{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,1]],"date-time":"2025-11-01T09:35:40Z","timestamp":1761989740652,"version":"3.37.3"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"3","license":[{"start":{"date-parts":[[2023,8,28]],"date-time":"2023-08-28T00:00:00Z","timestamp":1693180800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,8,28]],"date-time":"2023-08-28T00:00:00Z","timestamp":1693180800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"National Key Research and Development Program of China","award":["No. 2016YFC0803000"],"award-info":[{"award-number":["No. 2016YFC0803000"]}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No. 41371342"],"award-info":[{"award-number":["No. 41371342"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Memetic Comp."],"published-print":{"date-parts":[[2023,9]]},"DOI":"10.1007\/s12293-023-00395-y","type":"journal-article","created":{"date-parts":[[2023,8,28]],"date-time":"2023-08-28T02:01:36Z","timestamp":1693188096000},"page":"341-354","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Learning to estimate optical flow using dual-frequency paradigm"],"prefix":"10.1007","volume":"15","author":[{"given":"Yujin","family":"Zheng","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chu","family":"He","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yan","family":"Huang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shenghua","family":"Fan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Min","family":"Jiang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Dingwen","family":"Wang","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yang","family":"Yi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2023,8,28]]},"reference":[{"key":"395_CR1","doi-asserted-by":"crossref","unstructured":"Dosovitskiy A, Fischer P, Ilg E, Hausser P, Hazirbas C, Golkov V, Van Der Smagt P, Cremers D, Brox T (2015) Flownet: learning optical flow with convolutional networks. In: Proceedings of the IEEE international conference on computer vision, pp 2758\u20132766","DOI":"10.1109\/ICCV.2015.316"},{"issue":"C","key":"395_CR2","doi-asserted-by":"publisher","first-page":"9","DOI":"10.1016\/j.image.2018.12.002","volume":"72","author":"Z Tu","year":"2019","unstructured":"Tu Z, Xie W, Zhang D, Poppe R, Veltkamp RC, Li B, Yuan J (2019) A survey of variational and CNN-based optical flow techniques. Image Commun 72(C):9\u201324. https:\/\/doi.org\/10.1016\/j.image.2018.12.002","journal-title":"Image Commun"},{"key":"395_CR3","doi-asserted-by":"publisher","unstructured":"Menze M, Geiger A (2015) Object scene flow for autonomous vehicles. In: 2015 IEEE conference on computer vision and pattern recognition (CVPR), pp 3061\u20133070. https:\/\/doi.org\/10.1109\/CVPR.2015.7298925","DOI":"10.1109\/CVPR.2015.7298925"},{"key":"395_CR4","unstructured":"Rahaman N, Baratin A, Arpit D, Draxler F, Lin M, Hamprecht F, Bengio Y, Courville A (2019) On the spectral bias of neural networks. In: International conference on machine learning, pp 5301\u20135310. PMLR"},{"issue":"5","key":"395_CR5","doi-asserted-by":"publisher","first-page":"1746","DOI":"10.4208\/cicp.OA-2020-0085","volume":"28","author":"Z-QJ Xu","year":"2020","unstructured":"Xu Z-QJ, Zhang Y, Luo T, Xiao Y, Zheng M (2020) Frequency principle: Fourier analysis sheds light on deep neural networks. Commun. Comput. Phys. 28(5):1746\u20131767. https:\/\/doi.org\/10.4208\/cicp.OA-2020-0085","journal-title":"Commun. Comput. Phys."},{"key":"395_CR6","unstructured":"Basri R, Galun M, Geifman A, Jacobs D, Kasten Y, Kritchman S (2020) Frequency bias in neural networks for input of non-uniform density. In: International conference on machine learning, pp 685\u2013694. PMLR"},{"key":"395_CR7","doi-asserted-by":"crossref","unstructured":"Wang H, Wu X, Huang Z, Xing EP (2020) High-frequency component helps explain the generalization of convolutional neural networks. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 8684\u20138694","DOI":"10.1109\/CVPR42600.2020.00871"},{"issue":"2","key":"395_CR8","doi-asserted-by":"publisher","first-page":"511","DOI":"10.1137\/S0036141095289051","volume":"29","author":"W Sweldens","year":"1998","unstructured":"Sweldens W (1998) The lifting scheme: a construction of second generation wavelets. SIAM J Math Anal 29(2):511\u2013546","journal-title":"SIAM J Math Anal"},{"key":"395_CR9","volume-title":"Wavelets: a tutorial in theory and applications","author":"CK Chui","year":"1992","unstructured":"Chui CK (1992) Wavelets: a tutorial in theory and applications. Academic Press, Cambridge"},{"key":"395_CR10","doi-asserted-by":"publisher","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: 2017 IEEE conference on computer vision and pattern recognition (CVPR), pp 1647\u20131655. https:\/\/doi.org\/10.1109\/CVPR.2017.179","DOI":"10.1109\/CVPR.2017.179"},{"key":"395_CR11","doi-asserted-by":"crossref","unstructured":"Ranjan A, Black MJ (2017) Optical flow estimation using a spatial pyramid network. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 4161\u20134170","DOI":"10.1109\/CVPR.2017.291"},{"key":"395_CR12","doi-asserted-by":"crossref","unstructured":"Hui T-W, 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, pp 8981\u20138989","DOI":"10.1109\/CVPR.2018.00936"},{"key":"395_CR13","doi-asserted-by":"crossref","unstructured":"Sun D, Yang X, Liu M-Y, 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 recognition, pp 8934\u20138943","DOI":"10.1109\/CVPR.2018.00931"},{"key":"395_CR14","unstructured":"Yang G, Ramanan D (2019) Volumetric correspondence networks for optical flow. In: Advances in neural information processing systems 32"},{"key":"395_CR15","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, pp 5754\u20135763","DOI":"10.1109\/CVPR.2019.00590"},{"key":"395_CR16","doi-asserted-by":"crossref","unstructured":"Zheng Y, Zhang M, Lu F (2020) Optical flow in the dark. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 6749\u20136757","DOI":"10.1109\/CVPR42600.2020.00678"},{"key":"395_CR17","doi-asserted-by":"crossref","unstructured":"Yan W, Sharma A, Tan RT (2020) Optical flow in dense foggy scenes using semi-supervised learning. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 13259\u201313268","DOI":"10.1109\/CVPR42600.2020.01327"},{"issue":"3","key":"395_CR18","doi-asserted-by":"publisher","first-page":"231","DOI":"10.1007\/s12293-016-0219-9","volume":"9","author":"Y Zhang","year":"2017","unstructured":"Zhang Y, Jin X, Wang Z (2017) A new modified panoramic UAV image stitching model based on the GA-sift and adaptive threshold method. Memet Comput 9(3):231\u2013244","journal-title":"Memet Comput"},{"issue":"1","key":"395_CR19","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1007\/s12293-021-00350-9","volume":"14","author":"Z WangPing","year":"2022","unstructured":"WangPing Z, Min J, JunFeng Y, KunHong L, QingQiang W (2022) The design of evolutionary feature selection operator for the micro-expression recognition. Memet Comput 14(1):61\u201376","journal-title":"Memet Comput"},{"key":"395_CR20","doi-asserted-by":"crossref","unstructured":"Teed Z, Deng J (2020) Raft: eecurrent all-pairs field transforms for optical flow. In: European conference on computer vision. Springer, Berlin, pp 402\u2013419","DOI":"10.1007\/978-3-030-58536-5_24"},{"key":"395_CR21","doi-asserted-by":"crossref","unstructured":"Jiang S, Campbell D, Lu Y, Li H, Hartley R (2021) Learning to estimate hidden motions with global motion aggregation. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 9772\u20139781","DOI":"10.1109\/ICCV48922.2021.00963"},{"key":"395_CR22","doi-asserted-by":"crossref","unstructured":"Bai S, Geng Z, Savani Y, Kolter JZ (2022) Deep equilibrium optical flow estimation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 620\u2013630","DOI":"10.1109\/CVPR52688.2022.00070"},{"key":"395_CR23","doi-asserted-by":"crossref","unstructured":"Luo A, Yang F, Li X, Liu S (2022) Learning optical flow with kernel patch attention. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 8906\u20138915","DOI":"10.1109\/CVPR52688.2022.00870"},{"key":"395_CR24","doi-asserted-by":"crossref","unstructured":"Zhang F, Woodford OJ, Prisacariu VA, Torr PH (2021) Separable flow: Learning motion cost volumes for optical flow estimation. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 10807\u201310817","DOI":"10.1109\/ICCV48922.2021.01063"},{"key":"395_CR25","doi-asserted-by":"crossref","unstructured":"Zhao S, Zhao L, Zhang Z, Zhou E, Metaxas D (2022) Global matching with overlapping attention for optical flow estimation. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 17592\u201317601","DOI":"10.1109\/CVPR52688.2022.01707"},{"key":"395_CR26","doi-asserted-by":"crossref","unstructured":"Peebles W, Zhu J-Y, Zhang R, Torralba A, Efros A, Shechtman E (2022) Gan-supervised dense visual alignment. In: CVPR","DOI":"10.1109\/CVPR52688.2022.01311"},{"key":"395_CR27","doi-asserted-by":"publisher","unstructured":"Li Y, Barnes C, Huang K, Zhang F-L (2022) Deep $$360^{\\circ }$$ optical flow estimation based on multi-projection fusion. In: Proceedings of the European conference on computer vision (ECCV) 2022, pp 336\u2013352. https:\/\/doi.org\/10.1007\/978-3-031-19833-5_20","DOI":"10.1007\/978-3-031-19833-5_20"},{"key":"395_CR28","doi-asserted-by":"crossref","unstructured":"Huang J, Guan D, Xiao A, Lu S (2021) Rda: Robust domain adaptation via Fourier adversarial attacking. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 8988\u20138999","DOI":"10.1109\/ICCV48922.2021.00886"},{"issue":"6","key":"395_CR29","doi-asserted-by":"publisher","first-page":"3791","DOI":"10.1109\/TGRS.2019.2957251","volume":"58","author":"D Hong","year":"2020","unstructured":"Hong D, Wu X, Ghamisi P, Chanussot J, Yokoya N, Zhu XX (2020) Invariant attribute profiles: a spatial-frequency joint feature extractor for hyperspectral image classification. IEEE Trans Geosci Remote Sens 58(6):3791\u20133808","journal-title":"IEEE Trans Geosci Remote Sens"},{"key":"395_CR30","doi-asserted-by":"crossref","unstructured":"Liu Y, Li Q, Sun Z (2019) Attribute-aware face aging with wavelet-based generative adversarial networks. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp 11877\u201311886","DOI":"10.1109\/CVPR.2019.01215"},{"key":"395_CR31","doi-asserted-by":"publisher","unstructured":"Chen Y, Fan H, Xu B, Yan Z, Kalantidis Y, Rohrbach M, Shuicheng Y, Feng J (2019) Drop an octave: reducing spatial redundancy in convolutional neural networks with octave convolution. In: 2019 IEEE\/CVF international conference on computer vision (ICCV), pp 3434\u20133443. https:\/\/doi.org\/10.1109\/ICCV.2019.00353","DOI":"10.1109\/ICCV.2019.00353"},{"key":"395_CR32","unstructured":"Williams T, Li R (2018) Wavelet pooling for convolutional neural networks. In: International conference on learning representations"},{"key":"395_CR33","doi-asserted-by":"crossref","unstructured":"Ferra A, Aguilar E, Radeva P (2018) Multiple wavelet pooling for CNNs. In: Proceedings of the European conference on computer vision (ECCV) workshops","DOI":"10.1007\/978-3-030-11018-5_55"},{"key":"395_CR34","doi-asserted-by":"crossref","unstructured":"Li Q, Shen L, Guo S, Lai Z (2020) Wavelet integrated CNNs for noise-robust image classification. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition (CVPR)","DOI":"10.1109\/CVPR42600.2020.00727"},{"key":"395_CR35","unstructured":"Gomez AN, Ren M, Urtasun R, Grosse RB (2017) The reversible residual network: Backpropagation without storing activations. In: Advances in neural information processing systems, 30"},{"key":"395_CR36","doi-asserted-by":"publisher","first-page":"200430","DOI":"10.1109\/ACCESS.2020.3035839","volume":"8","author":"Y Zheng","year":"2020","unstructured":"Zheng Y, Shi Z, He C, Zhang Q (2020) Lifting based object detection networks of remote sensing imagery for FPGA accelerator. IEEE Access 8:200430\u2013200439. https:\/\/doi.org\/10.1109\/ACCESS.2020.3035839","journal-title":"IEEE Access"},{"key":"395_CR37","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. In: Proceedings of the 31st international conference on neural information processing systems. NIPS\u201917. Curran Associates Inc., Red Hook, NY, USA, pp 6000\u20136010"},{"issue":"12","key":"395_CR38","doi-asserted-by":"publisher","first-page":"1449","DOI":"10.1109\/TIP.2003.817237","volume":"12","author":"RL Claypoole","year":"2003","unstructured":"Claypoole RL, Davis GM, Sweldens W, Baraniuk RG (2003) Nonlinear wavelet transforms for image coding via lifting. IEEE Trans Image Process 12(12):1449\u20131459","journal-title":"IEEE Trans Image Process"},{"key":"395_CR39","doi-asserted-by":"crossref","unstructured":"Zheng Y, Wang R, Li J (2010) Nonlinear wavelets and BP neural networks adaptive lifting scheme. In: The 2010 international conference on apperceiving computing and intelligence analysis proceeding. IEEE, pp 316\u2013319","DOI":"10.1109\/ICACIA.2010.5709909"},{"key":"395_CR40","doi-asserted-by":"publisher","unstructured":"Mayer N, Ilg E, H\u00e4usser P, Fischer P, Cremers D, Dosovitskiy A, Brox T (2016) A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation. In: 2016 IEEE conference on computer vision and pattern recognition (CVPR), pp 4040\u20134048. https:\/\/doi.org\/10.1109\/CVPR.2016.438","DOI":"10.1109\/CVPR.2016.438"},{"key":"395_CR41","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, Berlin, pp 611\u2013625","DOI":"10.1007\/978-3-642-33783-3_44"},{"key":"395_CR42","doi-asserted-by":"publisher","unstructured":"Kondermann D, Nair R, Honauer K, Krispin K, Andrulis J, Brock A, G\u00fcssefeld B, Rahimimoghaddam M, Hofmann S, Brenner C, J\u00e4hne B (2016) The HCI benchmark suite: Stereo and flow ground truth with uncertainties for urban autonomous driving. In: 2016 IEEE conference on computer vision and pattern recognition workshops (CVPRW), pp 19\u201328. https:\/\/doi.org\/10.1109\/CVPRW.2016.10","DOI":"10.1109\/CVPRW.2016.10"}],"container-title":["Memetic Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12293-023-00395-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12293-023-00395-y\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12293-023-00395-y.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,7]],"date-time":"2023-09-07T08:39:51Z","timestamp":1694075991000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12293-023-00395-y"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,28]]},"references-count":42,"journal-issue":{"issue":"3","published-print":{"date-parts":[[2023,9]]}},"alternative-id":["395"],"URL":"https:\/\/doi.org\/10.1007\/s12293-023-00395-y","relation":{},"ISSN":["1865-9284","1865-9292"],"issn-type":[{"type":"print","value":"1865-9284"},{"type":"electronic","value":"1865-9292"}],"subject":[],"published":{"date-parts":[[2023,8,28]]},"assertion":[{"value":"14 July 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 July 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"28 August 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}