{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T17:14:07Z","timestamp":1743009247155,"version":"3.40.3"},"publisher-location":"Cham","reference-count":48,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030695316"},{"type":"electronic","value":"9783030695323"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-69532-3_26","type":"book-chapter","created":{"date-parts":[[2021,2,26]],"date-time":"2021-02-26T08:04:33Z","timestamp":1614326673000},"page":"421-437","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Self-supervised Sparse to Dense Motion Segmentation"],"prefix":"10.1007","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6477-6631","authenticated-orcid":false,"given":"Amirhossein","family":"Kardoost","sequence":"first","affiliation":[]},{"given":"Kalun","family":"Ho","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4880-7511","authenticated-orcid":false,"given":"Peter","family":"Ochs","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8437-7993","authenticated-orcid":false,"given":"Margret","family":"Keuper","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,2,27]]},"reference":[{"key":"26_CR1","unstructured":"Koffka, K.: Principles of Gestalt Psychology. Hartcourt Brace Jovanovich, NewYork (1935)"},{"key":"26_CR2","doi-asserted-by":"publisher","first-page":"1187","DOI":"10.1109\/TPAMI.2013.242","volume":"36","author":"P Ochs","year":"2014","unstructured":"Ochs, P., Malik, J., Brox, T.: Segmentation of moving objects by long term video analysis. IEEE TPAMI 36, 1187\u20131200 (2014)","journal-title":"IEEE TPAMI"},{"key":"26_CR3","doi-asserted-by":"crossref","unstructured":"Tokmakov, P., Alahari, K., Schmid, C.: Learning video object segmentation with visual memory. In: ICCV (2017)","DOI":"10.1109\/ICCV.2017.480"},{"key":"26_CR4","doi-asserted-by":"crossref","unstructured":"Tokmakov, P., Alahari, K., Schmid, C.: Learning motion patterns in videos. In: CVPR (2017)","DOI":"10.1109\/CVPR.2017.64"},{"key":"26_CR5","doi-asserted-by":"crossref","unstructured":"Keuper, M., Andres, B., Brox, T.: Motion trajectory segmentation via minimum cost multicuts. In: IEEE International Conference on Computer Vision (ICCV) (2015)","DOI":"10.1109\/ICCV.2015.374"},{"key":"26_CR6","doi-asserted-by":"crossref","unstructured":"Keuper, M.: Higher-order minimum cost lifted multicuts for motion segmentation. In: The IEEE International Conference on Computer Vision (ICCV) (2017)","DOI":"10.1109\/ICCV.2017.455"},{"key":"26_CR7","doi-asserted-by":"crossref","unstructured":"Brox, T., Malik, J.: Object segmentation by long term analysis of point trajectories. In: European Conference on Computer Vision (ECCV). Lecture Notes in Computer Science, Springer (2010)","DOI":"10.1007\/978-3-642-15555-0_21"},{"key":"26_CR8","doi-asserted-by":"crossref","unstructured":"Fragkiadaki, K., Zhang, W., Zhang, G., Shi, J.: Two-granularity tracking: mediating trajectory and detection graphs for tracking under occlusions. In: ECCV (2012)","DOI":"10.1007\/978-3-642-33715-4_40"},{"key":"26_CR9","doi-asserted-by":"crossref","unstructured":"Shi, F., Zhou, Z., Xiao, J., Wu, W.: Robust trajectory clustering for motion segmentation. In: ICCV (2013)","DOI":"10.1109\/ICCV.2013.383"},{"key":"26_CR10","doi-asserted-by":"crossref","unstructured":"Rao, S.R., Tron, R., Vidal, R., Yi Ma: Motion segmentation via robust subspace separation in the presence of outlying, incomplete, or corrupted trajectories. In: 2008 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1\u20138 (2008)","DOI":"10.1109\/CVPR.2008.4587437"},{"key":"26_CR11","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"234","DOI":"10.1007\/978-3-319-24574-4_28","volume-title":"Medical Image Computing and Computer-Assisted Intervention \u2014 MICCAI 2015","author":"O Ronneberger","year":"2015","unstructured":"Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234\u2013241. Springer, Cham (2015). https:\/\/doi.org\/10.1007\/978-3-319-24574-4_28"},{"key":"26_CR12","doi-asserted-by":"crossref","unstructured":"Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: ImageNet: a large-scale hierarchical image database. In: CVPR09 (2009)","DOI":"10.1109\/CVPR.2009.5206848"},{"key":"26_CR13","doi-asserted-by":"crossref","unstructured":"Dosovitskiy, A., et al.: Flownet: learning optical flow with convolutional networks. In: IEEE International Conference on Computer Vision (ICCV) (2015)","DOI":"10.1109\/ICCV.2015.316"},{"key":"26_CR14","doi-asserted-by":"crossref","unstructured":"Perazzi, F., Pont-Tuset, J., McWilliams, B., Van Gool, L., Gross, M., Sorkine-Hornung, A.: A benchmark dataset and evaluation methodology for video object segmentation, pp. 724\u2013732 (2016)","DOI":"10.1109\/CVPR.2016.85"},{"key":"26_CR15","doi-asserted-by":"crossref","unstructured":"Maczyta, L., Bouthemy, P., Meur, O.: CNN-based temporal detection of motion saliency in videos. Pattern Recognition Letters 128 (2019)","DOI":"10.1016\/j.patrec.2019.09.016"},{"key":"26_CR16","doi-asserted-by":"crossref","unstructured":"Fragkiadaki, K., Arbelaez, P., Felsen, P., Malik, J.: Learning to segment moving objects in videos, pp. 4083\u20134090 (2015)","DOI":"10.1109\/CVPR.2015.7299035"},{"key":"26_CR17","doi-asserted-by":"crossref","unstructured":"Siam, M., Mahgoub, H., Zahran, M., Yogamani, S., Jagersand, M., El-Sallab, A.: Modnet: motion and appearance based moving object detection network for autonomous driving. In: 2018 21st International Conference on Intelligent Transportation Systems (ITSC), pp. 2859\u20132864 (2018)","DOI":"10.1109\/ITSC.2018.8569744"},{"key":"26_CR18","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"433","DOI":"10.1007\/978-3-319-46484-8_26","volume-title":"Computer Vision \u2013 ECCV 2016","author":"P Bideau","year":"2016","unstructured":"Bideau, P., Learned-Miller, E.: It\u2019s moving! a probabilistic model for causal motion segmentation in moving camera videos. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 433\u2013449. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-46484-8_26"},{"key":"26_CR19","unstructured":"Cremers, D.: A variational framework for image segmentation combining motion estimation and shape regularization. In: Proceedings of the 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR\u201903, Washington, DC, USA, IEEE Computer Society, pp. 53\u201358 (2003)"},{"key":"26_CR20","doi-asserted-by":"crossref","unstructured":"Lao, D., Sundaramoorthi, G.: Extending layered models to 3d motion. In: ECCV (2018)","DOI":"10.1007\/978-3-030-01249-6_27"},{"key":"26_CR21","doi-asserted-by":"crossref","unstructured":"Hu, Y.T., Huang, J.B., Schwing, A.: In: Unsupervised Video Object Segmentation Using Motion Saliency-Guided Spatio-Temporal Propagation: 15th EuropeanConference, Munich, Germany, 8-14 September 2018, Proceedings, Part I, pp. 813\u2013830 (2018)","DOI":"10.1007\/978-3-030-01246-5_48"},{"key":"26_CR22","doi-asserted-by":"crossref","unstructured":"Tsai, Y.H., Yang, M.H., Black, M.J.: Video segmentation via object flow. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)","DOI":"10.1109\/CVPR.2016.423"},{"key":"26_CR23","doi-asserted-by":"crossref","unstructured":"Papazoglou, A., Ferrari, V.: Fast object segmentation in unconstrained video. In: 2013 IEEE International Conference on Computer Vision, pp. 1777\u20131784 (2013)","DOI":"10.1109\/ICCV.2013.223"},{"key":"26_CR24","unstructured":"Elhamifar, E., Vidal, R.: Sparse subspace clustering. In: ICCV (2013)"},{"key":"26_CR25","doi-asserted-by":"crossref","unstructured":"Ochs, P., Brox, T.: Higher order motion models and spectral clustering. In: IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) (2012)","DOI":"10.1109\/CVPR.2012.6247728"},{"key":"26_CR26","unstructured":"Jianbo, S., Malik, J.: Motion segmentation and tracking using normalized cuts. In: Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271), pp. 1154\u20131160 (1998)"},{"key":"26_CR27","doi-asserted-by":"crossref","unstructured":"Fragkiadaki, K., Shi, J.: Detection free tracking: exploiting motion and topology for segmenting and tracking under entanglement. In: CVPR (2011)","DOI":"10.1109\/CVPR.2011.5995366"},{"key":"26_CR28","doi-asserted-by":"crossref","unstructured":"Chopra, S., Hadsell, R., LeCun, Y.: Learning a similarity metric discriminatively, with application to face verification. In: 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR\u201905). vol. 1, pp. 539\u2013546 (2005)","DOI":"10.1109\/CVPR.2005.202"},{"key":"26_CR29","doi-asserted-by":"crossref","unstructured":"Bhattacharyya, A., Fritz, M., Schiele, B.: Long-term on-board prediction of people in traffic scenes under uncertainty (2018)","DOI":"10.1109\/CVPR.2018.00441"},{"key":"26_CR30","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"117","DOI":"10.1007\/978-3-319-45886-1_10","volume-title":"Pattern Recognition","author":"S M\u00fcller","year":"2016","unstructured":"M\u00fcller, S., Ochs, P., Weickert, J., Graf, N.: Robust interactive multi-label segmentation with an advanced edge detector. In: Rosenhahn, B., Andres, B. (eds.) GCPR 2016. LNCS, vol. 9796, pp. 117\u2013128. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-45886-1_10"},{"key":"26_CR31","unstructured":"Shi, J.: Video segmentation by tracing discontinuities in a trajectory embedding. In: Proceedings of the 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). CVPR 2012, Washington, DC, USA, IEEE Computer Society, pp. 1846\u20131853 (2012)"},{"key":"26_CR32","doi-asserted-by":"crossref","unstructured":"Andres, B., et al.: Globally optimal closed-surface segmentation for connectomics. In: ECCV (2012)","DOI":"10.1007\/978-3-642-33712-3_56"},{"key":"26_CR33","doi-asserted-by":"publisher","first-page":"89","DOI":"10.1023\/B:MACH.0000033116.57574.95","volume":"56","author":"N Bansal","year":"2004","unstructured":"Bansal, N., Blum, A., Chawla, S.: Correlation clustering. Machine Learning 56, 89\u2013113 (2004)","journal-title":"Machine Learning"},{"key":"26_CR34","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1002\/j.1538-7305.1970.tb01770.x","volume":"49","author":"BW Kernighan","year":"1970","unstructured":"Kernighan, B.W., Lin, S.: An efficient heuristic procedure for partitioning graphs. The Bell Syst. Technical J. 49, 291\u2013307 (1970)","journal-title":"The Bell Syst. Technical J."},{"key":"26_CR35","doi-asserted-by":"crossref","unstructured":"Keuper, M., Levinkov, E., Bonneel, N., Lavoue, G., Brox, T., Andres, B.: Efficient decomposition of image and mesh graphs by lifted multicuts. In: IEEE International Conference on Computer Vision (ICCV) (2015)","DOI":"10.1109\/ICCV.2015.204"},{"key":"26_CR36","doi-asserted-by":"crossref","unstructured":"Beier, T., Andres, B., K\u00f6the, U., Hamprecht, F.A.: An efficient fusion move algorithm for the minimum cost lifted multicut problem. In: ECCV (2016)","DOI":"10.1007\/978-3-319-46475-6_44"},{"key":"26_CR37","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"74","DOI":"10.1007\/978-3-030-20870-7_5","volume-title":"Computer Vision \u2013 ACCV 2018","author":"A Kardoost","year":"2019","unstructured":"Kardoost, A., Keuper, M.: Solving minimum cost lifted multicut problems by node agglomeration. In: Jawahar, C.V., Li, H., Mori, G., Schindler, K. (eds.) ACCV 2018. LNCS, vol. 11364, pp. 74\u201389. Springer, Cham (2019). https:\/\/doi.org\/10.1007\/978-3-030-20870-7_5"},{"key":"26_CR38","unstructured":"Bailoni, A., Pape, C., Wolf, S., Beier, T., Kreshuk, A., Hamprecht, F.: A generalized framework for agglomerative clustering of signed graphs applied to instance segmentation (2019)"},{"key":"26_CR39","doi-asserted-by":"crossref","unstructured":"Keuper, M., Andres, B., Brox, T.: Motion trajectory segmentation via minimum cost multicuts. In: ICCV (2015)","DOI":"10.1109\/ICCV.2015.374"},{"key":"26_CR40","doi-asserted-by":"crossref","unstructured":"Siam, M., Gamal, M., Abdel-Razek, M., Yogamani, S., Jagersand, M.: Rtseg: real-time semantic segmentation comparative study. In: 2018 25th IEEE International Conference on Image Processing (ICIP), pp. 1603\u20131607 (2018)","DOI":"10.1109\/ICIP.2018.8451495"},{"key":"26_CR41","doi-asserted-by":"crossref","unstructured":"Siam, M., Elkerdawy, S., Jagersand, M., Yogamani, S.: Deep semantic segmentation for automated driving: Taxonomy, roadmap and challenges. In: 2017 IEEE 20th International Conference on Intelligent Transportation Systems (ITSC), pp. 1\u20138 (2017)","DOI":"10.1109\/ITSC.2017.8317714"},{"key":"26_CR42","doi-asserted-by":"crossref","unstructured":"Fu, J., Liu, J., Wang, Y., Zhou, J., Wang, C., Lu, H.: Stacked deconvolutional network for semantic segmentation. IEEE Transactions on Image Processing (2019)","DOI":"10.1109\/TIP.2019.2895460"},{"key":"26_CR43","doi-asserted-by":"publisher","first-page":"358","DOI":"10.1109\/4.996","volume":"23","author":"N Kanopoulos","year":"1988","unstructured":"Kanopoulos, N., Vasanthavada, N., Baker, R.L.: Design of an image edge detection filter using the sobel operator. IEEE J. Solid-State circ. 23, 358\u2013367 (1988)","journal-title":"IEEE J. Solid-State circ."},{"key":"26_CR44","unstructured":"Kr\u00e4henb\u00fchl, P., Koltun, V.: Efficient inference in fully connected CRFS with gaussian edge potentials. CoRR abs\/1210.5644 (2012)"},{"key":"26_CR45","unstructured":"Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: Semantic image segmentation with deep convolutional nets and fully connected CRFS. In: ICLR (2015)"},{"key":"26_CR46","doi-asserted-by":"crossref","unstructured":"Zheng, S., et al.: Conditional random fields as recurrent neural networks. In: International Conference on Computer Vision (ICCV) (2015)","DOI":"10.1109\/ICCV.2015.179"},{"key":"26_CR47","doi-asserted-by":"crossref","unstructured":"Brox, T., Bregler, C., Malik, J.: Large displacement optical flow. In: IEEE International Conference on Computer Vision and Pattern Recognition (CVPR) (2009)","DOI":"10.1109\/CVPR.2009.5206697"},{"key":"26_CR48","doi-asserted-by":"crossref","unstructured":"Ilg, E., Mayer, N., Saikia, T., Keuper, M., Dosovitskiy, A., Brox, T.: Flownet 2.0: evolution of optical flow estimation with deep networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)","DOI":"10.1109\/CVPR.2017.179"}],"container-title":["Lecture Notes in Computer Science","Computer Vision \u2013 ACCV 2020"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/link.springer.com\/content\/pdf\/10.1007\/978-3-030-69532-3_26","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,24]],"date-time":"2024-08-24T22:13:32Z","timestamp":1724537612000},"score":1,"resource":{"primary":{"URL":"http:\/\/link.springer.com\/10.1007\/978-3-030-69532-3_26"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030695316","9783030695323"],"references-count":48,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-69532-3_26","relation":{},"ISSN":["0302-9743","1611-3349"],"issn-type":[{"type":"print","value":"0302-9743"},{"type":"electronic","value":"1611-3349"}],"subject":[],"published":{"date-parts":[[2021]]},"assertion":[{"value":"27 February 2021","order":1,"name":"first_online","label":"First Online","group":{"name":"ChapterHistory","label":"Chapter History"}},{"value":"ACCV","order":1,"name":"conference_acronym","label":"Conference Acronym","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Asian Conference on Computer Vision","order":2,"name":"conference_name","label":"Conference Name","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Kyoto","order":3,"name":"conference_city","label":"Conference City","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"Japan","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":"30 November 2020","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4 December 2020","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"15","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"accv2020","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/accv2020.kyoto\/","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","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"768","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":"254","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":"33% - 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)"}},{"value":"The conference was held virtually.","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)"}}]}}