The DOI matches its Crossref record; 93 of the 93 references it cites were found in a registry or an index.
Learning Correspondence From the Cycle-Consistency of Time
Open accessAbstract
We introduce a self-supervised method for learning visual correspondence from unlabeled video. The main idea is to use cycle-consistency in time as free supervisory signal for learning visual representations from scratch. At training time, our model learns a feature map representation to be useful for performing cycle-consistent tracking. At test time, we use the acquired representation to find nearest neighbors across space and time. We demonstrate the generalizability of the representation -- without finetuning -- across a range of visual correspondence tasks, including video object segmentation, keypoint tracking, and optical flow. Our approach outperforms previous self-supervised methods and performs competitively with strongly supervised methods.
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Cites
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Cited by
Emerging Properties in Self-Supervised Vision Transformers
Caron, M., Touvron, H., Misra, I., Jegou, H., Mairal, J., Bojanowski, P., & Joulin, A. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00951 · Cited by 5,638
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Oquab, M., Darcet, T., Moutakanni, T., Vo, H., Szafraniec, M., Khalidov, V., Fernandez, P., Haziza, D., Massa, F., El-Nouby, A., et al. (2023) · arXiv · doi:10.48550/arxiv.2304.07193 · Cited by 1,078
“0% on Sketch (Wang et al., 2019) (2018) respectively – are mentioned at the top of the Table.”
BackgroundSelf-Supervised Representation Learning: Introduction, advances, and challenges
Ericsson, L., Gouk, H., Loy, C. C., & Hospedales, T. M. (2022) · IEEE Signal Processing Magazine · doi:10.1109/msp.2021.3134634 · Cited by 433
Self-Supervised Learning by Cross-Modal Audio-Video Clustering
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Self-supervised Co-training for Video Representation Learning
Han, T., Xie, W., & Zisserman, A. (2020) · arXiv · doi:10.48550/arxiv.2010.09709 · Cited by 292
Metadata
- Authors
- Wang, X., Jabri, A., & Efros, A. A.
- Venue
- 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
- Publisher
- IEEE
- Published
- 2019-06
- Pages
- 2561-2571
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2964700958
- Cited by
- 445
- References
- 93
- FWCI
- 24.12
- Type
- conference paper
- Language
- en
- Open access
- green
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- Wang, X., Jabri, A., & Efros, A. A. (2019). Learning Correspondence From the Cycle-Consistency of Time. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2019.00267
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