{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,13]],"date-time":"2025-11-13T07:22:22Z","timestamp":1763018542330,"version":"3.40.3"},"publisher-location":"Cham","reference-count":48,"publisher":"Springer Nature Switzerland","isbn-type":[{"type":"print","value":"9783031198175"},{"type":"electronic","value":"9783031198182"}],"license":[{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,1,1]],"date-time":"2022-01-01T00:00:00Z","timestamp":1640995200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2022]]},"DOI":"10.1007\/978-3-031-19818-2_22","type":"book-chapter","created":{"date-parts":[[2022,10,21]],"date-time":"2022-10-21T16:21:10Z","timestamp":1666369270000},"page":"379-395","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Slim Scissors: Segmenting Thin Object from\u00a0Synthetic Background"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3522-4816","authenticated-orcid":false,"given":"Kunyang","family":"Han","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7538-6759","authenticated-orcid":false,"given":"Jun Hao","family":"Liew","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6843-0064","authenticated-orcid":false,"given":"Jiashi","family":"Feng","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huawei","family":"Tian","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8581-9554","authenticated-orcid":false,"given":"Yao","family":"Zhao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2812-8781","authenticated-orcid":false,"given":"Yunchao","family":"Wei","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,10,22]]},"reference":[{"key":"22_CR1","doi-asserted-by":"crossref","unstructured":"Acuna, D., Ling, H., Kar, A., Fidler, S.: Efficient interactive annotation of segmentation datasets with Polygon-RNN++. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00096"},{"key":"22_CR2","doi-asserted-by":"crossref","unstructured":"Bai, X., Sapiro, G.: A geodesic framework for fast interactive image and video segmentation and matting. In: ICCV (2007)","DOI":"10.21236\/ADA478319"},{"key":"22_CR3","unstructured":"Boykov, Y.Y., Jolly, M.P.: Interactive graph cuts for optimal boundary & region segmentation of objects in nd images. In: ICCV (2001)"},{"key":"22_CR4","doi-asserted-by":"crossref","unstructured":"Castrejon, L., Kundu, K., Urtasun, R., Fidler, S.: Annotating object instances with a Polygon-RNN. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.477"},{"key":"22_CR5","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"293","DOI":"10.1007\/978-3-030-58601-0_18","volume-title":"Computer Vision \u2013 ECCV 2020","author":"B Chen","year":"2020","unstructured":"Chen, B., Ling, H., Zeng, X., Gao, J., Xu, Z., Fidler, S.: ScribbleBox: interactive annotation framework for video object segmentation. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12358, pp. 293\u2013310. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58601-0_18"},{"key":"22_CR6","doi-asserted-by":"crossref","unstructured":"Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs. TPAMI (2018)","DOI":"10.1109\/TPAMI.2017.2699184"},{"key":"22_CR7","doi-asserted-by":"crossref","unstructured":"Chen, X., Zhao, Z., Yu, F., Zhang, Y., Duan, M.: Conditional diffusion for interactive segmentation. In: ICCV (2021)","DOI":"10.1109\/ICCV48922.2021.00725"},{"key":"22_CR8","doi-asserted-by":"crossref","unstructured":"Dang, V.N., et al.: Vessel-captcha: an efficient learning framework for vessel annotation and segmentation. In: Medical Image Analysis (2021)","DOI":"10.1016\/j.media.2021.102263"},{"key":"22_CR9","doi-asserted-by":"crossref","unstructured":"Dong, X., Shen, J., Shao, L., Van Gool, L.: Sub-markov random walk for image segmentation. TIP (2015)","DOI":"10.1109\/TIP.2015.2505184"},{"key":"22_CR10","doi-asserted-by":"crossref","unstructured":"Everingham, M., Van Gool, L., Williams, C.K., Winn, J., Zisserman, A.: The pascal visual object classes (VOC) challenge. In: IJCV (2010)","DOI":"10.1007\/s11263-009-0275-4"},{"key":"22_CR11","doi-asserted-by":"crossref","unstructured":"Grady, L.: Random walks for image segmentation. TPAMI (2006)","DOI":"10.1109\/TPAMI.2006.233"},{"key":"22_CR12","doi-asserted-by":"crossref","unstructured":"Gulshan, V., Rother, C., Criminisi, A., Blake, A., Zisserman, A.: Geodesic star convexity for interactive image segmentation. In: CVPR (2010)","DOI":"10.1109\/CVPR.2010.5540073"},{"key":"22_CR13","doi-asserted-by":"crossref","unstructured":"Hao, Y., et al.: Edgeflow: Achieving practical interactive segmentation with edge-guided flow (2021)","DOI":"10.1109\/ICCVW54120.2021.00180"},{"key":"22_CR14","doi-asserted-by":"crossref","unstructured":"He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.90"},{"key":"22_CR15","doi-asserted-by":"crossref","unstructured":"Hu, Y., Soltoggio, A., Lock, R., Carter, S.: A fully convolutional two-stream fusion network for interactive image segmentation. In: Neural Networks (2019)","DOI":"10.1016\/j.neunet.2018.10.009"},{"key":"22_CR16","doi-asserted-by":"crossref","unstructured":"Jang, W.D., Kim, C.S.: Interactive image segmentation via backpropagating refinement scheme. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00544"},{"key":"22_CR17","unstructured":"Jegelka, S., Bilmes, J.: Cooperative cuts for image segmentation. Tech. rep., Technical Report 2010\u20130003, University of Washington (2010)"},{"key":"22_CR18","unstructured":"Kingma, D.P., Ba, J.: Adam: A method for stochastic optimization (2015)"},{"key":"22_CR19","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"579","DOI":"10.1007\/978-3-030-58517-4_34","volume-title":"Computer Vision \u2013 ECCV 2020","author":"T Kontogianni","year":"2020","unstructured":"Kontogianni, T., Gygli, M., Uijlings, J., Ferrari, V.: Continuous adaptation for interactive object segmentation by learning from corrections. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12361, pp. 579\u2013596. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58517-4_34"},{"key":"22_CR20","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"20","DOI":"10.1007\/978-3-030-01264-9_2","volume-title":"Computer Vision \u2013 ECCV 2018","author":"H Le","year":"2018","unstructured":"Le, H., Mai, L., Price, B., Cohen, S., Jin, H., Liu, F.: Interactive boundary prediction for object selection. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision \u2013 ECCV 2018. LNCS, vol. 11218, pp. 20\u201336. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01264-9_2"},{"key":"22_CR21","doi-asserted-by":"crossref","unstructured":"Li, Z., Chen, Q., Koltun, V.: Interactive image segmentation with latent diversity. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00067"},{"key":"22_CR22","doi-asserted-by":"crossref","unstructured":"Liew, J.H., Cohen, S., Price, B., Mai, L., Feng, J.: Deep interactive thin object selection. In: WACV (2021)","DOI":"10.1109\/WACV48630.2021.00035"},{"key":"22_CR23","doi-asserted-by":"crossref","unstructured":"Liew, J.H., Cohen, S., Price, B., Mai, L., Ong, S.H., Feng, J.: MultiSeg: Semantically meaningful, scale-diverse segmentations from minimal user input. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00075"},{"key":"22_CR24","doi-asserted-by":"crossref","unstructured":"Liew, J.H., Wei, Y., Xiong, W., Ong, S.H., Feng, J.: Regional interactive image segmentation networks. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.297"},{"key":"22_CR25","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.: 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":"22_CR26","doi-asserted-by":"crossref","unstructured":"Lin, Z., Zhang, Z., Chen, L.Z., Cheng, M.M., Lu, S.P.: Interactive image segmentation with first click attention. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01335"},{"key":"22_CR27","doi-asserted-by":"crossref","unstructured":"Ling, H., Gao, J., Kar, A., Chen, W., Fidler, S.: Fast interactive object annotation with Curve-GCN. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00540"},{"key":"22_CR28","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1007\/978-3-030-01252-6_6","volume-title":"Computer Vision \u2013 ECCV 2018","author":"G Liu","year":"2018","unstructured":"Liu, G., Reda, F.A., Shih, K.J., Wang, T.-C., Tao, A., Catanzaro, B.: Image inpainting for irregular holes using partial convolutions. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11215, pp. 89\u2013105. Springer, Cham (2018). https:\/\/doi.org\/10.1007\/978-3-030-01252-6_6"},{"key":"22_CR29","doi-asserted-by":"crossref","unstructured":"Majumder, S., Yao, A.: Content-aware multi-level guidance for interactive instance segmentation. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.01187"},{"key":"22_CR30","doi-asserted-by":"crossref","unstructured":"Maninis, K.K., Caelles, S., Pont-Tuset, J., Van Gool, L.: Deep extreme cut: From extreme points to object segmentation. In: CVPR (2018)","DOI":"10.1109\/CVPR.2018.00071"},{"key":"22_CR31","doi-asserted-by":"crossref","unstructured":"Mansilla, L.A., Miranda, P.A.: Oriented image foresting transform segmentation: Connectivity constraints with adjustable width. In: SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI) (2016)","DOI":"10.1109\/SIBGRAPI.2016.047"},{"key":"22_CR32","doi-asserted-by":"crossref","unstructured":"Mansilla, L.A., Miranda, P.A., Cappabianco, F.A.: Oriented image foresting transform segmentation with connectivity constraints. In: ICIP (2016)","DOI":"10.1109\/ICIP.2016.7532820"},{"key":"22_CR33","unstructured":"OpenCV: Open source computer vision library (2015)"},{"key":"22_CR34","doi-asserted-by":"crossref","unstructured":"Perazzi, F., Pont-Tuset, J., McWilliams, B., Gool, L.V., Gross, M., Sorkine-Hornung, A.: A benchmark dataset and evaluation methodology for video object segmentation. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.85"},{"key":"22_CR35","doi-asserted-by":"crossref","unstructured":"Rother, C., Kolmogorov, V., Blake, A.: Grabcut: Interactive foreground extraction using iterated graph cuts. In: ACM ToG (2004)","DOI":"10.1145\/1186562.1015720"},{"key":"22_CR36","doi-asserted-by":"crossref","unstructured":"Russakovsky, O., et al.: Imagenet large scale visual recognition challenge. IJCV (2015)","DOI":"10.1007\/s11263-015-0816-y"},{"key":"22_CR37","doi-asserted-by":"crossref","unstructured":"Sofiiuk, K., Petrov, I., Barinova, O., Konushin, A.: f-BRS: Rethinking backpropagating refinement for interactive segmentation. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00865"},{"key":"22_CR38","doi-asserted-by":"crossref","unstructured":"Sofiiuk, K., Petrov, I., Barinova, O., Konushin, A.: f-brs: Rethinking backpropagating refinement for interactive segmentation. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.00865"},{"key":"22_CR39","doi-asserted-by":"crossref","unstructured":"Vicente, S., Kolmogorov, V., Rother, C.: Graph cut based image segmentation with connectivity priors. In: CVPR (2008)","DOI":"10.1109\/CVPR.2008.4587440"},{"key":"22_CR40","doi-asserted-by":"crossref","unstructured":"Voigtlaender, P., Chai, Y., Schroff, F., Adam, H., Leibe, B., Chen, L.C.: FEELVOS: Fast end-to-end embedding learning for video object segmentation. In: CVPR (2019)","DOI":"10.1109\/CVPR.2019.00971"},{"key":"22_CR41","unstructured":"Wang, G., et al.: DeepIGeoS: a deep interactive geodesic framework for medical image segmentation. TPAMI (2018)"},{"key":"22_CR42","doi-asserted-by":"crossref","unstructured":"Wu, J., Zhao, Y., Zhu, J.Y., Luo, S., Tu, Z.: Milcut: A sweeping line multiple instance learning paradigm for interactive image segmentation. In: CVPR (2014)","DOI":"10.1109\/CVPR.2014.40"},{"key":"22_CR43","doi-asserted-by":"crossref","unstructured":"Xu, N., Price, B., Cohen, S., Yang, J., Huang, T.: Deep GrabCut for object selection. In: BMVC (2017)","DOI":"10.5244\/C.31.182"},{"key":"22_CR44","doi-asserted-by":"crossref","unstructured":"Xu, N., Price, B., Cohen, S., Yang, J., Huang, T.S.: Deep interactive object selection. In: CVPR (2016)","DOI":"10.1109\/CVPR.2016.47"},{"key":"22_CR45","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"332","DOI":"10.1007\/978-3-030-58558-7_20","volume-title":"Computer Vision \u2013 ECCV 2020","author":"Z Yang","year":"2020","unstructured":"Yang, Z., Wei, Y., Yang, Y.: Collaborative video object segmentation by foreground-background integration. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12350, pp. 332\u2013348. Springer, Cham (2020). https:\/\/doi.org\/10.1007\/978-3-030-58558-7_20"},{"key":"22_CR46","doi-asserted-by":"crossref","unstructured":"Yu, J., Lin, Z., Yang, J., Shen, X., Lu, X., Huang, T.S.: Free-form image inpainting with gated convolution. In: ICCV, pp. 4471\u20134480 (2019)","DOI":"10.1109\/ICCV.2019.00457"},{"key":"22_CR47","doi-asserted-by":"crossref","unstructured":"Zeng, Y., Zhang, P., Zhang, J., Lin, Z., Lu, H.: Towards high-resolution salient object detection. In: ICCV (2019)","DOI":"10.1109\/ICCV.2019.00733"},{"key":"22_CR48","doi-asserted-by":"crossref","unstructured":"Zhang, S., Liew, J.H., Wei, Y., Wei, S., Zhao, Y.: Interactive object segmentation with inside-outside guidance. In: CVPR (2020)","DOI":"10.1109\/CVPR42600.2020.01225"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ECCV 2022"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-031-19818-2_22","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T14:28:45Z","timestamp":1710340125000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-031-19818-2_22"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022]]},"ISBN":["9783031198175","9783031198182"],"references-count":48,"URL":"https:\/\/doi.org\/10.1007\/978-3-031-19818-2_22","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2022]]},"assertion":[{"value":"22 October 2022","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":"Tel Aviv","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Israel","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":"23 October 2022","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"27 October 2022","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"17","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"eccv2022","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"https:\/\/eccv2022.ecva.net\/","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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"5804","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":"1645","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":"28% - 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":"3.21","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":"3.91","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)"}}]}}