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.

本文提出了一种使用变换提供的可靠监督信息的框架，通过使用数据增强技术来运行另一个向前传递的过程，并使用原始数据的转换后的预测结果作为自我监督信号，从而得到了多帧轻量级网络的最佳精度。

类比学习：基于变换的可靠监督用于无监督光流估计