{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,22]],"date-time":"2026-04-22T19:49:37Z","timestamp":1776887377196,"version":"3.51.2"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"17","license":[{"start":{"date-parts":[[2022,6,20]],"date-time":"2022-06-20T00:00:00Z","timestamp":1655683200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2022,6,20]],"date-time":"2022-06-20T00:00:00Z","timestamp":1655683200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"funder":[{"name":"CAAI-Huawei MindSpore Open Fund"},{"DOI":"10.13039\/501100005046","name":"Natural Science Foundation of Heilongjiang Province of China","doi-asserted-by":"crossref","award":["LH2021F011"],"award-info":[{"award-number":["LH2021F011"]}],"id":[{"id":"10.13039\/501100005046","id-type":"DOI","asserted-by":"crossref"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61401113"],"award-info":[{"award-number":["61401113"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100012226","name":"Fundamental Research Funds for the Central Universities of China","doi-asserted-by":"crossref","award":["3072022CF0801"],"award-info":[{"award-number":["3072022CF0801"]}],"id":[{"id":"10.13039\/501100012226","id-type":"DOI","asserted-by":"crossref"}]},{"name":"Key Laboratory of Advanced Marine Communication and Information Technology Open Fund","award":["AMCIT2103-03"],"award-info":[{"award-number":["AMCIT2103-03"]}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2022,9]]},"DOI":"10.1007\/s00521-022-07483-z","type":"journal-article","created":{"date-parts":[[2022,6,20]],"date-time":"2022-06-20T18:02:41Z","timestamp":1655748161000},"page":"14341-14353","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Unsupervised optical flow estimation method based on transformer and occlusion compensation"],"prefix":"10.1007","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6185-833X","authenticated-orcid":false,"given":"Xuezhi","family":"Xiang","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rokia","family":"Abdein","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ning","family":"Lv","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2022,6,20]]},"reference":[{"key":"7483_CR1","doi-asserted-by":"crossref","unstructured":"Ranftl R, Bochkovskiy A, Koltun V (2021) Vision transformers for dense prediction. In: Proceedings of the IEEE\/CVF International conference on computer vision, p 12179\u201312188","DOI":"10.1109\/ICCV48922.2021.01196"},{"key":"7483_CR2","doi-asserted-by":"crossref","unstructured":"Sun D, Yang X, Liu MY, Kautz J (2018) Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume. In: Proceedings of the IEEE conference on computer vision and pattern recognitionn; p 8934\u20138943","DOI":"10.1109\/CVPR.2018.00931"},{"key":"7483_CR3","doi-asserted-by":"crossref","unstructured":"Dosovitskiy A, Fischer P, Ilg E, Hausser P, Hazirbas C, Golkov V, et\u00a0al. (2015) Flownet: Learning optical flow with convolutional networks. In: Proceedings of the IEEE International conference on computer vision, p 2758\u20132766","DOI":"10.1109\/ICCV.2015.316"},{"key":"7483_CR4","unstructured":"Zhang H, Goodfellow I, Metaxas D, Odena A (2019) Self-attention generative adversarial networks. In: International conference on machine learning. PMLR, p 7354\u20137363"},{"key":"7483_CR5","unstructured":"Parmar N, Vaswani A, Uszkoreit J, Kaiser L, Shazeer N, Ku A, et\u00a0al. (2018) Image transformer. In: International conference on machine learning. PMLR, p 4055\u20134064"},{"key":"7483_CR6","doi-asserted-by":"crossref","unstructured":"Yang B, Xie H, Li H, Li N, Liu A, Ren Z, et\u00a0al. (2020) Unsupervised Optical Flow Estimation Based on Improved Feature Pyramid. Neural Process Lett. p 1601\u20131612","DOI":"10.1007\/s11063-020-10328-2"},{"key":"7483_CR7","doi-asserted-by":"crossref","unstructured":"Zhai M, Xiang X, Zhang R, Lv N, El\u00a0Saddik A (2019) Learning optical flow using deep dilated residual networks. IEEE Access. p 22566\u201322578","DOI":"10.1109\/ACCESS.2019.2898988"},{"key":"7483_CR8","doi-asserted-by":"crossref","unstructured":"Zhai M, Xiang X, Zhang R, Lv N, El\u00a0Saddik A (2019) Ad-net: attention guided network for optical flow estimation using dilated convolution. In: ICASSP 2019-2019 IEEE International conference on acoustics, speech and signal processing (ICASSP). IEEE, p 2207\u20132211","DOI":"10.1109\/ICASSP.2019.8682229"},{"key":"7483_CR9","doi-asserted-by":"crossref","unstructured":"Zhai M, Xiang X, Zhang R, Lv N, El\u00a0Saddik A (2019) Optical flow estimation using dual self-attention pyramid networks. IEEE Trans Circ Syst Video Technol, p 3663\u20133674","DOI":"10.1109\/TCSVT.2019.2943140"},{"key":"7483_CR10","doi-asserted-by":"publisher","first-page":"8163","DOI":"10.1109\/TIP.2020.3011807","volume":"29","author":"C Yan","year":"2020","unstructured":"Yan C, Zheng Q, Chang X, Luo M, Yeh CH, Hauptman AG (2020) Semantics-preserving graph propagation for zero-shot object detection. IEEE Trans Image Process 29:8163\u20138176","journal-title":"IEEE Trans Image Process"},{"issue":"1","key":"7483_CR11","first-page":"1","volume":"12","author":"C Yan","year":"2020","unstructured":"Yan C, Chang X, Luo M, Zheng Q, Zhang X, Li Z et al (2020) Self-weighted robust LDA for multiclass classification with edge classes. ACM Trans Intell Syst Technol (TIST) 12(1):1\u201319","journal-title":"ACM Trans Intell Syst Technol (TIST)"},{"issue":"7","key":"7483_CR12","doi-asserted-by":"publisher","first-page":"1502","DOI":"10.1109\/TNNLS.2015.2441735","volume":"27","author":"X Chang","year":"2015","unstructured":"Chang X, Nie F, Wang S, Yang Y, Zhou X, Zhang C (2015) Compound rank-$$k$$ projections for bilinear analysis. IEEE Trans Neural Netw Learn Syst 27(7):1502\u20131513","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"7483_CR13","doi-asserted-by":"crossref","unstructured":"Carion N, Massa F, Synnaeve G, Usunier N, Kirillov A, Zagoruyko S (2020) End-to-end object detection with transformers. In: European conference on computer vision. Springer, p 213\u2013229","DOI":"10.1007\/978-3-030-58452-8_13"},{"key":"7483_CR14","unstructured":"Dosovitskiy A, Beyer L, Kolesnikov A, Weissenborn D, Zhai X, Unterthiner T, et\u00a0al. (2021) An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale. In: International conference on learning representations, https:\/\/openreview.net\/forum?id=YicbFdNTTy"},{"key":"7483_CR15","doi-asserted-by":"crossref","unstructured":"Zheng S, Lu J, Zhao H, Zhu X, Luo Z, Wang Y, et\u00a0al. (2021) Rethinking semantic segmentation from a sequence-to-sequence perspective with transformers. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, p 6881\u20136890","DOI":"10.1109\/CVPR46437.2021.00681"},{"key":"7483_CR16","doi-asserted-by":"crossref","unstructured":"Chen X, Yan B, Zhu J, Wang D, Yang X, Lu H (2021) Transformer tracking. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, p 8126\u20138135","DOI":"10.1109\/CVPR46437.2021.00803"},{"key":"7483_CR17","unstructured":"Mudassar BA, Mukhopadhyay S (2019) Rethinking convolutional feature extraction for small object detection. In: BMVC, p 234"},{"key":"7483_CR18","doi-asserted-by":"crossref","unstructured":"Meister S, Hur J, Roth S (2018) Unflow: Unsupervised learning of optical flow with a bidirectional census loss. In: Proceedings of the AAAI conference on artificial intelligence. 32","DOI":"10.1609\/aaai.v32i1.12276"},{"key":"7483_CR19","doi-asserted-by":"crossref","unstructured":"Wang Y, Yang Y, Yang Z, Zhao L, Wang P, Xu W (2018) Occlusion aware unsupervised learning of optical flow. In: Proceedings of the IEEE conference on computer vision and pattern recognition, p 4884\u20134893","DOI":"10.1109\/CVPR.2018.00513"},{"key":"7483_CR20","doi-asserted-by":"crossref","unstructured":"Ilg E, Mayer N, Saikia T, Keuper M, Dosovitskiy A, Brox T (2017) Flownet 2.0: Evolution of optical flow estimation with deep networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, p 2462\u20132470","DOI":"10.1109\/CVPR.2017.179"},{"key":"7483_CR21","doi-asserted-by":"crossref","unstructured":"Hui TW, Tang X, Loy CC (2018) Liteflownet: a lightweight convolutional neural network for optical flow estimation. In: Proceedings of the IEEE conference on computer vision and pattern recognition, p 8981\u20138989","DOI":"10.1109\/CVPR.2018.00936"},{"key":"7483_CR22","doi-asserted-by":"crossref","unstructured":"Hur J, Roth S (2019) Iterative residual refinement for joint optical flow and occlusion estimation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, p 5754\u20135763","DOI":"10.1109\/CVPR.2019.00590"},{"key":"7483_CR23","doi-asserted-by":"crossref","unstructured":"Zhao S, Sheng Y, Dong Y, Chang EI, Xu Y, et\u00a0al. (2020) Maskflownet: asymmetric feature matching with learnable occlusion mask. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, p 6278\u20136287","DOI":"10.1109\/CVPR42600.2020.00631"},{"key":"7483_CR24","doi-asserted-by":"crossref","unstructured":"Teed Z, Deng J. (2020) Raft: recurrent all-pairs field transforms for optical flow. In: European conference on computer vision. Springer, p 402\u2013419","DOI":"10.1007\/978-3-030-58536-5_24"},{"key":"7483_CR25","doi-asserted-by":"crossref","unstructured":"Liu P, King I, Lyu MR, Xu J (2019) Ddflow: Learning optical flow with unlabeled data distillation. In: Proceedings of the AAAI conference on artificial intelligence. 33: 8770\u20138777","DOI":"10.1609\/aaai.v33i01.33018770"},{"key":"7483_CR26","doi-asserted-by":"crossref","unstructured":"Liu L, Zhang J, He R, Liu Y, Wang Y, Tai Y, et\u00a0al. (2020) Learning by analogy: reliable supervision from transformations for unsupervised optical flow estimation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, p 6489\u20136498","DOI":"10.1109\/CVPR42600.2020.00652"},{"key":"7483_CR27","doi-asserted-by":"crossref","unstructured":"Wolf T, Chaumond J, Debut L, Sanh V, Delangue C, Moi A, et\u00a0al. (2020) Transformers: state-of-the-art natural language processing. In: Proceedings of the 2020 conference on empirical methods in natural language processing: system demonstrations, p 38\u201345","DOI":"10.18653\/v1\/2020.emnlp-demos.6"},{"key":"7483_CR28","doi-asserted-by":"crossref","unstructured":"Wang X, Girshick R, Gupta A, He K (2018) Non-local neural networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition, p 7794\u20137803","DOI":"10.1109\/CVPR.2018.00813"},{"key":"7483_CR29","doi-asserted-by":"crossref","unstructured":"Ilg E, Saikia T, Keuper M, Brox T (2018) Occlusions, motion and depth boundaries with a generic network for disparity, optical flow or scene flow estimation. In: Proceedings of the European conference on computer vision (ECCV), p 614\u2013630","DOI":"10.1007\/978-3-030-01258-8_38"},{"key":"7483_CR30","doi-asserted-by":"crossref","unstructured":"Jason JY, Harley AW, Derpanis KG (2016) Back to basics: unsupervised learning of optical flow via brightness constancy and motion smoothness. In: European conference on computer vision. Springer, p 3\u201310","DOI":"10.1007\/978-3-319-49409-8_1"},{"key":"7483_CR31","doi-asserted-by":"crossref","unstructured":"Ren Z, Yan J, Ni B, Liu B, Yang X, Zha H (2017) Unsupervised deep learning for optical flow estimation. In: Thirty-First AAAI conference on artificial intelligence","DOI":"10.1609\/aaai.v31i1.10723"},{"key":"7483_CR32","doi-asserted-by":"crossref","unstructured":"Liu P, Lyu M, King I, Xu J (2019) Selflow: self-supervised learning of optical flow. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, p 4571\u20134580","DOI":"10.1109\/CVPR.2019.00470"},{"key":"7483_CR33","unstructured":"Adam P, Sam G, Soumith C, Gregory C, Edward Y, Zachary D, et\u00a0al. (2017) Automatic differentiation in pytorch. In: Proceedings of neural information processing systems"},{"key":"7483_CR34","unstructured":": Available from: https:\/\/www.mindspore.cn\/"},{"key":"7483_CR35","doi-asserted-by":"crossref","unstructured":"Butler DJ, Wulff J, Stanley GB, Black MJ (2012) A naturalistic open source movie for optical flow evaluation. In: European conference on computer vision. Springer, p 611\u2013625","DOI":"10.1007\/978-3-642-33783-3_44"},{"key":"7483_CR36","doi-asserted-by":"crossref","unstructured":"Geiger A, Lenz P, Urtasun R (2012) Are we ready for autonomous driving? The kitti vision benchmark suite. In: 2012 IEEE conference on computer vision and pattern recognition. IEEE, p 3354\u20133361","DOI":"10.1109\/CVPR.2012.6248074"},{"key":"7483_CR37","doi-asserted-by":"crossref","unstructured":"Menze M, Geiger A (2015) Object scene flow for autonomous vehicles. In: Proceedings of the IEEE conference on computer vision and pattern recognition, p 3061\u20133070","DOI":"10.1109\/CVPR.2015.7298925"},{"key":"7483_CR38","doi-asserted-by":"crossref","unstructured":"Geiger A, Lenz P, Stiller C, Urtasun R (2013) Vision meets robotics: the kitti dataset. Int J Robot Res, p 1231\u20131237","DOI":"10.1177\/0278364913491297"},{"key":"7483_CR39","doi-asserted-by":"crossref","unstructured":"Janai J, Guney F, Ranjan A, Black M, Geiger A (2018) Unsupervised learning of multi-frame optical flow with occlusions. In: Proceedings of the European conference on computer vision (ECCV), p 690\u2013706","DOI":"10.1007\/978-3-030-01270-0_42"},{"key":"7483_CR40","doi-asserted-by":"crossref","unstructured":"Zhong Y, Ji P, Wang J, Dai Y, Li H (2019) Unsupervised deep epipolar flow for stationary or dynamic scenes. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, p 12095\u201312104","DOI":"10.1109\/CVPR.2019.01237"},{"key":"7483_CR41","doi-asserted-by":"crossref","unstructured":"Liu L, Zhai G, Ye W, Liu Y (2019) Unsupervised learning of scene flow estimation fusing with local rigidity. In: IJCAI, p 876\u2013882","DOI":"10.24963\/ijcai.2019\/123"},{"key":"7483_CR42","doi-asserted-by":"crossref","unstructured":"Ranjan A, Jampani V, Balles L, Kim K, Sun D, Wulff J, et\u00a0al. (2019) Competitive collaboration: joint unsupervised learning of depth, camera motion, optical flow and motion segmentation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, p 12240\u201312249","DOI":"10.1109\/CVPR.2019.01252"},{"key":"7483_CR43","doi-asserted-by":"crossref","unstructured":"Wang Y, Wang P, Yang Z, Luo C, Yang Y, Xu W (2019) Unos: Unified unsupervised optical-flow and stereo-depth estimation by watching videos. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, p 8071\u20138081","DOI":"10.1109\/CVPR.2019.00826"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07483-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-022-07483-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-022-07483-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,8,13]],"date-time":"2022-08-13T21:31:51Z","timestamp":1660426311000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-022-07483-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,6,20]]},"references-count":43,"journal-issue":{"issue":"17","published-print":{"date-parts":[[2022,9]]}},"alternative-id":["7483"],"URL":"https:\/\/doi.org\/10.1007\/s00521-022-07483-z","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,6,20]]},"assertion":[{"value":"10 December 2021","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 May 2022","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 June 2022","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}