.2019.2941644","volume":"29","author":"Z Huang","year":"2019","unstructured":"Huang, Z., Wang, C., Wang, X., Liu, W., Wang, J.: Semantic image segmentation by scale-adaptive networks. IEEE Trans. Image Process. 29, 2066\u20132077 (2019). https:\/\/doi.org\/10.1109\/TIP.2019.2941644","journal-title":"IEEE Trans. Image Process."},{"issue":"1","key":"13_CR17","doi-asserted-by":"publisher","first-page":"24","DOI":"10.1037\/0033-2909.112.1.24","volume":"112","author":"R Kimchi","year":"1992","unstructured":"Kimchi, R.: Primacy of wholistic processing and global\/local paradigm: a critical review. Psychol. Bull. 112(1), 24 (1992)","journal-title":"Psychol. Bull."},{"key":"13_CR18","doi-asserted-by":"crossref","unstructured":"Kontschieder, P., Fiterau, M., Criminisi, A., Bul\u00f2, S.R.: Deep neural decision forests. In: ICCV, pp. 1467\u20131475 (2015)","DOI":"10.1109\/ICCV.2015.172"},{"issue":"4","key":"13_CR19","doi-asserted-by":"publisher","first-page":"541","DOI":"10.1162\/neco.1989.1.4.541","volume":"1","author":"Y LeCun","year":"1989","unstructured":"LeCun, Y., et al.: Backpropagation applied to handwritten zip code recognition. Neural Comput. 1(4), 541\u2013551 (1989)","journal-title":"Neural Comput."},{"key":"13_CR20","unstructured":"Li, J., Zhao, J., Wei, Y., Lang, C., Li, Y., Feng, J.: Towards real world human parsing: multiple-human parsing in the wild. CoRR abs\/1705.07206 (2017)"},{"key":"13_CR21","unstructured":"Liang, X., Gong, K., Shen, X., Lin, L.: Look into person: joint body parsing & pose estimation network and a new benchmark. CoRR abs\/1804.01984 (2018)"},{"issue":"4","key":"13_CR22","doi-asserted-by":"publisher","first-page":"871","DOI":"10.1109\/TPAMI.2018.2820063","volume":"41","author":"X Liang","year":"2019","unstructured":"Liang, X., Gong, K., Shen, X., Lin, L.: Look into person: Joint body parsing & pose estimation network and a new benchmark. TPAMI 41(4), 871\u2013885 (2019)","journal-title":"TPAMI"},{"key":"13_CR23","doi-asserted-by":"crossref","unstructured":"Liang, X., Lin, L., Shen, X., Feng, J., Yan, S., Xing, E.P.: Interpretable structure-evolving LSTM. CoRR abs\/1703.03055 (2017)","DOI":"10.1109\/CVPR.2017.234"},{"key":"13_CR24","unstructured":"Liang, X., et al.: Deep human parsing with active template regression. CoRR abs\/1503.02391 (2015)"},{"key":"13_CR25","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"125","DOI":"10.1007\/978-3-319-46448-0_8","volume-title":"Computer Vision \u2013 ECCV 2016","author":"X Liang","year":"2016","unstructured":"Liang, X., Shen, X., Feng, J., Lin, L., Yan, S.: Semantic object parsing with graph LSTM. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 125\u2013143. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46448-0_8"},{"key":"13_CR26","doi-asserted-by":"crossref","unstructured":"Lin, G., Milan, A., Shen, C., Reid, I.D.: Refinenet: multi-path refinement networks for high-resolution semantic segmentation. In: CVPR, pp. 5168\u20135177 (2017)","DOI":"10.1109\/CVPR.2017.549"},{"key":"13_CR27","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"740","DOI":"10.1007\/978-3-319-10602-1_48","volume-title":"Computer Vision \u2013 ECCV 2014","author":"T-Y Lin","year":"2014","unstructured":"Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8693, pp. 740\u2013755. Springer, Cham (2014). https:\/\/doi.org\/10.1007\/978-3-319-10602-1_48"},{"key":"13_CR28","unstructured":"Liu, T., et al.: Devil in the details: towards accurate single and multiple human parsing. CoRR abs\/1809.05996 (2018)"},{"key":"13_CR29","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. In: CVPR, pp. 3431\u20133440 (2015)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"13_CR30","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"424","DOI":"10.1007\/978-3-030-01240-3_26","volume-title":"Computer Vision \u2013 ECCV 2018","author":"Y Luo","year":"2018","unstructured":"Luo, Y., et al.: Macro-micro adversarial network for human parsing. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11213, pp. 424\u2013440. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01240-3_26"},{"key":"13_CR31","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"519","DOI":"10.1007\/978-3-030-01228-1_31","volume-title":"Computer Vision \u2013 ECCV 2018","author":"X Nie","year":"2018","unstructured":"Nie, X., Feng, J., Yan, S.: Mutual learning to adapt for joint human parsing and pose estimation. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11209, pp. 519\u2013534. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01228-1_31"},{"key":"13_CR32","doi-asserted-by":"crossref","unstructured":"Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. CoRR abs\/1512.00567 (2015)","DOI":"10.1109\/CVPR.2016.308"},{"key":"13_CR33","doi-asserted-by":"crossref","unstructured":"Wang, W., Zhang, Z., Qi, S., Shen, J., Pang, Y., Shao, L.: Learning compositional neural information fusion for human parsing. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00580"},{"key":"13_CR34","doi-asserted-by":"crossref","unstructured":"Wang, W., Zhu, H., Dai, J., Pang, Y., Shen, J., Shao, L.: Hierarchical human parsing with typed part-relation reasoning. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00895"},{"key":"13_CR35","doi-asserted-by":"crossref","unstructured":"Wang, Y., Tran, D., Liao, Z.: Learning hierarchical poselets for human parsing. In: CVPR, pp. 1705\u20131712 (2011)","DOI":"10.1109\/CVPR.2011.5995519"},{"key":"13_CR36","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"648","DOI":"10.1007\/978-3-319-46454-1_39","volume-title":"Computer Vision \u2013 ECCV 2016","author":"F Xia","year":"2016","unstructured":"Xia, F., Wang, P., Chen, L.-C., Yuille, A.L.: Zoom better to see clearer: human and object parsing with hierarchical auto-zoom net. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9909, pp. 648\u2013663. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46454-1_39"},{"key":"13_CR37","doi-asserted-by":"crossref","unstructured":"Xia, F., Wang, P., Chen, X., Yuille, A.L.: Joint multi-person pose estimation and semantic part segmentation. In: CVPR, pp. 6080\u20136089 (2017)","DOI":"10.1109\/CVPR.2017.644"},{"key":"13_CR38","unstructured":"Xiao, H.: NDT: neual decision tree towards fully functioned neural graph. CoRR abs\/1712.05934 (2017)"},{"key":"13_CR39","doi-asserted-by":"crossref","unstructured":"Zhang, R., Tang, S., Zhang, Y., Li, J., Yan, S.: Scale-adaptive convolutions for scene parsing. In: ICCV, pp. 2050\u20132058 (2017)","DOI":"10.1109\/ICCV.2017.224"},{"key":"13_CR40","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. In: CVPR, pp. 6230\u20136239 (2017)","DOI":"10.1109\/CVPR.2017.660"},{"key":"13_CR41","doi-asserted-by":"crossref","unstructured":"Zhao, J., Li, J., Cheng, Y., Sim, T., Yan, S., Feng, J.: Understanding humans in crowded scenes: deep nested adversarial learning and a new benchmark for multi-human parsing. In: ACM MM, pp. 792\u2013800 (2018)","DOI":"10.1145\/3240508.3240509"},{"key":"13_CR42","doi-asserted-by":"crossref","unstructured":"Zhao, J., et al.: Understanding humans in crowded scenes: deep nested adversarial learning and a new benchmark for multi-human parsing. CoRR abs\/1804.03287 (2018)","DOI":"10.1145\/3240508.3240509"},{"key":"13_CR43","doi-asserted-by":"crossref","unstructured":"Zhu, X., Hu, H., Lin, S., Dai, J.: Deformable convnets V2: more deformable, better results. In: CVPR, pp. 9308\u20139316 (2019)","DOI":"10.1109\/CVPR.2019.00953"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2020"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-58601-0_13","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,27]],"date-time":"2024-11-27T00:09:02Z","timestamp":1732666142000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-58601-0_13"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030586003","9783030586010"],"references-count":43,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-58601-0_13","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"value":"0302-9743","type":"print"},{"value":"1611-3349","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"28 November 2020","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ECCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"European Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Glasgow","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"United Kingdom","order":4,"name":"conference_country","label":"Conference Country","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"23 August 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"28 August 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2020.eu\/","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":"OpenReview","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5025","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":"1360","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":"27% - 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":"7","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)"}},{"value":"The conference was held virtually due to the COVID-19 pandemic. From the ECCV Workshops 249 full papers, 18 short papers, and 21 further contributions were published out of a total of 467 submissions.","order":10,"name":"additional_info_on_review_process","label":"Additional Info on Review Process","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}}]}}