{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,6]],"date-time":"2025-12-06T16:48:47Z","timestamp":1765039727795,"version":"3.40.3"},"publisher-location":"Cham","reference-count":43,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031463044"},{"type":"electronic","value":"9783031463051"}],"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\/978-3-031-46305-1_11","type":"book-chapter","created":{"date-parts":[[2023,10,28]],"date-time":"2023-10-28T07:02:41Z","timestamp":1698476561000},"page":"130-141","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Weakly Supervised Image Matting via\u00a0Patch Clustering"],"prefix":"10.1007","author":[{"given":"Yunke","family":"Zhang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chi","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yu","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hujun","family":"Bao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weiwei","family":"Xu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,10,29]]},"reference":[{"issue":"4","key":"11_CR1","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1145\/3197517.3201275","volume":"37","author":"Y Aksoy","year":"2018","unstructured":"Aksoy, Y., Oh, T.H., Paris, S., Pollefeys, M., Matusik, W.: Semantic soft segmentation. ACM Trans. Graph. 37(4), 72 (2018)","journal-title":"ACM Trans. Graph."},{"key":"11_CR2","doi-asserted-by":"crossref","unstructured":"Cai, S., et al.: Disentangled image matting. In: International Conference on Computer Vision, October 2019","DOI":"10.1109\/ICCV.2019.00891"},{"issue":"9","key":"11_CR3","doi-asserted-by":"publisher","first-page":"2175","DOI":"10.1109\/TPAMI.2013.18","volume":"35","author":"Q Chen","year":"2013","unstructured":"Chen, Q., Li, D., Tang, C.K.: KNN matting. IEEE Trans. Pattern Anal. Mach. Intell. 35(9), 2175\u20132188 (2013)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"11_CR4","unstructured":"Chuang, Y.Y., Curless, B., Salesin, D., Szeliski, R.: A bayesian approach to digital matting. In: CVPR, 2001. In: Proceedings of the 2001 IEEE Computer Society Conference on, CVPR 2001, vol. 2, pp. II-II. IEEE (2001)"},{"key":"11_CR5","doi-asserted-by":"crossref","unstructured":"Dai, Y., Lu, H., Shen, C.: Learning affinity-aware upsampling for deep image matting. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 6841\u20136850. Computer Vision Foundation\/IEEE (2021)","DOI":"10.1109\/CVPR46437.2021.00677"},{"key":"11_CR6","doi-asserted-by":"publisher","unstructured":"Duchon, J.: Splines minimizing rotation-invariant semi-norms in sobolev spaces. In: Schempp, W., Zeller, K. (eds.) Constructive Theory of Functions of Several Variables: Proceedings of a Conference Held at Oberwolfach, Germany, April 25\u2013May 1, 1976. LNM, vol. 571, pp. 85\u2013100. Springer, Cham (1976). https:\/\/doi.org\/10.1007\/BFb0086566","DOI":"10.1007\/BFb0086566"},{"issue":"2","key":"11_CR7","doi-asserted-by":"publisher","first-page":"303","DOI":"10.1007\/s11263-009-0275-4","volume":"88","author":"M Everingham","year":"2010","unstructured":"Everingham, M., Van Gool, L., Williams, C.K.I., Winn, J., Zisserman, A.: The pascal visual object classes (VOC) challenge. IJCV 88(2), 303\u2013338 (2010)","journal-title":"IJCV"},{"key":"11_CR8","doi-asserted-by":"crossref","unstructured":"Gastal, E.S., Oliveira, M.M.: Shared sampling for real-time alpha matting. In: Computer Graphics Forum, pp. 575\u2013584. Wiley Online Library (2010)","DOI":"10.1111\/j.1467-8659.2009.01627.x"},{"key":"11_CR9","unstructured":"Grady, L., Schiwietz, T., Aharon, S., Westermann, R.: Random walks for interactive alpha-matting. In: Proceedings of VIIP, vol. 2005, pp. 423\u2013429 (2005)"},{"key":"11_CR10","doi-asserted-by":"crossref","unstructured":"He, K., Rhemann, C., Rother, C., Tang, X., Sun, J.: A global sampling method for alpha matting. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2049\u20132056. IEEE Computer Society (2011)","DOI":"10.1109\/CVPR.2011.5995495"},{"key":"11_CR11","doi-asserted-by":"crossref","unstructured":"Hou, Q., Liu, F.: Context-aware image matting for simultaneous foreground and alpha estimation. In: International Conference on Computer Vision, October 2019","DOI":"10.1109\/ICCV.2019.00423"},{"key":"11_CR12","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"290","DOI":"10.1007\/978-3-030-58583-9_18","volume-title":"Computer Vision \u2013 ECCV 2020","author":"V Kulharia","year":"2020","unstructured":"Kulharia, V., Chandra, S., Agrawal, A., Torr, P., Tyagi, A.: Box2Seg: attention weighted loss and discriminative feature learning for weakly supervised segmentation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12372, pp. 290\u2013308. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58583-9_18"},{"key":"11_CR13","doi-asserted-by":"crossref","unstructured":"Levin, A., Lischinski, D., Weiss, Y.: A closed form solution to natural image matting. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 61\u201368. IEEE (2006)","DOI":"10.1109\/CVPR.2006.18"},{"issue":"10","key":"11_CR14","doi-asserted-by":"publisher","first-page":"1699","DOI":"10.1109\/TPAMI.2008.168","volume":"30","author":"A Levin","year":"2008","unstructured":"Levin, A., Rav-Acha, A., Lischinski, D.: Spectral matting. IEEE Trans. Pattern Anal. Mach. Intell. 30(10), 1699\u20131712 (2008)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"11_CR15","doi-asserted-by":"publisher","unstructured":"Li, J., Zhang, J., Maybank, S.J., Tao, D.: Bridging composite and real: towards end-to-end deep image matting. Int. J. Comput. Vis. 1\u201321 (2021). https:\/\/doi.org\/10.1007\/s11263-021-01541-0","DOI":"10.1007\/s11263-021-01541-0"},{"key":"11_CR16","doi-asserted-by":"crossref","unstructured":"Li, J., Zhang, J., Tao, D.: Deep automatic natural image matting. In: Zhou, Z. (ed.) Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence, IJCAI 2021, Virtual Event\/Montreal, Canada, 19\u201327 August 2021, pp. 800\u2013806. ijcai.org (2021)","DOI":"10.24963\/ijcai.2021\/111"},{"key":"11_CR17","doi-asserted-by":"crossref","unstructured":"Li, Y., Lu, H.: Natural image matting via guided contextual attention. In: AAAI, vol. 34, pp. 11450\u201311457 (2020)","DOI":"10.1609\/aaai.v34i07.6809"},{"key":"11_CR18","doi-asserted-by":"crossref","unstructured":"Lin, S., Ryabtsev, A., Sengupta, S., Curless, B.L., Seitz, S.M., Kemelmacher-Shlizerman, I.: Real-time high-resolution background matting. In: IEEE Conference on Computer Vision and Pattern recognition, pp. 8762\u20138771, June 2021","DOI":"10.1109\/CVPR46437.2021.00865"},{"key":"11_CR19","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":"11_CR20","doi-asserted-by":"crossref","unstructured":"Liu, J., Yao, Y., Hou, W., Cui, M., Xie, X., Zhang, C., Hua, X.: Boosting semantic human matting with coarse annotations. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 8560\u20138569. Computer Vision Foundation\/IEEE (2020)","DOI":"10.1109\/CVPR42600.2020.00859"},{"key":"11_CR21","doi-asserted-by":"crossref","unstructured":"Liu, W., Zhang, C., Lin, G., Hung, T.Y., Miao, C.: Weakly supervised segmentation with maximum bipartite graph matching. In: ACMMM (2020)","DOI":"10.1145\/3394171.3413652"},{"key":"11_CR22","doi-asserted-by":"crossref","unstructured":"Liu, Y., Xie, J., Shi, X., Qiao, Y., Huang, Y., Tang, Y., Yang, X.: Tripartite information mining and integration for image matting. In: ICCV, pp. 7555\u20137564 (2021)","DOI":"10.1109\/ICCV48922.2021.00746"},{"key":"11_CR23","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":"11_CR24","doi-asserted-by":"crossref","unstructured":"Lu, H., Dai, Y., Shen, C., Xu, S.: Indices matter: learning to index for deep image matting. In: International Conference on Computer Vision, October 2019","DOI":"10.1109\/ICCV.2019.00336"},{"key":"11_CR25","unstructured":"Lutz, S., Amplianitis, K., Smolic, A.: AlphaGAN: generative adversarial networks for natural image matting. In: British Machine Vision Conference, p. 259. BMVA Press (2018)"},{"key":"11_CR26","doi-asserted-by":"crossref","unstructured":"Qiao, Y., Liu, Y., Yang, X., Zhou, D., Xu, M., Zhang, Q., Wei, X.: Attention-guided hierarchical structure aggregation for image matting. In: IEEE Conference on Computer Vision and Pattern Recognition, June 2020","DOI":"10.1109\/CVPR42600.2020.01369"},{"key":"11_CR27","doi-asserted-by":"crossref","unstructured":"Ren, Z., et al.: Instance-aware, context-focused, and memory-efficient weakly supervised object detection. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 10598\u201310607 (2020)","DOI":"10.1109\/CVPR42600.2020.01061"},{"key":"11_CR28","doi-asserted-by":"crossref","unstructured":"Rhemann, C., Rother, C., Wang, J., Gelautz, M., Kohli, P., Rott, P.: A perceptually motivated online benchmark for image matting. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1826\u20131833. IEEE (2009)","DOI":"10.1109\/CVPR.2009.5206503"},{"key":"11_CR29","unstructured":"Ruzon, M.A., Tomasi, C.: Alpha estimation in natural images. In: IEEE Conference on Computer Vision and Pattern Recognition, p. 1018. IEEE (2000)"},{"key":"11_CR30","series-title":"Progress in Nonlinear Differential Equations and Their Applications","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-20828-2","volume-title":"Optimal Transport for Applied Mathematicians","author":"F Santambrogio","year":"2015","unstructured":"Santambrogio, F.: Optimal Transport for Applied Mathematicians. PNDETA, vol. 87. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-20828-2"},{"key":"11_CR31","doi-asserted-by":"crossref","unstructured":"Sengupta, S., Jayaram, V., Curless, B., Seitz, S.M., Kemelmacher-Shlizerman, I.: Background matting: the world is your green screen. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 2291\u20132300 (2020)","DOI":"10.1109\/CVPR42600.2020.00236"},{"key":"11_CR32","doi-asserted-by":"crossref","unstructured":"Simard, P.Y., Steinkraus, D., Platt, J.C.: Best practices for convolutional neural networks applied to visual document analysis. In: ICDAR 2003, pp. 958\u2013962. IEEE Computer Society (2003)","DOI":"10.1109\/ICDAR.2003.1227801"},{"issue":"3","key":"11_CR33","doi-asserted-by":"publisher","first-page":"315","DOI":"10.1145\/1015706.1015721","volume":"23","author":"J Sun","year":"2004","unstructured":"Sun, J., Jia, J., Tang, C.K., Shum, H.Y.: Poisson matting. ACM Trans. Graph. 23(3), 315\u2013321 (2004)","journal-title":"ACM Trans. Graph."},{"key":"11_CR34","doi-asserted-by":"crossref","unstructured":"Sun, Y., Tang, C., Tai, Y.: Semantic image matting. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 11120\u201311129. Computer Vision Foundation\/IEEE (2021)","DOI":"10.1109\/CVPR46437.2021.01097"},{"key":"11_CR35","doi-asserted-by":"crossref","unstructured":"Sun, Y., et al.: Circle loss: a unified perspective of pair similarity optimization. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 6397\u20136406. Computer Vision Foundation\/IEEE (2020)","DOI":"10.1109\/CVPR42600.2020.00643"},{"key":"11_CR36","doi-asserted-by":"crossref","unstructured":"Wang, J., Cohen, M.F., et al.: Image and video matting: a survey. Found. Trends\u00ae Comput. Graph. Vis. 3(2), 97\u2013175 (2008)","DOI":"10.1561\/0600000019"},{"key":"11_CR37","doi-asserted-by":"crossref","unstructured":"Xu, N., Price, B.L., Cohen, S., Huang, T.S.: Deep image matting. In: IEEE Conference on Computer Vision and Pattern Recognition, vol. 2, p. 4 (2017)","DOI":"10.1109\/CVPR.2017.41"},{"key":"11_CR38","doi-asserted-by":"crossref","unstructured":"Yu, Q., et al.: Mask guided matting via progressive refinement network. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1154\u20131163. Computer Vision Foundation\/IEEE (2021)","DOI":"10.1109\/CVPR46437.2021.00121"},{"issue":"9","key":"11_CR39","first-page":"5866","volume":"44","author":"D Zhang","year":"2021","unstructured":"Zhang, D., Han, J., Cheng, G., Yang, M.H.: Weakly supervised object localization and detection: a survey. IEEE Trans. Pattern Anal. Mach. Intell. 44(9), 5866\u20135885 (2021)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"11_CR40","doi-asserted-by":"crossref","unstructured":"Zhang, Y., et al.: A late fusion CNN for digital matting. In: IEEE Conference on Computer Vision and Pattern Recognition, June 2019","DOI":"10.1109\/CVPR.2019.00765"},{"issue":"6","key":"11_CR41","doi-asserted-by":"publisher","first-page":"2192","DOI":"10.1109\/TCSVT.2020.3024213","volume":"31","author":"F Zhou","year":"2020","unstructured":"Zhou, F., Tian, Y., Qi, Z.: Attention transfer network for nature image matting. IEEE Trans. Circ. Syst. Video Technol. 31(6), 2192\u20132205 (2020)","journal-title":"IEEE Trans. Circ. Syst. Video Technol."},{"issue":"1","key":"11_CR42","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1093\/nsr\/nwx106","volume":"5","author":"ZH Zhou","year":"2018","unstructured":"Zhou, Z.H.: A brief introduction to weakly supervised learning. Natl. Sci. Rev. 5(1), 44\u201353 (2018)","journal-title":"Natl. Sci. Rev."},{"key":"11_CR43","doi-asserted-by":"crossref","unstructured":"Zou, Z., Li, W., Shi, T., Shi, Z., Ye, J.: Generative adversarial training for weakly supervised cloud matting. In: ICCV, pp. 201\u2013210 (2019)","DOI":"10.1109\/ICCV.2019.00029"}],"container-title":["Lecture Notes in Computer Science","Image and Graphics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-46305-1_11","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,11,1]],"date-time":"2024-11-01T00:00:35Z","timestamp":1730419235000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-46305-1_11"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"ISBN":["9783031463044","9783031463051"],"references-count":43,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-46305-1_11","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2023]]},"assertion":[{"value":"29 October 2023","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ICIG","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"International Conference on Image and Graphics","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":"2023","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"22 September 2023","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"24 September 2023","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"12","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"icig2023","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/icig2023.csig.org.cn\/","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":"Conference Management Toolkit","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"409","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":"166","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":"41% - 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)"}}]}}