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We introduce CoTracker, a transformer-based model that tracks a large number of 2D points in long video sequences.
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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: Proc. ICCV (2015)
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Li, Y., Zhu, J., Hoi, S.C.: Reliable patch trackers: Robust visual tracking by exploiting reliable patches. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 353–361 (2015)
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Bertinetto, L., Valmadre, J., Henriques, J.F., Vedaldi, A., Torr, P.H.: Fully-convolutional siamese networks for object tracking. In: Computer Vision–ECCV 2016 Workshops: Amsterdam, The Netherlands, October 8-10 and 15-16, 2016, Proceedings, Part II 14. pp. 850–865. Springer (2016)
2016
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Held, D., Thrun, S., Savarese, S.: Learning to track at 100 fps with deep regression networks. In: Computer Vision–ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11–14, 2016, Proceedings, Part I 14. pp. 749–765. Springer (2016)
2016
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Mayer, N., Ilg, E., Hausser, P., Fischer, P., Cremers, D., Dosovitskiy, A., Brox, T.: A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation. In: Proc. CVPR (2016)
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Carreira, J., Zisserman, A.: Quo vadis, action recognition? a new model and the kinetics dataset. In: Proc. CVPR (2017)
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Danelljan, M., Bhat, G., Shahbaz Khan, F., Felsberg, M.: Eco: Efficient convolution operators for tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 6638–6646 (2017)
2017
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Ilg, E., Mayer, N., Saikia, T., Keuper, M., Dosovitskiy, A., Brox, T.: FlowNet 2.0: Evolution of optical flow estimation with deep networks. In: Proc. CVPR (2017)
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Pont-Tuset, J., Perazzi, F., Caelles, S., Arbeláez, P., Sorkine-Hornung, A., Van Gool, L.: The 2017 davis challenge on video object segmentation. arXiv (2017)
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Song, Y., Ma, C., Gong, L., Zhang, J., Lau, R.W., Yang, M.H.: Crest: Convolutional residual learning for visual tracking. In: Proceedings of the IEEE international conference on computer vision. pp. 2555–2564 (2017)
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Xu, H., Yang, J., Cai, J., Zhang, J., Tong, X.: High-resolution optical flow from 1d attention and correlation. In: Proc. CVPR (2021)
2021
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Zhang, F., Woodford, O.J., Prisacariu, V.A., Torr, P.H.: Separable flow: Learning motion cost volumes for optical flow estimation. In: Proc. CVPR (2021)
2021
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2022
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Cui, Y., Jiang, C., Wang, L., Wu, G.: Mixformer: End-to-end tracking with iterative mixed attention. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 13608–13618 (2022)
2022
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Doersch, C., Gupta, A., Markeeva, L., Recasens, A., Smaira, L., Aytar, Y., Carreira, J., Zisserman, A., Yang, Y.: Tap-vid: A benchmark for tracking any point in a video. arXiv (2022)
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2017
Cited alongside, same era.
Xu, J., Ranftl, R., Koltun, V.: Accurate optical flow via direct cost volume processing. In: Proc. CVPR (July 2017)
2017
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Janai, J., Guney, F., Ranjan, A., Black, M., Geiger, A.: Unsupervised learning of multi-frame optical flow with occlusions. In: Proc. ECCV (2018)
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Jiang, H., Sun, D., Jampani, V., Yang, M.H., Learned-Miller, E., Kautz, J.: Super slomo: High quality estimation of multiple intermediate frames for video interpolation. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 9000–9008 (2018)
2018
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Li, F., Tian, C., Zuo, W., Zhang, L., Yang, M.H.: Learning spatial-temporal regularized correlation filters for visual tracking. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 4904–4913 (2018)
2018
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Sun, D., Yang, X., Liu, M.Y., Kautz, J.: Pwc-net: Cnns for optical flow using pyramid, warping, and cost volume. In: Proc. CVPR (2018)
2018
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Bhat, G., Danelljan, M., Gool, L.V., Timofte, R.: Learning discriminative model prediction for tracking. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (October 2019)
2019
Cited alongside, same era.
Danelljan, M., Bhat, G., Khan, F.S., Felsberg, M.: Atom: Accurate tracking by overlap maximization. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 4660–4669 (2019)
2019
Cited alongside, same era.
2022
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Greff, K., Belletti, F., Beyer, L., Doersch, C., Du, Y., Duckworth, D., Fleet, D.J., Gnanapragasam, D., Golemo, F., Herrmann, C., et al.: Kubric: A scalable dataset generator. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 3749–3761 (2022)
2022
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Harley, A.W., Fang, Z., Fragkiadaki, K.: Particle video revisited: Tracking through occlusions using point trajectories. In: Proc. ECCV (2022)
2022
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Harley, A.W., Fang, Z., Fragkiadaki, K.: Particle videos revisited: Tracking through occlusions using point trajectories. In: Proc. ECCV (2022)
2022
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Huang, Z., Shi, X., Zhang, C., Wang, Q., Cheung, K.C., Qin, H., Dai, J., Li, H.: Flowformer: A transformer architecture for optical flow. In: Proc. ECCV (2022)
2022
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Jaegle, A., Borgeaud, S., Alayrac, J., Doersch, C., Ionescu, C., Ding, D., Koppula, S., Zoran, D., Brock, A., Shelhamer, E., Hénaff, O.J., Botvinick, M.M., Zisserman, A., Vinyals, O., Carreira, J.: Perceiver IO: A general architecture for structured inputs & outputs. In: Proc. ICLR (2022)
2022
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Sui, X., Li, S., Geng, X., Wu, Y., Xu, X., Liu, Y., Goh, R., Zhu, H.: Craft: Cross-attentional flow transformer for robust optical flow. In: Proc. CVPR (2022)
2022
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Sun, S., Chen, Y., Zhu, Y., Guo, G., Li, G.: Skflow: Learning optical flow with super kernels. arXiv (2022)
2022
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Yang, G., Vo, M., Neverova, N., Ramanan, D., Vedaldi, A., Joo, H.: Banmo: Building animatable 3d neural models from many casual videos. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 2863–2873 (2022)
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Zhao, S., Zhao, L., Zhang, Z., Zhou, E., Metaxas, D.: Global matching with overlapping attention for optical flow estimation. In: Proc. CVPR (2022)
2022
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2023
Closest in time.
Doersch, C., Yang, Y., Vecerik, M., Gokay, D., Gupta, A., Aytar, Y., Carreira, J., Zisserman, A.: Tapir: Tracking any point with per-frame initialization and temporal refinement (2023)
2023
Closest in time.
Karaev, N., Rocco, I., Graham, B., Neverova, N., Vedaldi, A., Rupprecht, C.: Dynamicstereo: Consistent dynamic depth from stereo videos. In: Proc. CVPR (2023)
2023
Closest in time.
Luo, J., Wan, Z., Li, B., Dai, Y., et al.: Continuous parametric optical flow. In: Thirty-seventh Conference on Neural Information Processing Systems (2023)
2023
Closest in time.
Neoral, M., Šerých, J., Matas, J.: Mft: Long-term tracking of every pixel (2023)
2023
Closest in time.
Shi, X., Huang, Z., Bian, W., Li, D., Zhang, M., Cheung, K.C., See, S., Qin, H., Dai, J., Li, H.: Videoflow: Exploiting temporal cues for multi-frame optical flow estimation. arXiv (2023)
2023
Closest in time.
Shi, X., Huang, Z., Li, D., Zhang, M., Cheung, K.C., See, S., Qin, H., Dai, J., Li, H.: Flowformer++: Masked cost volume autoencoding for pretraining optical flow estimation. arXiv (2023)
2023
Closest in time.
Wang, Q., Chang, Y.Y., Cai, R., Li, Z., Hariharan, B., Holynski, A., Snavely, N.: Tracking everything everywhere all at once (2023)
2023
Closest in time.
Zheng, Y., Harley, A.W., Shen, B., Wetzstein, G., Guibas, L.J.: Pointodyssey: A large-scale synthetic dataset for long-term point tracking. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 19855–19865 (2023)
2023
Closest in time.