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Conference Paper: Conditional prior networks for optical flow

TitleConditional prior networks for optical flow
Authors
Issue Date2018
PublisherSpringer
Citation
15th European Conference on Computer Vision (ECCV 2018), Munich, Germany, 8-14 September 2018. In Ferrari, V, Hebert, M, Sminchisescu, C, et al. (Eds.), Computer Vision – ECCV 2018: 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part XV, p. 282-298. Cham, Switzerland: Springer, 2018 How to Cite?
AbstractClassical computation of optical flow involves generic priors (regularizers) that capture rudimentary statistics of images, but not long-range correlations or semantics. On the other hand, fully supervised methods learn the regularity in the annotated data, without explicit regularization and with the risk of overfitting. We seek to learn richer priors on the set of possible flows that are statistically compatible with an image. Once the prior is learned in a supervised fashion, one can easily learn the full map to infer optical flow directly from two or more images, without any need for (additional) supervision. We introduce a novel architecture, called Conditional Prior Network (CPN), and show how to train it to yield a conditional prior. When used in conjunction with a simple optical flow architecture, the CPN beats all variational methods and all unsupervised learning-based ones using the same data term. It performs comparably to fully supervised ones, that however are fine-tuned to a particular dataset. Our method, on the other hand, performs well even when transferred between datasets. Code is available at: https://github.com/YanchaoYang/Conditional-Prior-Networks.
Persistent Identifierhttp://hdl.handle.net/10722/325417
ISBN
ISSN
2023 SCImago Journal Rankings: 0.606
ISI Accession Number ID
Series/Report no.Lecture Notes in Computer Science ; 11219
LNCS Sublibrary. SL 6, Image Processing, Computer Vision, Pattern Recognition, and Graphics

 

DC FieldValueLanguage
dc.contributor.authorYang, Yanchao-
dc.contributor.authorSoatto, Stefano-
dc.date.accessioned2023-02-27T07:33:06Z-
dc.date.available2023-02-27T07:33:06Z-
dc.date.issued2018-
dc.identifier.citation15th European Conference on Computer Vision (ECCV 2018), Munich, Germany, 8-14 September 2018. In Ferrari, V, Hebert, M, Sminchisescu, C, et al. (Eds.), Computer Vision – ECCV 2018: 15th European Conference, Munich, Germany, September 8-14, 2018, Proceedings, Part XV, p. 282-298. Cham, Switzerland: Springer, 2018-
dc.identifier.isbn9783030012663-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10722/325417-
dc.description.abstractClassical computation of optical flow involves generic priors (regularizers) that capture rudimentary statistics of images, but not long-range correlations or semantics. On the other hand, fully supervised methods learn the regularity in the annotated data, without explicit regularization and with the risk of overfitting. We seek to learn richer priors on the set of possible flows that are statistically compatible with an image. Once the prior is learned in a supervised fashion, one can easily learn the full map to infer optical flow directly from two or more images, without any need for (additional) supervision. We introduce a novel architecture, called Conditional Prior Network (CPN), and show how to train it to yield a conditional prior. When used in conjunction with a simple optical flow architecture, the CPN beats all variational methods and all unsupervised learning-based ones using the same data term. It performs comparably to fully supervised ones, that however are fine-tuned to a particular dataset. Our method, on the other hand, performs well even when transferred between datasets. Code is available at: https://github.com/YanchaoYang/Conditional-Prior-Networks.-
dc.languageeng-
dc.publisherSpringer-
dc.relation.ispartofLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)-
dc.relation.ispartofseriesLecture Notes in Computer Science ; 11219-
dc.relation.ispartofseriesLNCS Sublibrary. SL 6, Image Processing, Computer Vision, Pattern Recognition, and Graphics-
dc.titleConditional prior networks for optical flow-
dc.typeConference_Paper-
dc.description.naturelink_to_subscribed_fulltext-
dc.identifier.doi10.1007/978-3-030-01267-0_17-
dc.identifier.scopuseid_2-s2.0-85055444471-
dc.identifier.spage282-
dc.identifier.epage298-
dc.identifier.eissn1611-3349-
dc.identifier.isiWOS:000612999000017-
dc.publisher.placeCham, Switzerland-

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