Papers › Learning by Analogy: Reliable Supervision from Transformations for Unsupervised...
Learning by Analogy: Reliable Supervision from Transformations for Unsupervised Optical Flow Estimation
Liang Liu, Jiangning Zhang, Ruifei He, Yong liu, Yabiao Wang, Ying Tai, Donghao Luo, Chengjie Wang, Jilin Li, Feiyue Huang
Unsupervised learning of optical flow, which leverages the supervision from view synthesis, has emerged as a promising alternative to supervised methods. However, the objective of unsupervised learning is likely to be unreliable in challenging scenes. In this work, we present a framework to use more reliable supervision from transformations. It simply twists the general unsupervised learning pipeline by running another forward pass with transformed data from augmentation, along with using transformed predictions of original data as the self-supervision signal. Besides, we further introduce a lightweight network with multiple frames by a highly-shared flow decoder. Our method consistently gets a leap of performance on several benchmarks with the best accuracy among deep unsupervised methods. Also, our method achieves competitive results to recent fully supervised methods while with much fewer parameters.
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Optical Flow Estimation | KITTI 2012 unsupervised | ARFlow-MV | Average End-Point Error | 1.5 | #2 of 2 | Archive leaderboard | report |
| Optical Flow Estimation | KITTI 2015 unsupervised | ARFlow-MV | Fl-all | 11.79 | #4 of 4 | Archive leaderboard | report |
| Optical Flow Estimation | Sintel Clean unsupervised | ARFlow-MV | Average End-Point Error | 4.49 | #2 of 5 | Archive leaderboard | report |
| Optical Flow Estimation | Sintel Final unsupervised | ARFlow-MV | Average End-Point Error | 5.67 | #3 of 5 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
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