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Unsupervised learning of optical flow, which leverages the supervision fromview synthesis, has emerged as a promising alternative to supervised methods.However, the objective of unsupervised learning is likely to be unreliable inchallenging scenes. In this work, we present a framework to use more reliablesupervision from transformations. It simply twists the general unsupervisedlearning pipeline by running another forward pass with transformed data fromaugmentation, along with using transformed predictions of original data as theself-supervision signal. Besides, we further introduce a lightweight networkwith multiple frames by a highly-shared flow decoder. Our method consistentlygets a leap of performance on several benchmarks with the best accuracy amongdeep unsupervised methods. Also, our method achieves competitive results torecent fully supervised methods while with much fewer parameters.

Learning by Analogy: Reliable Supervision from Transformations for Unsupervised Optical Flow Estimation