Giovanni Maria Farinella, and Tal Hassner, editors, Computer Vision - ECCV 2022, pages 736-753, Cham, 2022. Springer Nature Switzerland.
[25] Yijin Li, Zhaoyang Huang, Shuo Chen, Xiaoyu Shi, Hongsheng Li, Hujun Bao, Zhaopeng Cui, and Guofeng Zhang. Blinkflow: A dataset to push the limits of event-based optical flow estimation. arXiv preprint arXiv:2303.07716, 2023.
[26] Nikolaus Mayer, Eddy Ilg, Philip Hausser, Philipp Fischer, Daniel Cremers, Alexey Dosovitskiy, and Thomas Brox. A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4040-4048, 2016.
[27] Zhixiang Min, Yiding Yang, and Enrique Dunn. Voldor: Visual odometry from log-logistic dense optical flow residuals. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 4898-4909, 2020.
[28] Anurag Ranjan and Michael J Black. Optical flow estimation using a spatial pyramid network. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 4161-4170, 2017.
[29] Zhile Ren, Orazio Gallo, Deqing Sun, Ming-Hsuan Yang, Erik B Sudderth, and Jan Kautz. A fusion approach for multi-frame optical flow estimation. In 2019 IEEE Winter Conference on Applications of Computer Vision (WACV), pages 2077-2086. IEEE, 2019.
[30] Peter Sand and Seth Teller. Particle video: Long-range motion estimation using point trajectories. International Journal of Computer Vision, 80:72-91, 2008.
[31] Xiaoyu Shi, Zhaoyang Huang, Weikang Bian, Dasong Li, Manyuan Zhang, Ka Chun Cheung, Simon See, Hongwei Qin, Jifeng Dai, and Hongsheng Li. Videoflow: Exploiting temporal cues for multi-frame optical flow estimation. arXiv preprint arXiv:2303.08340, 2023.
[32] Xiaoyu Shi, Zhaoyang Huang, Dasong Li, Manyuan Zhang, Ka Chun Cheung, Simon See, Hongwei Qin, Jifeng Dai, and Hongsheng Li. Flowformer++: Masked cost volume autoencoding for pretraining optical flow estimation. arXiv preprint arXiv:2303.01237, 2023.
[33] Xiuchao Sui, Shaohua Li, Xue Geng, Yan Wu, Xinxing Xu, Yong Liu, Rick Goh, and Hongyuan Zhu. Craft: Cross-attentional flow transformer for robust optical flow. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 17602-17611, 2022.
[34] Deqing Sun, Stefan Roth, and Michael J Black. A quantitative analysis of current practices in optical flow estimation and the principles behind them. International Journal of Computer Vision, 106(2):115-137, 2014.
[35] Deqing Sun, Xiaodong Yang, Ming-Yu Liu, and Jan Kautz. Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 8934-8943, 2018.
[36] Jiaming Sun, Zehong Shen, Yuang Wang, Hujun Bao, and Xiaowei Zhou. Loftr: Detector-free local feature matching with transformers. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 8922-8931, 2021.
[37] Shangkun Sun, Yuanqi Chen, Yu Zhu, Guodong Guo, and Ge Li. Skflow: Learning optical flow with super kernels. arXiv preprint arXiv:2205.14623, 2022.
[38] Ramana Sundararaman, Cedric De Almeida Braga, Eric Marchand, and Julien Pette. Tracking pedestrian heads in dense crowd. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 3865-3875, June 2021.
[39] Zachary Teed and Jia Deng. Raft: Recurrent all-pairs field transforms for optical flow. In European conference on computer vision, pages 402-419. Springer, 2020.
[40] Ilya O Tolstikhin, Neil Houlsby, Alexander Kolesnikov, Lucas Beyer, Xiaohua Zhai, Thomas Unterthiner, Jessica Yung, Andreas Steiner, Daniel Keysers, Jakob Uszkoreit, et al. Mlp-mixer: An all-mlp architecture for vision. Advances in neural information processing systems, 34:24261-24272, 2021.
[41] Xiaoshi Wu, Yiming Hao, Keqiang Sun, Yixiong Chen, Feng Zhu, Rui Zhao, and Hongsheng Li. Human preference score v2: A solid benchmark for evaluating human preferences of text-to-image synthesis. arXiv preprint arXiv:2306.09341, 2023.
[42] Xiaoshi Wu, Keqiang Sun, Feng Zhu, Rui Zhao, and Hongsheng Li. Better aligning text-to-image models with human preference. arXiv preprint arXiv:2303.14420, 2023.
[43] Liu Xinyang, Li Yijin, Teng Yanbin, Bao Hujun, Zhang Guofeng, Zhang Yinda, and Cui Zhaopeng. Multi-modal neural radiance field for monocular dense slam with a light-weight tof sensor. In International Conference on Computer Vision (ICCV), 2023.
[44] Jiarui Xu and Xiaolong Wang. Rethinking self-supervised correspondence learning: A video frame-level similarity perspective. In Proceedings of the IEEE/CVF International Conference on Computer Vision, pages 10075-10085, 2021.
[45] Gengshan Yang and Deva Ramanan. Volumetric correspondence networks for optical flow. Advances in neural information processing systems, 32:794-805, 2019.
[46] Shuai Yang, Yifan Zhou, Ziwei Liu, and Chen Change Loy. Rerender a video: Zero-shot text-guided video-to-video translation. arXiv preprint arXiv:2306.07954, 2023.