The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Learning Optical Flow
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Citations
Cites
Training Products of Experts by Minimizing Contrastive Divergence
Hinton, G. E. (2002) · Neural Computation · doi:10.1162/089976602760128018 · Cited by 4,879 · cited 1×
“Because ML estimation in loopy graphs is generally intractable, we approximate the learning objective and use the contrastive divergence (CD) algorithm [24] to learn the parameters.”
FusionFlow: Discrete-continuous optimization for optical flow estimation
Lempitsky, V., Roth, S., & Rother, C. (2008) · 2008 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2008.4587751 · Cited by 145
Self-citationOptical flow estimation with uncertainties through dynamic MRFs
Glocker, B., Paragios, N., Komodakis, N., Tziritas, G., & Navab, N. (2008) · 2008 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2008.4587562 · Cited by 69
Research works printed but not linked: 25
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Cited by
Flownet: Learning optical flow with convolutional networks
Dosovitskiy, A., Fischer, P., Ilg, E., Hausser, P., Hazirbas, C., Golkov, V., Smagt, P. V. D., Cremers, D., & Brox, T. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.316 · Cited by 4,313
“[32] study statistics of optical flow and learn regularizers using Gaussian scale mixtures; Rosenbaum et al.”
MethodPWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume
Sun, D., Yang, X., Liu, M.-Y., & Kautz, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00931 · Cited by 2,648
Self-citationA Database and Evaluation Methodology for Optical Flow
Baker, S., Scharstein, D., Lewis, J. P., Roth, S., Black, M. J., & Szeliski, R. (2011) · International Journal of Computer Vision · doi:10.1007/s11263-010-0390-2 · Cited by 2,205
“The training set includes the ground truth and is meant to be used for debugging, parameter estimation, and possibly even learning [41, 70].”
BackgroundMethodInfluentialSelf-citationA Naturalistic Open Source Movie for Optical Flow Evaluation
Butler, D. J., Wulff, J., Stanley, G. B., & Black, M. J. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33783-3_44 · Cited by 1,842
Self-citationLarge Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation
Brox, T., & Malik, J. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.143 · Cited by 1,295
Metadata
- Authors
- Sun, D., Roth, S., Lewis, J. P., & Black, M. J.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2008
- Pages
- 83-97
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W2132529238
- Cited by
- 230
- References
- 3
- FWCI
- 12.02
- Type
- conference paper
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
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- Sun, D., Roth, S., Lewis, J. P., & Black, M. J. (2008). Learning Optical Flow. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-540-88690-7_7
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Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
