{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:40:33Z","timestamp":1760136033729,"version":"build-2065373602"},"publisher-location":"Cham","reference-count":40,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030606329"},{"type":"electronic","value":"9783030606336"}],"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\/978-3-030-60633-6_40","type":"book-chapter","created":{"date-parts":[[2020,10,10]],"date-time":"2020-10-10T18:02:37Z","timestamp":1602352957000},"page":"481-492","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Multi-human Parsing with Pose and Boundary Guidance"],"prefix":"10.1007","author":[{"given":"Shuncheng","family":"Du","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yigang","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zizhao","family":"Wu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2020,10,11]]},"reference":[{"key":"40_CR1","doi-asserted-by":"crossref","unstructured":"Lin, J., Guo, X., Shao, J., Jiang, C., Zhu, Y., Zhu, S.: A virtual reality platform for dynamic human-scene interaction. In: SIGGRAPH ASIA, Virtual Reality meets Physical Reality: Modelling and Simulating Virtual Humans and Environments, pp. 11:1\u201311:4. ACM (2016)","DOI":"10.1145\/2992138.2992144"},{"key":"40_CR2","first-page":"3075","volume":"13","author":"Y Wang","year":"2012","unstructured":"Wang, Y., Tran, D., Liao, Z., Forsyth, D.A.: Discriminative hierarchical part-based models for human parsing and action recognition. J. Mach. Learn. Res. 13, 3075\u20133102 (2012)","journal-title":"J. Mach. Learn. Res."},{"key":"40_CR3","unstructured":"Liang, X., Wei, Y., Shen, X., Yang, J., Lin, L., Yan, S.: Proposal-free network for instance-level object segmentation. CoRR abs\/1509.02636 (2015)"},{"key":"40_CR4","doi-asserted-by":"crossref","unstructured":"Chen, L., Yang, Y., Wang, J., Xu, W., Yuille, A.L.: Attention to scale: scale-aware semantic image segmentation. CoRR abs\/1511.03339 (2015)","DOI":"10.1109\/CVPR.2016.396"},{"key":"40_CR5","unstructured":"Lin, G., Milan, A., Shen, C., Reid, I.D.: RefineNet: multi-path refinement networks for high-resolution semantic segmentation. CoRR abs\/1611.06612 (2016)"},{"issue":"1","key":"40_CR6","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1109\/TPAMI.2016.2537339","volume":"39","author":"X Liang","year":"2017","unstructured":"Liang, X., et al.: Human parsing with contextualized convolutional neural network. IEEE Trans. Pattern Anal. Mach. Intell. 39(1), 115\u2013127 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"40_CR7","unstructured":"Xia, F., Wang, P., Chen, L., Yuille, A.L.: Zoom better to see clearer: human part segmentation with auto zoom net. CoRR abs\/1511.06881 (2015)"},{"key":"40_CR8","doi-asserted-by":"crossref","unstructured":"Xia, F., Wang, P., Chen, X., Yuille, A.L.: Joint multi-person pose estimation and semantic part segmentation. CoRR abs\/1708.03383 (2017)","DOI":"10.1109\/CVPR.2017.644"},{"key":"40_CR9","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":"40_CR10","doi-asserted-by":"crossref","unstructured":"Gong, K., Liang, X., Shen, X., Lin, L.: Look into person: self-supervised structure-sensitive learning and a new benchmark for human parsing. CoRR abs\/1703.05446 (2017)","DOI":"10.1109\/CVPR.2017.715"},{"issue":"4","key":"40_CR11","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. IEEE Trans. Pattern Anal. Mach. Intell. 41(4), 871\u2013885 (2019)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"40_CR12","doi-asserted-by":"crossref","unstructured":"Bourdev, L.D., Malik, J.: Poselets: body part detectors trained using 3D human pose annotations. In: IEEE 12th International Conference on Computer Vision, pp. 1365\u20131372. IEEE Computer Society (2009)","DOI":"10.1109\/ICCV.2009.5459303"},{"key":"40_CR13","doi-asserted-by":"crossref","unstructured":"Dong, J., Chen, Q., Xia, W., Huang, Z., Yan, S.: A deformable mixture parsing model with parselets. In: IEEE International Conference on Computer Vision, pp. 3408\u20133415. IEEE Computer Society (2013)","DOI":"10.1109\/ICCV.2013.423"},{"key":"40_CR14","doi-asserted-by":"crossref","unstructured":"Yang, Y., Ramanan, D.: Articulated pose estimation with flexible mixtures-of-parts. In: The 24th IEEE Conference on Computer Vision and Pattern Recognition, pp. 1385\u20131392. IEEE Computer Society (2011)","DOI":"10.1109\/CVPR.2011.5995741"},{"key":"40_CR15","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":"40_CR16","doi-asserted-by":"crossref","unstructured":"Long, J., Shelhamer, E., Darrell, T.: Fully convolutional networks for semantic segmentation. CoRR abs\/1411.4038 (2014)","DOI":"10.1109\/CVPR.2015.7298965"},{"key":"40_CR17","doi-asserted-by":"crossref","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. CoRR abs\/1505.04597 (2015)","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"12","key":"40_CR18","doi-asserted-by":"publisher","first-page":"2481","DOI":"10.1109\/TPAMI.2016.2644615","volume":"39","author":"V Badrinarayanan","year":"2017","unstructured":"Badrinarayanan, V., Kendall, A., Cipolla, R.: SegNet: a deep convolutional encoder-decoder architecture for image segmentation. IEEE Trans. Pattern Anal. Mach. Intell. 39(12), 2481\u20132495 (2017)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"40_CR19","unstructured":"Chen, L., Papandreou, G., Schroff, F., Adam, H.: Rethinking atrous convolution for semantic image segmentation. CoRR abs\/1706.05587 (2017)"},{"key":"40_CR20","doi-asserted-by":"crossref","unstructured":"Zhao, H., Shi, J., Qi, X., Wang, X., Jia, J.: Pyramid scene parsing network. CoRR abs\/1612.01105 (2016)","DOI":"10.1109\/CVPR.2017.660"},{"issue":"12","key":"40_CR21","doi-asserted-by":"publisher","first-page":"2402","DOI":"10.1109\/TPAMI.2015.2408360","volume":"37","author":"X Liang","year":"2015","unstructured":"Liang, X., et al.: Deep human parsing with active template regression. IEEE Trans. Pattern Anal. Mach. Intell. 37(12), 2402\u20132414 (2015)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"40_CR22","doi-asserted-by":"crossref","unstructured":"Liu, S., et al.: Matching-CNN meets KNN: quasi-parametric human parsing. CoRR abs\/1504.01220 (2015)","DOI":"10.1109\/CVPR.2015.7298748"},{"key":"40_CR23","doi-asserted-by":"crossref","unstructured":"G\u00fcler, R.A., Neverova, N., Kokkinos, I.: DensePose: dense human pose estimation in the wild. CoRR abs\/1802.00434 (2018)","DOI":"10.1109\/CVPR.2018.00762"},{"key":"40_CR24","doi-asserted-by":"crossref","unstructured":"Li, J., Wang, C., Zhu, H., Mao, Y., Fang, H., Lu, C.: CrowdPose: efficient crowded scenes pose estimation and a new benchmark. CoRR abs\/1812.00324 (2018)","DOI":"10.1109\/CVPR.2019.01112"},{"key":"40_CR25","doi-asserted-by":"crossref","unstructured":"Toshev, A., Szegedy, C.: DeepPose: human pose estimation via deep neural networks. CoRR abs\/1312.4659 (2013)","DOI":"10.1109\/CVPR.2014.214"},{"key":"40_CR26","doi-asserted-by":"crossref","unstructured":"Wei, S., Ramakrishna, V., Kanade, T., Sheikh, Y.: Convolutional pose machines. CoRR abs\/1602.00134 (2016)","DOI":"10.1109\/CVPR.2016.511"},{"key":"40_CR27","doi-asserted-by":"crossref","unstructured":"Cao, Z., Hidalgo, G., Simon, T., Wei, S., Sheikh, Y.: OpenPose: realtime multi-person 2D pose estimation using part affinity fields. CoRR abs\/1812.08008 (2018)","DOI":"10.1109\/CVPR.2017.143"},{"key":"40_CR28","unstructured":"Fang, H., Xie, S., Lu, C.: RMPE: regional multi-person pose estimation. CoRR abs\/1612.00137 (2016)"},{"issue":"2","key":"40_CR29","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1109\/TPAMI.2018.2844175","volume":"42","author":"K He","year":"2020","unstructured":"He, K., Gkioxari, G., Doll\u00e1r, P., Girshick, R.B.: Mask R-CNN. IEEE Trans. Pattern Anal. Mach. Intell. 42(2), 386\u2013397 (2020)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"issue":"1\u20133","key":"40_CR30","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/s11263-017-1004-z","volume":"125","author":"S Xie","year":"2017","unstructured":"Xie, S., Tu, Z.: Holistically-nested edge detection. Int. J. Comput. Vis. 125(1\u20133), 3\u201318 (2017)","journal-title":"Int. J. Comput. Vis."},{"issue":"8","key":"40_CR31","doi-asserted-by":"publisher","first-page":"1347","DOI":"10.1109\/TMM.2015.2443559","volume":"17","author":"S Liu","year":"2015","unstructured":"Liu, S., Liang, X., Liu, L., Lu, K., Lin, L., Cao, X., Yan, S.: Fashion parsing with video context. IEEE Trans. Multimedia 17(8), 1347\u20131358 (2015)","journal-title":"IEEE Trans. Multimedia"},{"key":"40_CR32","doi-asserted-by":"crossref","unstructured":"Yamaguchi, K., Kiapour, M.H., Ortiz, L.E., Berg, T.L.: Parsing clothing in fashion photographs. In: 2012 IEEE Conference on Computer Vision and Pattern Recognition, pp. 3570\u20133577. IEEE Computer Society (2012)","DOI":"10.1109\/CVPR.2012.6248101"},{"key":"40_CR33","doi-asserted-by":"crossref","unstructured":"Yang, W., Luo, P., Lin, L.: Clothing co-parsing by joint image segmentation and labeling. CoRR abs\/1502.00739 (2015)","DOI":"10.1109\/CVPR.2014.407"},{"key":"40_CR34","unstructured":"Bertasius, G., Shi, J., Torresani, L.: Semantic segmentation with boundary neural fields. CoRR abs\/1511.02674 (2015)"},{"key":"40_CR35","doi-asserted-by":"crossref","unstructured":"Chen, L., Barron, J.T., Papandreou, G., Murphy, K., Yuille, A.L.: Semantic image segmentation with task-specific edge detection using CNNs and a discriminatively trained domain transform. CoRR abs\/1511.03328 (2015)","DOI":"10.1109\/CVPR.2016.492"},{"key":"40_CR36","unstructured":"Liu, T., et al.: Devil in the details: towards accurate single and multiple human parsing. CoRR abs\/1809.05996 (2018)"},{"key":"40_CR37","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. CoRR abs\/1512.03385 (2015)"},{"key":"40_CR38","doi-asserted-by":"crossref","unstructured":"Osokin, D.: Real-time 2D multi-person pose estimation on CPU: lightweight OpenPose. CoRR abs\/1811.12004 (2018)","DOI":"10.5220\/0007555407440748"},{"key":"40_CR39","unstructured":"Kendall, A., Gal, Y., Cipolla, R.: Multi-task learning using uncertainty to weigh losses for scene geometry and semantics. CoRR abs\/1705.07115 (2017)"},{"key":"40_CR40","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. CoRR abs\/1406.2031 (2014)","DOI":"10.1109\/CVPR.2014.254"}],"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\/978-3-030-60633-6_40","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:04:34Z","timestamp":1760133874000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-60633-6_40"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020]]},"ISBN":["9783030606329","9783030606336"],"references-count":40,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-60633-6_40","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2020]]},"assertion":[{"value":"11 October 2020","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":"Nanjing","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":"2020","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"16 October 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"18 October 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"3","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.prcv.cn\/index_en.html","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 system","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"402","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":"158","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":"4","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)"}}]}}