Optical flow computation is an important research direction in image processing and computer vision. With the rapid development of the deep learning technology

the convolutional neural network based deep learning theories and methodologies have been the research focus of optical flow computation. This article mainly reviews the research progress of the deep learning based optical flow estimation technologies. First

the typical models and training strategies of the optical flow computing networks with supervised learning

unsupervised learning and semi-supervised learning are introduced. Second

the optimization methods of various network models are described and analyzed. Third

the evaluation benchmarks of Middlebury

MPI-Sintel and KITTI databases are summarized

and the experimental comparison results and analysis between the different deep-learning and variational optical flow methods are conducted. Finally

we discuss some issues of the deep learning based optical flow computation technology including the model complexity and generalization

the robustness of optical flow estimation and the accuracy of the small sample training. Afterwards

we point out several possible solutions and research ideas to address the above mentioned issues.