[1] S. Aigner and M. Körner. Futuregan: Anticipating the future frames of video sequences using spatio-temporal 3d convolutions in progressively growing gans. arXiv preprint arXiv:1810.01325, 2018.
[2] L. Castrejon, N. Ballas, and A. Courville. Improved conditional vrnns for video prediction. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 7608-7617, 2019.
[3] Z. Chang, X. Zhang, S. Wang, S. Ma, Y. Ye, X. Xinguang, and W. Gao. Mau: A motion-aware unit for video prediction and beyond. Advances in Neural Information Processing Systems, 34, 2021.
[4] T. Chen, S. Kornblith, M. Norouzi, and G. Hinton. A simple framework for contrastive learning of visual representations. In Proceedings of the International Conference on Machine Learning, 2020.
[5] M. Contributors. MMCV: OpenMMLab computer vision foundation. https://github.com/open-mmlab/mmcv, 2018.
[6] P. Dollár, C. Wojek, B. Schiele, and P. Perona. Pedestrian detection: A benchmark. In CVPR, June 2009.
[7] A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, et al. An image is worth 16x16 words: Transformers for image recognition at scale. In International Conference on Learning Representations.
[8] S. Fang, Q. Zhang, G. Meng, S. Xiang, and C. Pan. GSTnet: Global spatial-temporal network for traffic flow prediction. In IJCAI, pages 2286-2293, 2019.
[9] C. Finn, I. Goodfellow, and S. Levine. Unsupervised learning for physical interaction through video prediction. Advances in Neural Information Processing Systems, 29, 2016.
[10] Z. Gao, C. Tan, and S. Z. Li. Simvp: Simpler yet better video prediction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 3170–3180, 2022.
[11] A. Geiger, P. Lenz, C. Stiller, and R. Urtasun. Vision meets robotics: The kitti dataset. The International Journal of Robotics Research, 32(11):1231-1237, 2013.
[12] V. L. Guen and N. Thome. Disentangling physical dynamics from unknown factors for unsupervised video prediction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11474-11484, 2020.
[13] M.-H. Guo, C.-Z. Lu, Z.-N. Liu, M.-M. Cheng, and S.-M. Hu. Visual attention network. arXiv preprint arXiv:2202.09741, 2022.
[14] K. He, X. Chen, S. Xie, Y. Li, P. Dollár, and R. Girshick. Masked autoencoders are scalable vision learners. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022.
[15] S. Hochreiter and J. Schmidhuber. Long short-term memory. Neural computation, 9(8):1735-1780, 1997.
[16] J.-T. Hsieh, B. Liu, D.-A. Huang, L. F. Fei-Fei, and J. C. Niebles. Learning to decompose and disentangle representations for video prediction. Advances in Neural Information Processing Systems, 31, 2018.
[17] X. Hu, Z. Huang, A. Huang, J. Xu, and S. Zhou. A dynamic multi-scale voxel flow network for video prediction. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2023.
[18] C. Ionescu, D. Papava, V. Olaru, and C. Sminchisescu. Human3.6m: Large scale datasets and predictive methods for 3d human sensing in natural environments. IEEE transactions on pattern analysis and machine intelligence, 36(7):1325-1339, 2013.
[19] S. Jenni, G. Meishvili, and P. Favaro. Video representation learning by recognizing temporal transformations. In European Conference on Computer Vision, pages 425-442. Springer, 2020.
[20] A. Krizhevsky, G. Hinton, et al. Learning multiple layers of features from tiny images. 2009.
[21] K. Li, Y. Wang, J. Zhang, P. Gao, G. Song, Y. Liu, H. Li, and Y. Qiao. Uniformer: Unifying convolution and self-attention for visual recognition. arXiv preprint arXiv:2201.09450, 2022.