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We introduce LocoTrack, a highly accurate and efficient model designed for the task of tracking any point (TAP) across video sequences.
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Lee, J., Kim, D., Ponce, J., Ham, B.: Sfnet: Learning object-aware semantic correspondence. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 2278–2287 (2019)
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Hong, S., Cho, S., Nam, J., Lin, S., Kim, S.: Cost aggregation with 4d convolutional swin transformer for few-shot segmentation. In: European Conference on Computer Vision. pp. 108–126. Springer (2022)
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Sun, D., Herrmann, C., Reda, F., Rubinstein, M., Fleet, D.J., Freeman, W.T.: Disentangling architecture and training for optical flow. In: European Conference on Computer Vision. pp. 165–182. Springer (2022)
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2023
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Truong, P., Danelljan, M., Timofte, R., Van Gool, L.: Pdc-net+: Enhanced probabilistic dense correspondence network. IEEE Transactions on Pattern Analysis and Machine Intelligence (2023)
2023
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2023
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2023
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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)
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Cho, S., Huang, J., Kim, S., Lee, J.Y.: Flowtrack: Revisiting optical flow for long-range dense tracking. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 19268–19277 (2024)
2024
Closest in time.
Hong, S., Cho, S., Kim, S., Lin, S.: Unifying feature and cost aggregation with transformers for semantic and visual correspondence. In: The Twelfth International Conference on Learning Representations (2024)
2024
Closest in time.
Neoral, M., Šerỳch, J., Matas, J.: Mft: Long-term tracking of every pixel. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 6837–6847 (2024)
2024
Closest in time.