FlowNet: Learning Optical Flow With Convolutional Networks

Articles citing FlowNet: Learning Optical Flow With Convolutional Networks

Title / Author	Year
Fully convolutional networks for semantic segmentation J Long, E Shelhamer, T Darrell Proceedings of the IEEE conference on computer vision and pattern …	2015
Deeplab: Semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected crfs LC Chen, G Papandreou, I Kokkinos, K Murphy, AL Yuille IEEE transactions on pattern analysis and machine intelligence 40 (4), 834-848	2017
Fully-convolutional siamese networks for object tracking L Bertinetto, J Valmadre, JF Henriques, A Vedaldi, PHS Torr European conference on computer vision, 850-865	2016
Flownet 2.0: Evolution of optical flow estimation with deep networks E Ilg, N Mayer, T Saikia, M Keuper, A Dosovitskiy, T Brox Proceedings of the IEEE conference on computer vision and pattern …	2017
A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation N Mayer, E Ilg, P Hausser, P Fischer, D Cremers, A Dosovitskiy, T Brox Proceedings of the IEEE Conference on Computer Vision and Pattern …	2016
Geometric deep learning: going beyond euclidean data MM Bronstein, J Bruna, Y LeCun, A Szlam, P Vandergheynst IEEE Signal Processing Magazine 34 (4), 18-42	2017
Learning to track at 100 fps with deep regression networks D Held, S Thrun, S Savarese European Conference on Computer Vision, 749-765	2016
Learning to refine object segments PO Pinheiro, TY Lin, R Collobert, P Dollár European Conference on Computer Vision, 75-91	2016
Playing for data: Ground truth from computer games SR Richter, V Vineet, S Roth, V Koltun European conference on computer vision, 102-118	2016
Synthetic data for text localisation in natural images A Gupta, A Vedaldi, A Zisserman Proceedings of the IEEE Conference on Computer Vision and Pattern …	2016
Octnet: Learning deep 3d representations at high resolutions G Riegler, A Osman Ulusoy, A Geiger Proceedings of the IEEE Conference on Computer Vision and Pattern …	2017
An uncertain future: Forecasting from static images using variational autoencoders J Walker, C Doersch, A Gupta, M Hebert European Conference on Computer Vision, 835-851	2016
Deep multi-scale convolutional neural network for dynamic scene deblurring S Nah, T Hyun Kim, K Mu Lee Proceedings of the IEEE Conference on Computer Vision and Pattern …	2017
Dynamic filter networks X Jia, B De Brabandere, T Tuytelaars, LV Gool Advances in Neural Information Processing Systems, 667-675	2016
Demon: Depth and motion network for learning monocular stereo B Ummenhofer, H Zhou, J Uhrig, N Mayer, E Ilg, A Dosovitskiy, T Brox Proceedings of the IEEE Conference on Computer Vision and Pattern …	2017
Optical flow estimation using a spatial pyramid network A Ranjan, MJ Black Proceedings of the IEEE Conference on Computer Vision and Pattern …	2017
Learning a variational network for reconstruction of accelerated MRI data K Hammernik, T Klatzer, E Kobler, MP Recht, DK Sodickson, T Pock, ... Magnetic resonance in medicine 79 (6), 3055-3071	2018
Learning from synthetic humans G Varol, J Romero, X Martin, N Mahmood, MJ Black, I Laptev, C Schmid Proceedings of the IEEE Conference on Computer Vision and Pattern …	2017
Semi-supervised deep learning for monocular depth map prediction Y Kuznietsov, J Stuckler, B Leibe Proceedings of the IEEE Conference on Computer Vision and Pattern …	2017
Recurrent instance segmentation B Romera-Paredes, PHS Torr European conference on computer vision, 312-329	2016

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