Fetching the paper…
Reading the bibliography…
We introduce a novel self-supervised learning approach to learn representations of videos that are responsive to changes in the motion dynamics.
Caruana, R., de Sa, V.R.: Promoting poor features to supervisors: Some inputs work better as outputs. NIPS (1996)
1996
Earlier work this paper cites.
Ando, R., Zhang, T.: A framework for learning predictive structures from multiple tasks and unlabeled data. JMLR (2005)
2005
Earlier work this paper cites.
Everingham, M., Zisserman, A., Williams, C., Van-Gool, L.: The pascal visual object classes challenge. VOC (2006)
2006
Earlier work this paper cites.
2007
Earlier work this paper cites.
Schindler, K., Van Gool, L.: Action snippets: How many frames does human action recognition require? In: 2008 IEEE Conference on Computer Vision and Pattern Recognition. pp. 1–8. IEEE (2008)
2008
Earlier work this paper cites.
Kuehne, H., Jhuang, H., Garrote, E., Poggio, T., Serre, T.: HMDB: a large video database for human motion recognition. In: Proceedings of the International Conference on Computer Vision (ICCV) (2011)
2011
Earlier work this paper cites.
2012
Earlier work this paper cites.
Springenberg, J.T., Dosovitskiy, A., Brox, T., Riedmiller, M.: Striving for simplicity: The all convolutional net (2014)
2014
Earlier work this paper cites.
Agrawal, P., Carreira, J., Malik, J.: Learning to see by moving. The IEEE International Conference on Computer Vision (ICCV) (December 2015)
2015
Earlier work this paper cites.
Doersch, C., Gupta, A., Efros, A.A.: Unsupervised visual representation learning by context prediction. ICCV (2015)
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Ma, S., Huang, Z., Karpathy, A., Khosla, A., Bernstein, M., Berg, A.C., Fei-Fei, L.: Imagenet large scale visual recognition challenge. International Journal of Computer Vision 115
2015
Earlier work this paper cites.
Tran, D., Bourdev, L., Fergus, R., Torresani, L., Paluri, M.: Learning spatiotemporal features with 3d convolutional networks. In: Proceedings of the IEEE international conference on computer vision. pp. 4489–4497 (2015)
2015
Earlier work this paper cites.
Wang, X., Gupta, A.: Unsupervised learning of visual representations using videos. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 2794–2802 (2015)
2015
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 770–778 (2016)
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
Misra, I., Zitnick, C.L., Hebert, M.: Shuffle and learn: unsupervised learning using temporal order verification. In: European Conference on Computer Vision. pp. 527–544. Springer (2016)
2016
Earlier work this paper cites.
Noroozi, M., Favaro, P.: Unsupervised learning of visual representations by solving jigsaw puzzles. In: European Conference on Computer Vision. pp. 69–84. Springer (2016)
2016
Earlier work this paper cites.
Owens, A., Wu, J., McDermott, J.H., Freeman, W.T., Torralba, A.: Ambient sound provides supervision for visual learning. In: European Conference on Computer Vision. pp. 801–816. Springer (2016)
2016
Earlier work this paper cites.
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: Unified, real-time object detection. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 779–788 (2016)
2016
Cited alongside, same era.
Zhang, R., Isola, P., Efros, A.A.: Colorful image colorization. In: European Conference on Computer Vision. pp. 649–666. Springer (2016)
2016
Cited alongside, same era.
Arandjelovic, R., Zisserman, A.: Look, listen and learn. In: 2017 IEEE International Conference on Computer Vision (ICCV). pp. 609–617. IEEE (2017)
2017
Cited alongside, same era.
Brattoli, B., Büchler, U., Wahl, A.S., Schwab, M.E., Ommer, B.: Lstm self-supervision for detailed behavior analysis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). vol. 2 (2017)
2017
Cited alongside, same era.
Korbar, B., Tran, D., Torresani, L.: Cooperative learning of audio and video models from self-supervised synchronization. In: Advances in Neural Information Processing Systems. pp. 7763–7774 (2018)
2018
Later among the works it cites.
Owens, A., Efros, A.A.: Audio-visual scene analysis with self-supervised multisensory features. In: The European Conference on Computer Vision (ECCV) (September 2018)
2018
Later among the works it cites.
Tran, D., Wang, H., Torresani, L., Ray, J., LeCun, Y., Paluri, M.: A closer look at spatiotemporal convolutions for action recognition. In: Proceedings of the IEEE conference on Computer Vision and Pattern Recognition. pp. 6450–6459 (2018)
2018
Later among the works it cites.
Vondrick, C., Shrivastava, A., Fathi, A., Guadarrama, S., Murphy, K.: Tracking emerges by colorizing videos. In: Proc. ECCV (2018)
2018
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Fernando, B., Bilen, H., Gavves, E., Gould, S.: Self-supervised video representation learning with odd-one-out networks. In: Computer Vision and Pattern Recognition (CVPR), 2017 IEEE Conference on. pp. 5729–5738. IEEE (2017)
2017
Cited alongside, same era.
He, K., Gkioxari, G., Dollár, P., Girshick, R.: Mask r-cnn. In: Proceedings of the IEEE international conference on computer vision. pp. 2961–2969 (2017)
2017
Cited alongside, same era.
Larsson, G., Maire, M., Shakhnarovich, G.: Colorization as a proxy task for visual understanding. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 6874–6883 (2017)
2017
Cited alongside, same era.
Lee, H.Y., Huang, J.B., Singh, M., Yang, M.H.: Unsupervised representation learning by sorting sequences. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 667–676 (2017)
2017
Cited alongside, same era.
2017
Cited alongside, same era.
Luo, Z., Peng, B., Huang, D.A., Alahi, A., Fei-Fei, L.: Unsupervised learning of long-term motion dynamics for videos. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 2203–2212 (2017)
2017
Cited alongside, same era.
Zhang, R., Isola, P., Efros, A.A.: Split-brain autoencoders: Unsupervised learning by cross-channel prediction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 1058–1067 (2017)
2017
Cited alongside, same era.
2018
Cited alongside, same era.
Wei, D., Lim, J., Zisserman, A., Freeman, W.T.: Learning and using the arrow of time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 8052–8060 (2018)
2018
Later among the works it cites.
Han, T., Xie, W., Zisserman, A.: Video representation learning by dense predictive coding. In: Proceedings of the IEEE International Conference on Computer Vision Workshops. pp. 0–0 (2019)
2019
Later among the works it cites.
Iwana, B.K., Uchida, S.: Time series classification using local distance-based features in multi-modal fusion networks. Pattern Recognition 97
2019
Later among the works it cites.
Kim, D., Cho, D., Kweon, I.S.: Self-supervised video representation learning with space-time cubic puzzles. In: Proceedings of the AAAI Conference on Artificial Intelligence. vol. 33, pp. 8545–8552 (2019)
2019
Later among the works it cites.
Li, X., Liu, S., De Mello, S., Wang, X., Kautz, J., Yang, M.H.: Joint-task self-supervised learning for temporal correspondence. In: Advances in Neural Information Processing Systems. pp. 317–327 (2019)
2019
Later among the works it cites.
2019
Later among the works it cites.
Wang, J., Jiao, J., Bao, L., He, S., Liu, Y., Liu, W.: Self-supervised spatio-temporal representation learning for videos by predicting motion and appearance statistics. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 4006–4015 (2019)
2019
Later among the works it cites.
Wang, X., Jabri, A., Efros, A.A.: Learning correspondence from the cycle-consistency of time. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 2566–2576 (2019)
2019
Later among the works it cites.
Xu, D., Xiao, J., Zhao, Z., Shao, J., Xie, D., Zhuang, Y.: Self-supervised spatiotemporal learning via video clip order prediction. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 10334–10343 (2019)
2019
Later among the works it cites.
Benaim, S., Ephrat, A., Lang, O., Mosseri, I., Freeman, W.T., Rubinstein, M., Irani, M., Dekel, T.: Speednet: Learning the speediness in videos. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 9922–9931 (2020)
2020
Closest in time.
Epstein, D., Chen, B., Vondrick, C.: Oops! predicting unintentional action in video. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 919–929 (2020)
2020
Closest in time.
Jenni, S., Jin, H., Favaro, P.: Steering self-supervised feature learning beyond local pixel statistics. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 6408–6417 (2020)
2020
Closest in time.
2020
Closest in time.
Yao, Y., Liu, C., Luo, D., Zhou, Y., Ye, Q.: Video playback rate perception for self-supervised spatio-temporal representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 6548–6557 (2020)
2020
Closest in time.