Fetching the paper…
Reading the bibliography…
The ability to anticipate the future is essential when making real time critical decisions, provides valuable information to understand dynamic natural scenes, and can help unsupervised video representation learning.
Horn, B.K., Schunck, B.G.: Determining optical flow. Artificial intelligence 17
1981
Earlier work this paper cites.
Mundy, J.L., Zisserman, A.: Geometric invariance in computer vision, vol. 92. MIT press Cambridge, MA (1992)
1992
Earlier work this paper cites.
Wang, Z., Bovik, A.C., Sheikh, H.R., Simoncelli, E.P.: Image quality assessment: from error visibility to structural similarity. IEEE transactions on image processing 13
2004
Earlier work this paper cites.
Hesterberg, T., Choi, N.H., Meier, L., Fraley, C.: Least angle and l1 penalized regression: A review. Statistics Surveys 2
2008
Earlier work this paper cites.
Beck, A., Teboulle, M.: A fast iterative shrinkage-thresholding algorithm for linear inverse problems. SIAM journal on imaging sciences 2
2009
Earlier work this paper cites.
Dollár, P., Wojek, C., Schiele, B., Perona, P.: Pedestrian detection: A benchmark. In: Computer Vision and Pattern Recognition, 2009. CVPR 2009. IEEE Conference on. pp. 304–311. IEEE (2009)
2009
Earlier work this paper cites.
Gregor, K., LeCun, Y.: Learning fast approximations of sparse coding. In: Proceedings of the 27th International Conference on Machine Learning (ICML-10). pp. 399–406 (2010)
2010
Earlier work this paper cites.
Ayazoglu, M., Li, B., Dicle, C., Sznaier, M., Camps, O.I.: Dynamic subspace-based coordinated multicamera tracking. In: Computer Vision (ICCV), 2011 IEEE International Conference on. pp. 2462–2469. IEEE (2011)
2011
Earlier work this paper cites.
Li, B., Camps, O.I., Sznaier, M.: Cross-view activity recognition using hankelets. In: Computer Vision and Pattern Recognition (CVPR), 2012 IEEE Conference on. pp. 1362–1369. IEEE (2012)
2012
Earlier work this paper cites.
2012
Earlier work this paper cites.
Geiger, A., Lenz, P., Stiller, C., Urtasun, R.: Vision meets robotics: The kitti dataset. The International Journal of Robotics Research 32
2013
Earlier work this paper cites.
Wadhwa, N., Rubinstein, M., Durand, F., Freeman, W.T.: Phase-based video motion processing. ACM Transactions on Graphics (TOG) 32
2013
Earlier work this paper cites.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Advances in neural information processing systems. pp. 2672–2680 (2014)
2014
Earlier work this paper cites.
2014
Cited alongside, same era.
Dosovitskiy, A., Fischer, P., Ilg, E., Hausser, P., Hazirbas, C., Golkov, V., van der Smagt, P., Cremers, D., Brox, T.: Flownet: Learning optical flow with convolutional networks. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 2758–2766 (2015)
2015
Cited alongside, same era.
Jozefowicz, R., Zaremba, W., Sutskever, I.: An empirical exploration of recurrent network architectures. In: ICML. pp. 2342–2350 (2015)
2015
Cited alongside, same era.
2015
Cited alongside, same era.
Zhou, Y., Berg, T.L.: Learning temporal transformations from time-lapse videos. In: European Conference on Computer Vision. pp. 262–277 (2016)
2016
Later among the works it cites.
2017
Later among the works it cites.
Ilg, E., Mayer, N., Saikia, T., Keuper, M., Dosovitskiy, A., Brox, T.: Flownet 2.0: Evolution of optical flow estimation with deep networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR). vol. 2 (2017)
2017
Later among the works it cites.
2017
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Meyer, S., Wang, O., Zimmer, H., Grosse, M., Sorkine-Hornung, A.: Phase-based frame interpolation for video. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1410–1418 (2015)
2015
Cited alongside, same era.
Srivastava, N., Mansimov, E., Salakhudinov, R.: Unsupervised learning of video representations using lstms. In: International conference on machine learning. pp. 843–852 (2015)
2015
Cited alongside, same era.
Dicle, C., Yilmaz, B., Camps, O., Sznaier, M.: Solving temporal puzzles. In: CVPR. pp. 5896–5905 (2016)
2016
Cited alongside, same era.
2016
Cited alongside, same era.
2016
Cited alongside, same era.
Peng, X., Schmid, C.: Multi-region two-stream r-cnn for action detection. In: ECCV. pp. 744–759. Springer (2016)
2016
Cited alongside, same era.
2016
Cited alongside, same era.
Xue, T., Wu, J., Bouman, K., Freeman, B.: Visual dynamics: Probabilistic future frame synthesis via cross convolutional networks. In: Advances in Neural Information Processing Systems. pp. 91–99 (2016)
2016
Cited alongside, same era.
Liang, X., Lee, L., Dai, W., Xing, E.P.: Dual motion gan for future-flow embedded video prediction. arXiv preprint (2017)
2017
Later among the works it cites.
Liu, Z., Yeh, R., Tang, X., Liu, Y., Agarwala, A.: Video frame synthesis using deep voxel flow. In: International Conference on Computer Vision (ICCV). vol. 2 (2017)
2017
Later among the works it cites.
Luc, P., Neverova, N., Couprie, C., Verbeek, J., LeCun, Y.: Predicting deeper into the future of semantic segmentation. In: of: ICCV 2017-International Conference on Computer Vision. p. 10 (2017)
2017
Later among the works it cites.
2017
Later among the works it cites.
2017
Later among the works it cites.
Pathak, D., Girshick, R., Dollár, P., Darrell, T., Hariharan, B.: Learning features by watching objects move. In: Computer Vision and Pattern Recognition (CVPR) (2017)
2017
Later among the works it cites.
2017
Later among the works it cites.
Yilmaz, B., Bekiroglu, K., Lagoa, C., Sznaier, M.: A randomized algorithm for parsimonious model identification. IEEE Transactions on Automatic Control 63
2018
Closest in time.