17.3.1 Opeical Flow, Learning, Neural Networks, GAN

Chapter Contents (Back)
Optical Flow. Learning. Neural Networks. GAN.

Chen, T.W.[Tsu Wang], and Lin, W.C.[Wei Chung], Chen, C.T.,
Artificial Neural Networks for 3-D Motion Analysis I: Rigid Motion,
TNN(6), No. 6, November 1995, pp. 1386-1393. BibRef 9511
And:
Artificial Neural Networks for 3-D Motion Analysis II: Nonrigid Motion,
TNN(6), No. 6, November 1995, pp. 1394-1401.
See also Neural-Network Approach to CSG-Based 3-D Object Recognition, A. BibRef

Wang, R.Y.,
A Network Model for the Optic Flow Computation of the MST Neurons,
NeurNet(9), No. 3, April 1996, pp. 411-426. 9605
BibRef

Gray, W.S., Nabet, B.,
Volterra Series Analysis and Synthesis of a Neural Network for Velocity Estimation,
SMC-B(29), No. 2, April 1999, pp. 190. BibRef 9904

Mayer, N.[Nikolaus], Ilg, E.[Eddy], Fischer, P.[Philipp], Hazirbas, C.[Caner], Cremers, D.[Daniel], Dosovitskiy, A.[Alexey], Brox, T.[Thomas],
What Makes Good Synthetic Training Data for Learning Disparity and Optical Flow Estimation?,
IJCV(126), No. 9, September 2018, pp. 942-960.
Springer DOI 1809
BibRef

Dosovitskiy, A., Fischery, P., Ilg, E., Hausser, P., Hazirbas, C., Golkov, V., van de Smagt, P., Cremers, D., Brox, T.,
FlowNet: Learning Optical Flow with Convolutional Networks,
ICCV15(2758-2766)
IEEE DOI 1602
Computer architecture BibRef

Ilg, E.[Eddy], Saikia, T.[Tonmoy], Keuper, M.[Margret], Brox, T.[Thomas],
Occlusions, Motion and Depth Boundaries with a Generic Network for Disparity, Optical Flow or Scene Flow Estimation,
ECCV18(XII: 626-643).
Springer DOI 1810
BibRef

Schrodi, S.[Simon], Saikia, T.[Tonmoy], Brox, T.[Thomas],
Towards Understanding Adversarial Robustness of Optical Flow Networks,
CVPR22(8906-8914)
IEEE DOI 2210
Codes, Computational modeling, Estimation, Network architecture, Apertures, Robustness, Motion and tracking, Adversarial attack and defense BibRef

Ilg, E.[Eddy], Mayer, N.[Nikolaus], Saikia, T.[Tonmoy], Keuper, M.[Margret], Dosovitskiy, A.[Alexey], Brox, T.[Thomas],
FlowNet 2.0: Evolution of Optical Flow Estimation with Deep Networks,
CVPR17(1647-1655)
IEEE DOI 1711
Adaptive optics, Computer architecture, Estimation, Optical imaging, Schedules, Stacking, Training BibRef

Wannenwetsch, A.S., Keuper, M., Roth, S.,
ProbFlow: Joint Optical Flow and Uncertainty Estimation,
ICCV17(1182-1191)
IEEE DOI 1802
Bayes methods, entropy, image sequences, motion estimation, energy minimization approach, energy-based formulations, Uncertainty BibRef

Makansi, O.[Osama], Ilg, E.[Eddy], Cicek, O.[Ozgun], Brox, T.[Thomas],
Overcoming Limitations of Mixture Density Networks: A Sampling and Fitting Framework for Multimodal Future Prediction,
CVPR19(7137-7146).
IEEE DOI 2002
BibRef

Ilg, E.[Eddy], Çiçek, Ö.[Özgün], Galesso, S.[Silvio], Klein, A.[Aaron], Makansi, O.[Osama], Hutter, F.[Frank], Brox, T.[Thomas],
Uncertainty Estimates and Multi-hypotheses Networks for Optical Flow,
ECCV18(VII: 677-693).
Springer DOI 1810
BibRef

Paredes-Vallés, F.[Federico], Scheper, K.Y.W.[Kirk Y. W.], de Croon, G.C.H.E.[Guido C.H.E.],
Unsupervised Learning of a Hierarchical Spiking Neural Network for Optical Flow Estimation: From Events to Global Motion Perception,
PAMI(42), No. 8, August 2020, pp. 2051-2064.
IEEE DOI 2007
Neurons, Visualization, Biomedical optical imaging, Optical sensors, Biological system modeling, unsupervised learning BibRef

Hui, T.W.[Tak-Wai], Tang, X.[Xiaoou], Loy, C.C.[Chen Change],
A Lightweight Optical Flow CNN: Revisiting Data Fidelity and Regularization,
PAMI(43), No. 8, August 2021, pp. 2555-2569.
IEEE DOI 2107
BibRef
Earlier:
LiteFlowNet: A Lightweight Convolutional Neural Network for Optical Flow Estimation,
CVPR18(8981-8989)
IEEE DOI 1812
Optical imaging, Adaptive optics, Estimation, Optical computing, Convolutional codes, Optical network units, and warping. Estimation, Feature extraction, Convolution, Optical filters, Optical fiber networks BibRef

Hui, T.W.[Tak-Wai], Loy, C.C.[Chen Change],
LiteFlownet3: Resolving Correspondence Ambiguity for More Accurate Optical Flow Estimation,
ECCV20(XX:169-184).
Springer DOI 2011
BibRef

Che, T.T.[Tong-Tong], Zheng, Y.J.[Yuan-Jie], Yang, Y.S.[Yun-Shuai], Hou, S.J.[Su-Juan], Jia, W.K.[Wei-Kuan], Yang, J.[Jie], Gong, C.[Chen],
SDOF-GAN: Symmetric Dense Optical Flow Estimation With Generative Adversarial Networks,
IP(30), 2021, pp. 6036-6049.
IEEE DOI 2107
Estimation, Optical imaging, Adaptive optics, Generative adversarial networks, Brightness, semi-supervised learning BibRef

Zheng, Y.J.[Yuan-Jie], Sui, X.D.[Xiao-Dan], Jiang, Y.[Yanyun], Che, T.T.[Tong-Tong], Zhang, S.T.[Shao-Ting], Yang, J.[Jie], Li, H.S.[Hong-Sheng],
SymReg-GAN: Symmetric Image Registration With Generative Adversarial Networks,
PAMI(44), No. 9, September 2022, pp. 5631-5646.
IEEE DOI 2208
Image registration, Generative adversarial networks, Generators, Estimation, Training, Magnetic resonance imaging, Image resolution, multimodal image registration BibRef

Liu, S.C.[Shuai-Cheng], Luo, K.M.[Kun-Ming], Ye, N.J.[Nian-Jin], Wang, C.[Chuan], Wang, J.[Jue], Zeng, B.[Bing],
OIFlow: Occlusion-Inpainting Optical Flow Estimation by Unsupervised Learning,
IP(30), 2021, pp. 6420-6433.
IEEE DOI 2107
Optical imaging, Feature extraction, Optical variables control, Estimation, Optical fiber networks, Adaptive optics, unsupervised learning BibRef

Liu, S.C.[Shuai-Cheng], Luo, K.M.[Kun-Ming], Luo, A.[Ao], Wang, C.[Chuan], Meng, F.M.[Fan-Man], Zeng, B.[Bing],
ASFlow: Unsupervised Optical Flow Learning With Adaptive Pyramid Sampling,
CirSysVideo(32), No. 7, July 2022, pp. 4282-4295.
IEEE DOI 2207
Optical imaging, Optical variables control, Optical losses, Feature extraction, Interpolation, Adaptive systems, pyramid downsampling BibRef

Luo, K.M.[Kun-Ming], Wang, C.[Chuan], Liu, S.C.[Shuai-Cheng], Fan, H.Q.[Hao-Qiang], Wang, J.[Jue], Sun, J.[Jian],
UPFlow: Upsampling Pyramid for Unsupervised Optical Flow Learning,
CVPR21(1045-1054)
IEEE DOI 2111
Optical losses, Interpolation, Estimation, Benchmark testing, Pattern recognition, Optical flow BibRef

Zhao, R.[Rui], Xiong, R.Q.[Rui-Qin], Ding, Z.[Ziluo], Fan, X.P.[Xiao-Peng], Zhang, J.[Jian], Huang, T.J.[Tie-Jun],
MRDFlow: Unsupervised Optical Flow Estimation Network With Multi-Scale Recurrent Decoder,
CirSysVideo(32), No. 7, July 2022, pp. 4639-4652.
IEEE DOI 2207
Optical flow, Decoding, Estimation, Optical losses, Image motion analysis, Correlation, recurrent decoder BibRef

Liu, P.P.[Peng-Peng], Lyu, M.R.[Michael R.], King, I.[Irwin], Xu, J.[Jia],
Learning by Distillation: A Self-Supervised Learning Framework for Optical Flow Estimation,
PAMI(44), No. 9, September 2022, pp. 5026-5041.
IEEE DOI 2208
Optical imaging, Predictive models, Optical variables control, Estimation, Optical computing, Training, Data models, Optical flow, and stereo matching BibRef

de Jong, D.B.[David B.], Paredes-Vallés, F.[Federico], de Croon, G.C.H.E.[Guido C. H. E.],
How Do Neural Networks Estimate Optical Flow? A Neuropsychology-Inspired Study,
PAMI(44), No. 11, November 2022, pp. 8290-8305.
IEEE DOI 2210
Optical imaging, Optical fiber networks, Optical sensors, Optical computing, Estimation, Biomedical optical imaging, neuropsychology BibRef

Lin, X.H.[Xiu-Hong], Yang, C.H.[Chen-Hui], Bian, X.S.[Xue-Sheng], Liu, W.Q.[Wei-Quan], Wang, C.[Cheng],
EAGAN: Event-based attention generative adversarial networks for optical flow and depth estimation,
IET-CV(16), No. 7, 2022, pp. 581-595.
DOI Link 2210
BibRef

Chi, C.[Cheng], Hao, T.Y.[Tian-Yu], Wang, Q.J.[Qing-Jie], Guo, P.[Peng], Yang, X.[Xin],
Subspace-PnP: A Geometric Constraint Loss for Mutual Assistance of Depth and Optical Flow Estimation,
IJCV(130), No. 12, December 2022, pp. 3054-3069.
Springer DOI 2211
BibRef

Chi, C.[Cheng], Wang, Q.J.[Qing-Jie], Hao, T.Y.[Tian-Yu], Guo, P.[Peng], Yang, X.[Xin],
Feature-Level Collaboration: Joint Unsupervised Learning of Optical Flow, Stereo Depth and Camera Motion,
CVPR21(2463-2473)
IEEE DOI 2111
Optical losses, Motion estimation, Collaboration, Cameras, Feature extraction, Task analysis BibRef

Savian, S.[Stefano], Elahi, M.[Mehdi], Janes, A.A.[Andrea A.], Tillo, T.[Tammam],
Benchmarking equivariance for Deep Learning based optical flow estimators,
SP:IC(111), 2023, pp. 116892.
Elsevier DOI 2301
Optical flow, Deep Learning, Equivariance, Benchmark BibRef

Kong, L.T.[Ling-Tong], Yang, J.[Jie],
MDFlow: Unsupervised Optical Flow Learning by Reliable Mutual Knowledge Distillation,
CirSysVideo(33), No. 2, February 2023, pp. 677-688.
IEEE DOI 2302
Reliability, Knowledge engineering, Costs, Estimation, Biomedical optical imaging, Task analysis, Optical flow, real time BibRef

Xiang, X.Z.[Xue-Zhi], Abdein, R.[Rokia], Lv, N.[Ning], Yang, J.[Jie],
Self-Supervised Learning of Scene Flow with Occlusion Handling Through Feature Masking,
PR(139), 2023, pp. 109487.
Elsevier DOI 2304
BibRef
Earlier: A2, A1, A3, Only:
Self-Supervised Learning of Optical Flow, Depth, Camera Pose and Rigidity Segmentation with Occlusion Handling,
ICIP22(6-10)
IEEE DOI 2211
Optical flow, Depth estimation, Camera pose, Rigidity segmentation, Occlusion handling, Deformable decoder. Training, Integrated optics, Image motion analysis, Correlation, Self-supervised learning, Cameras, Optical flow estimation BibRef


Marsal, R.[Rémi], Chabot, F.[Florian], Loesch, A.[Angélique], Sahbi, H.[Hichem],
BrightFlow: Brightness-Change-Aware Unsupervised Learning of Optical Flow,
WACV23(2060-2069)
IEEE DOI 2302
Training, Optical losses, Source coding, Brightness, Estimation, Radiometry, Reflection BibRef

Min, C.[Chaerin], Kim, T.[Taehyun], Lim, J.W.[Jong-Woo],
Meta-Learning for Adaptation of Deep Optical Flow Networks,
WACV23(2144-2153)
IEEE DOI 2302
Training, Optical losses, Neural networks, Estimation, Training data, Reliability, Labeling, and algorithms (including transfer) BibRef

Schelling, M.[Michael], Hermosilla, P.[Pedro], Ropinski, T.[Timo],
Weakly-Supervised Optical Flow Estimation for Time-of-Flight,
WACV23(2134-2143)
IEEE DOI 2302
Training, Optical losses, Optical variables measurement, Motion compensation, Motion measurement, 3D computer vision BibRef

Savian, S.[Stefano], Morerio, P.[Pietro], del Bue, A.[Alessio], Janes, A.A.[Andrea A.], Tillo, T.[Tammam],
Towards Equivariant Optical Flow Estimation with Deep Learning,
WACV23(5077-5086)
IEEE DOI 2302
Training, Measurement, Deep learning, Optical losses, Codes, Computational modeling BibRef

Chen, Y.H.[Yong-Hu], Zhu, D.C.[Dong-Chen], Shi, W.J.[Wen-Jun], Zhang, G.H.[Guang-Hui], Zhang, T.Y.[Tian-Yu], Zhang, X.L.[Xiao-Lin], Li, J.[Jiamao],
MFCFlow: A Motion Feature Compensated Multi-Frame Recurrent Network for Optical Flow Estimation,
WACV23(5057-5066)
IEEE DOI 2302
Optical filters, Geometry, Matched filters, Correlation, Computational modeling, Estimation, Coherence, Low-level and physics-based vision BibRef

Chong, X.Y.[Xiao-Ya], Zhou, N.[Niyun], Li, Q.[Qing], Leung, H.[Howard],
NoiseFlow: Learning Optical Flow from Low SNR Cryo-EM Movie,
ICPR22(3471-3477)
IEEE DOI 2212
Deep learning, Correlation, Computational modeling, Stacking, Noise reduction, Estimation, Motion pictures BibRef

Bhandari, K.[Keshav], Duan, B.[Bin], Liu, G.[Gaowen], Latapie, H.[Hugo], Zong, Z.L.[Zi-Liang], Yan, Y.[Yan],
Learning Omnidirectional Flow in 360° Video via Siamese Representation,
ECCV22(VIII:557-574).
Springer DOI 2211
BibRef

Chang, C.P.[Chih-Peng], Chen, P.Y.[Peng-Yu], Ho, Y.H.[Yung-Han], Peng, W.H.[Wen-Hsiao],
Deep Incremental Optical Flow Coding For Learned Video Compression,
ICIP22(3988-3992)
IEEE DOI 2211
Image coding, Bit rate, Video compression, Motion compensation, Encoding, Video codecs, Optical flow, Video Coding, Double Warping BibRef

Im, W.B.[Woo-Bin], Lee, S.[Sebin], Yoon, S.E.[Sung-Eui],
Semi-supervised Learning of Optical Flow by Flow Supervisor,
ECCV22(XXXV:302-318).
Springer DOI 2211
BibRef

Li, Y.H.[Yi-Heng], Barnes, C.[Connelly], Huang, K.[Kun], Zhang, F.L.[Fang-Lue],
Deep 360° Optical Flow Estimation Based on Multi-projection Fusion,
ECCV22(XXXV:336-352).
Springer DOI 2211
BibRef

Yuan, S.[Shuai], Sun, X.[Xian], Kim, H.[Hannah], Yu, S.Z.[Shu-Zhi], Tomasi, C.[Carlo],
Optical Flow Training Under Limited Label Budget via Active Learning,
ECCV22(XXII:410-427).
Springer DOI 2211
BibRef

Huang, Z.Y.[Zhao-Yang], Shi, X.Y.[Xiao-Yu], Zhang, C.[Chao], Wang, Q.[Qiang], Cheung, K.C.[Ka Chun], Qin, H.W.[Hong-Wei], Dai, J.F.[Ji-Feng], Li, H.S.[Hong-Sheng],
FlowFormer: A Transformer Architecture for Optical Flow,
ECCV22(XVII:668-685).
Springer DOI 2211
BibRef

Le Guen, V.[Vincent], Rambour, C.[Clément], Thome, N.[Nicolas],
Complementing Brightness Constancy with Deep Networks for Optical Flow Prediction,
ECCV22(XXI:121-138).
Springer DOI 2211
BibRef

Sun, D.Q.[De-Qing], Herrmann, C.[Charles], Reda, F.[Fitsum], Rubinstein, M.[Michael], Fleet, D.J.[David J.], Freeman, W.T.[William T.],
Disentangling Architecture and Training for Optical Flow,
ECCV22(XXII:165-182).
Springer DOI 2211
BibRef

Xu, H.F.[Hao-Fei], Zhang, J.[Jing], Cai, J.F.[Jian-Fei], Rezatofighi, H.[Hamid], Tao, D.C.[Da-Cheng],
GMFlow: Learning Optical Flow via Global Matching,
CVPR22(8111-8120)
IEEE DOI 2210
Image motion analysis, Correlation, Costs, Pipelines, Estimation, Transformers, Motion and tracking, Low-level vision BibRef

Luo, A.[Ao], Yang, F.[Fan], Li, X.[Xin], Liu, S.C.[Shuai-Cheng],
Learning Optical Flow with Kernel Patch Attention,
CVPR22(8896-8905)
IEEE DOI 2210
Image motion analysis, Motion estimation, Benchmark testing, Pattern recognition, Kernel, Task analysis, Motion and tracking, Video analysis and understanding BibRef

Bai, S.J.[Shao-Jie], Geng, Z.Y.[Zheng-Yang], Savani, Y.[Yash], Kolter, J.Z.[J. Zico],
Deep Equilibrium Optical Flow Estimation,
CVPR22(610-620)
IEEE DOI 2210
Training, Degradation, Image motion analysis, Computational modeling, Memory management, Estimation, Machine learning BibRef

Hu, L.W.[Li-Wen], Zhao, R.[Rui], Ding, Z.[Ziluo], Ma, L.[Lei], Shi, B.X.[Bo-Xin], Xiong, R.Q.[Rui-Qin], Huang, T.J.[Tie-Jun],
Optical Flow Estimation for Spiking Camera,
CVPR22(17823-17832)
IEEE DOI 2210
Deep learning, Training, Photography, Tracking, Pipelines, Estimation, Cameras, Computational photography, Motion and tracking BibRef

Ammar, A.[Anis], Chebbah, A.[Amani], Fredj, H.B.[Hana Ben], Souani, C.[Chokri],
Comparative Study of latest CNN based Optical Flow Estimation,
ISCV22(1-6)
IEEE DOI 2208
Deep learning, Systematics, Image processing, Motion estimation, Estimation, Signal processing algorithms, deep learning BibRef

Zhang, F.H.[Fei-Hu], Woodford, O.J.[Oliver J.], Prisacariu, V.[Victor], Torr, P.H.S.[Philip H. S.],
Separable Flow: Learning Motion Cost Volumes for Optical Flow Estimation,
ICCV21(10787-10797)
IEEE DOI 2203
Knowledge engineering, Image motion analysis, Costs, Correlation, Estimation, Benchmark testing, Motion and tracking, BibRef

Li, H.P.[Hai-Peng], Luo, K.M.[Kun-Ming], Liu, S.C.[Shuai-Cheng],
GyroFlow: Gyroscope-Guided Unsupervised Optical Flow Learning,
ICCV21(12849-12858)
IEEE DOI 2203
Optical fibers, Measurement, Rain, Codes, Fuses, Brightness, Motion and tracking, Datasets and evaluation, Vision + other modalities BibRef

Jeny, A.A.[Afsana Ahsan], Islam, M.B.[Md Baharul], Aydin, T.[Tarkan],
DeepPyNet: A Deep Feature Pyramid Network for Optical Flow Estimation,
IVCNZ21(1-6)
IEEE DOI 2201
Visualization, Image motion analysis, Correlation, Refining, Neural networks, Estimation, Feature extraction, Iterative recurrent unit BibRef

Sun, D.[Deqing], Vlasic, D.[Daniel], Herrmann, C.[Charles], Jampani, V.[Varun], Krainin, M.[Michael], Chang, H.[Huiwen], Zabih, R.[Ramin], Freeman, W.T.[William T.], Liu, C.[Ce],
AutoFlow: Learning a Better Training Set for Optical Flow,
CVPR21(10088-10097)
IEEE DOI 2111
Training, Adaptation models, Technological innovation, Solid modeling, Shape, Training data BibRef

Poggi, M.[Matteo], Aleotti, F.[Filippo], Mattoccia, S.[Stefano],
Sensor-Guided Optical Flow,
ICCV21(7888-7898)
IEEE DOI 2203
Geometry, Image motion analysis, Optical variables measurement, Prediction algorithms, Sensors, Reliability, Vision for robotics and autonomous vehicles BibRef

Aleotti, F.[Filippo], Poggi, M.[Matteo], Mattoccia, S.[Stefano],
Learning optical flow from still images,
CVPR21(15196-15206)
IEEE DOI 2111
Training, Computational modeling, Training data, Estimation, Data models, Pattern recognition BibRef

Jiao, Y.[Yang], Shi, G.M.[Guang-Ming], Tran, T.D.[Trac D.],
Optical Flow Estimation Via Motion Feature Recovery,
ICIP21(2558-2562)
IEEE DOI 2201
Solid modeling, Adaptive learning, Adaptation models, Correlation, Estimation, Benchmark testing, CNN, Optical Flow, Cost Volume, Motion Consistency BibRef

Jiao, Y.[Yang], Tran, T.D.[Trac D.], Shi, G.M.[Guang-Ming],
EffiScene: Efficient Per-Pixel Rigidity Inference for Unsupervised Joint Learning of Optical Flow, Depth, Camera Pose and Motion Segmentation,
CVPR21(5534-5543)
IEEE DOI 2111
Couplings, Image motion analysis, Motion segmentation, Estimation, Benchmark testing, Cameras BibRef

Jiang, S.H.[Shi-Hao], Lu, Y.[Yao], Li, H.D.[Hong-Dong], Hartley, R.I.[Richard I.],
Learning Optical Flow from a Few Matches,
CVPR21(16587-16595)
IEEE DOI 2111
Training, Solid modeling, Correlation, Limiting, Computational modeling, Memory management, Estimation BibRef

Artizzu, C.O.[Charles-Olivier], Zhang, H.Z.[Hao-Zhou], Allibert, G.[Guillaume], Demonceaux, C.[Cédric],
OmniFlowNet: a Perspective Neural Network Adaptation for Optical Flow Estimation in Omnidirectional Images,
ICPR21(2657-2662)
IEEE DOI 2105
Training, Convolution, Image processing, Neural networks, Estimation, Optical distortion, Distortion BibRef

Yu, S.J.[Suihan-Jin], Zhang, Y.M.[You-Min], Wang, C.[Chen], Bai, X.[Xiao], Zhang, L.[Liang], Hancock, E.R.[Edwin R.],
HMFlow: Hybrid Matching Optical Flow Network for Small and Fast-Moving Objects,
ICPR21(1197-1204)
IEEE DOI 2105
Image resolution, Estimation, Search problems, Pattern recognition, Task analysis, Optical flow BibRef

Li, Z.Y.[Zhuo-Yan], Shen, J.W.[Jia-Wei], Liu, R.[Ruitao],
A Lightweight Network to Learn Optical Flow from Event Data,
ICPR21(1-7)
IEEE DOI 2105
Image motion analysis, Laplace equations, Data integrity, Neural networks, Estimation, Computer architecture, CNN BibRef

Lee, C.[Chankyu], Kosta, A.K.[Adarsh Kumar], Zhu, A.Z.[Alex Zihao], Chaney, K.[Kenneth], Daniilidis, K.[Kostas], Roy, K.[Kaushik],
Spike-flownet: Event-based Optical Flow Estimation with Energy-efficient Hybrid Neural Networks,
ECCV20(XXIX: 366-382).
Springer DOI 2010
BibRef

Xie, S., Lai, P.K., Laganiere, R., Lang, J.,
Effective Convolutional Neural Network Layers in Flow Estimation for Omni-Directional Images,
3DV19(671-680)
IEEE DOI 1911
Estimation, Optical imaging, Convolution, Optical computing, Adaptive optics, Optical fiber networks, Neural networks, neural network BibRef

Lu, Y.[Yao], Valmadre, J.[Jack], Wang, H.[Heng], Kannala, J.H.[Ju-Ho], Harandi, M.[Mehrtash], Torr, P.H.S.[Philip H. S.],
Devon: Deformable Volume Network for Learning Optical Flow,
WACV20(2694-2702)
IEEE DOI 2006
BibRef
Earlier: OpticalFlow18(VI:673-677).
Springer DOI 1905
Optical imaging, Optical computing, Optical distortion, Neural networks, Optical fiber networks, Optical propagation, Estimation BibRef

Yang, Y.C.[Yan-Chao], Soatto, S.[Stefano],
Conditional Prior Networks for Optical Flow,
ECCV18(XV: 282-298).
Springer DOI 1810
BibRef

Chapter on Optical Flow Field Computations and Use continues in
Scene Flow, Depth Image Flow, RGB-D .


Last update:Aug 31, 2023 at 09:37:21