Meta-Learning for Adaptation of Deep Optical Flow Networks

In this paper, we propose an instance-wise meta-learning algorithm for optical flow domain adaptation. Typical optical flow algorithms with deep learning suffer from weak cross-domain performance since their trainings largely rely on synthetic datasets in specific domains. This prevents optical flow performance on different scenes from carrying similar performance in practice. Meanwhile, test-time do-main adaptation approaches for optical flow estimation are yet to be studied. Our proposed method, with some training data, learns to adapt more sensitively to incoming in-puts in the target domain. During the inference process, our method readily exploits the information only accessible in the test-time. Since our algorithm adapts to each input image, we incorporate traditional unsupervised losses for optical flow estimation. Moreover, with the observation that optical flows in a single domain typically contain many similar motions, we show that our method demonstrates high performance with only a small number of training data. This allows to save labeling efforts. Through the experiments on KITTI and MPI-Sintel datasets, our algorithm significantly outperforms the results without adaptation and shows consistently better performance in comparison to typical fine-tuning with the same amount of data. Also qualitatively our proposed method demonstrates more accurate results for the images with high errors in the original networks.