{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,3,26]],"date-time":"2025-03-26T04:53:23Z","timestamp":1742964803415,"version":"3.40.3"},"publisher-location":"Cham","reference-count":29,"publisher":"Springer International Publishing","isbn-type":[{"type":"print","value":"9783030880033"},{"type":"electronic","value":"9783030880040"}],"license":[{"start":{"date-parts":[[2021,1,1]],"date-time":"2021-01-01T00:00:00Z","timestamp":1609459200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/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":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2021]]},"DOI":"10.1007\/978-3-030-88004-0_35","type":"book-chapter","created":{"date-parts":[[2021,10,21]],"date-time":"2021-10-21T23:06:25Z","timestamp":1634857585000},"page":"429-442","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["ReFlowNet: Revisiting Coarse-to-fine Learning of Optical Flow"],"prefix":"10.1007","author":[{"given":"Leyang","family":"Xu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zongqing","family":"Lu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2021,10,22]]},"reference":[{"key":"35_CR1","doi-asserted-by":"crossref","unstructured":"Butler, D.J., Wulff, J., Stanley, G.B., Black, M.J.: A naturalistic open source movie for optical flow evaluation. In: ECCV, pp. 611\u2013625 (2012)","DOI":"10.1007\/978-3-642-33783-3_44"},{"key":"35_CR2","doi-asserted-by":"crossref","unstructured":"Dosovitskiy, A., et al.: FlowNet: learning optical flow with convolutional networks. In: ICCV, pp. 2758\u20132766 (2015)","DOI":"10.1109\/ICCV.2015.316"},{"issue":"1","key":"35_CR3","doi-asserted-by":"publisher","first-page":"185","DOI":"10.1016\/0004-3702(81)90024-2","volume":"17","author":"BK Horn","year":"1981","unstructured":"Horn, B.K., Schunck, B.G.: Determining optical flow. Artif. Intell. 17(1), 185\u2013203 (1981)","journal-title":"Artif. Intell."},{"key":"35_CR4","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":"35_CR5","doi-asserted-by":"crossref","unstructured":"Hui, T.W., Tang, X., Loy, C.C.: LiteFlowNet: A lightweight convolutional neural network for optical flow estimation. In: CVPR, pp. 8981\u20138989 (2018)","DOI":"10.1109\/CVPR.2018.00936"},{"key":"35_CR6","doi-asserted-by":"crossref","unstructured":"Hur, J., Roth, S.: MirrorFlow: exploiting symmetries in joint optical flow and occlusion estimation. In: ICCV, pp. 312\u2013321 (2017)","DOI":"10.1109\/ICCV.2017.42"},{"key":"35_CR7","doi-asserted-by":"crossref","unstructured":"Hur, J., Roth, S.: Iterative residual refinement for joint optical flow and occlusion estimation. In: CVPR, pp. 5747\u20135756 (2019)","DOI":"10.1109\/CVPR.2019.00590"},{"key":"35_CR8","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: CVPR, pp. 2462\u20132470 (2017)","DOI":"10.1109\/CVPR.2017.179"},{"key":"35_CR9","doi-asserted-by":"crossref","unstructured":"Jonschkowski, R., Stone, A., Barron, J.T., Gordon, A., Konolige, K., Angelova, A.: What matters in unsupervised optical flow. arXiv preprint arXiv:2006.04902 (2020)","DOI":"10.1007\/978-3-030-58536-5_33"},{"key":"35_CR10","doi-asserted-by":"crossref","unstructured":"Liu, P., King, I., Lyu, M.R., Xu, J.: DDFlow: learning optical flow with unlabeled data distillation. In: AAAI, pp. 8770\u20138777 (2019)","DOI":"10.1609\/aaai.v33i01.33018770"},{"key":"35_CR11","doi-asserted-by":"crossref","unstructured":"Liu, P., Lyu, M., King, I., Xu, J.: SelFlow: self-supervised learning of optical flow. In: CVPR, pp. 4571\u20134580 (2019)","DOI":"10.1109\/CVPR.2019.00470"},{"key":"35_CR12","doi-asserted-by":"crossref","unstructured":"Lu, Y., Valmadre, J., Wang, H., Kannala, J., Harandi, M., Torr, P.: Devon: deformable volume network for learning optical flow. In: WACV, pp. 2705\u20132713 (2020)","DOI":"10.1109\/WACV45572.2020.9093590"},{"key":"35_CR13","unstructured":"Maurer, D., Bruhn, A.: ProFlow: learning to predict optical flow. arXiv preprint arXiv:1806.00800 (2018)"},{"key":"35_CR14","doi-asserted-by":"crossref","unstructured":"Meister, S., Hur, J., Roth, S.: UnFlow: unsupervised learning of optical flow with a bidirectional census loss. In: AAAI, pp. 7251\u20137259 (2018)","DOI":"10.1609\/aaai.v32i1.12276"},{"key":"35_CR15","doi-asserted-by":"crossref","unstructured":"Ranjan, A., Black, M.J.: Optical flow estimation using a spatial pyramid network. In: CVPR, pp. 4161\u20134170 (2017)","DOI":"10.1109\/CVPR.2017.291"},{"key":"35_CR16","doi-asserted-by":"crossref","unstructured":"Ren, Z., Gallo, O., Sun, D., Yang, M.H., Sudderth, E.B., Kautz, J.: A fusion approach for multi-frame optical flow estimation. In: WACV, pp. 2077\u20132086 (2019)","DOI":"10.1109\/WACV.2019.00225"},{"issue":"11","key":"35_CR17","doi-asserted-by":"publisher","first-page":"2553","DOI":"10.1109\/TPAMI.2018.2865351","volume":"41","author":"I Rocco","year":"2019","unstructured":"Rocco, I., Arandjelovic, R., Sivic, J.: Convolutional neural network architecture for geometric matching. IEEE Trans. Pattern Anal. Mach. Intell. 41(11), 2553\u20132567 (2019)","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"35_CR18","doi-asserted-by":"crossref","unstructured":"Sun, D., Yang, X., Liu, M.Y., Kautz, J.: PWC-Net: CNNs for optical flow using pyramid, warping, and cost volume. In: CVPR, pp. 8934\u20138943 (2018)","DOI":"10.1109\/CVPR.2018.00931"},{"key":"35_CR19","doi-asserted-by":"crossref","unstructured":"Sun, D., Yang, X., Liu, M.Y., Kautz, J.: Models matter, so does training: an empirical study of CNNs for optical flow estimation. IEEE Trans. Patt. Anal. Mach. Intell. 42, 1408\u20131423 (2020)","DOI":"10.1109\/TPAMI.2019.2894353"},{"key":"35_CR20","doi-asserted-by":"crossref","unstructured":"Wang, X., Girshick, R.B., Gupta, A., He, K.: Non-local neural networks. arXiv preprint arXiv:1711.07971 (2017)","DOI":"10.1109\/CVPR.2018.00813"},{"key":"35_CR21","doi-asserted-by":"crossref","unstructured":"Wang, Y., Yang, Y., Yang, Z., Zhao, L., Wang, P., Xu, W.: Occlusion aware unsupervised learning of optical flow. In: CVPR, pp. 4884\u20134893 (2018)","DOI":"10.1109\/CVPR.2018.00513"},{"key":"35_CR22","doi-asserted-by":"crossref","unstructured":"Woo, S., Park, J., Lee, J.Y., Kweon, I.S.: CBAM: convolutional block attention module. In: ECCV, pp. 3\u201319 (2018)","DOI":"10.1007\/978-3-030-01234-2_1"},{"key":"35_CR23","doi-asserted-by":"crossref","unstructured":"Xu, J., Ranftl, R., Koltun, V.: Accurate optical flow via direct cost volume processing. In: CVPR, pp. 5807\u20135815 (2017)","DOI":"10.1109\/CVPR.2017.615"},{"key":"35_CR24","unstructured":"Yang, G., Ramanan, D.: Volumetric correspondence networks for optical flow. In: NeurIPS, pp. 793\u2013803 (2019)"},{"key":"35_CR25","doi-asserted-by":"crossref","unstructured":"Yin, Z., Darrell, T., Yu, F.: Hierarchical discrete distribution decomposition for match density estimation. In: CVPR, pp. 6037\u20136046 (2019)","DOI":"10.1109\/CVPR.2019.00620"},{"key":"35_CR26","series-title":"Lecture Notes in Computer Science","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1007\/978-3-319-49409-8_1","volume-title":"Computer Vision \u2013 ECCV 2016 Workshops","author":"JJ Yu","year":"2016","unstructured":"Yu, J.J., Harley, A.W., Derpanis, K.G.: Back to basics: unsupervised learning of optical flow via brightness constancy and motion smoothness. In: Hua, G., J\u00e9gou, H. (eds.) ECCV 2016. LNCS, vol. 9915, pp. 3\u201310. Springer, Cham (2016). https:\/\/doi.org\/10.1007\/978-3-319-49409-8_1"},{"key":"35_CR27","doi-asserted-by":"crossref","unstructured":"Zhang, X., Ma, D., Ouyang, X., Jiang, S., Gan, L., Agam, G.: Layered optical flow estimation using a deep neural network with a soft mask. In: IJCAI, pp. 1170\u20131176 (2018)","DOI":"10.24963\/ijcai.2018\/163"},{"key":"35_CR28","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, K., Li, K., Wang, L., Zhong, B., Fu, Y.: Image super-resolution using very deep residual channel attention networks. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV, pp. 294\u2013310 (2018)","DOI":"10.1007\/978-3-030-01234-2_18"},{"key":"35_CR29","doi-asserted-by":"crossref","unstructured":"Zhao, S., Sheng, Y., Dong, Y., Chang, E.I.C., Xu, Y.: MaskFlownet: asymmetric feature matching with learnable occlusion mask. In: CVPR, pp. 6277\u20136286 (2020)","DOI":"10.1109\/CVPR42600.2020.00631"}],"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\/978-3-030-88004-0_35","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,3,7]],"date-time":"2024-03-07T15:09:47Z","timestamp":1709824187000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/978-3-030-88004-0_35"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021]]},"ISBN":["9783030880033","9783030880040"],"references-count":29,"URL":"https:\/\/doi.org\/10.1007\/978-3-030-88004-0_35","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":"22 October 2021","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":"Beijing","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":"2021","order":5,"name":"conference_year","label":"Conference Year","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"29 October 2021","order":7,"name":"conference_start_date","label":"Conference Start Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"1 November 2021","order":8,"name":"conference_end_date","label":"Conference End Date","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"4","order":9,"name":"conference_number","label":"Conference Number","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"ccprcv2021","order":10,"name":"conference_id","label":"Conference ID","group":{"name":"ConferenceInfo","label":"Conference Information"}},{"value":"http:\/\/www.prcv.cn\/2021\/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":"CMT","order":2,"name":"conference_management_system","label":"Conference Management System","group":{"name":"ConfEventPeerReviewInformation","label":"Peer Review Information (provided by the conference organizers)"}},{"value":"513","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":"201","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":"5","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":"There were 30 oral and 171 poster presentations at the conference.","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)"}}]}}