  Optical flow estimation is a fundamental task in computer vision. Recent
direct-regression methods using deep neural networks achieve remarkable
performance improvement. However, they do not explicitly capture long-term
motion correspondences and thus cannot handle large motions effectively. In
this paper, inspired by the traditional matching-optimization methods where
matching is introduced to handle large displacements before energy-based
optimizations, we introduce a simple but effective global matching step before
the direct regression and develop a learning-based matching-optimization
framework, namely GMFlowNet. In GMFlowNet, global matching is efficiently
calculated by applying argmax on 4D cost volumes. Additionally, to improve the
matching quality, we propose patch-based overlapping attention to extract large
context features. Extensive experiments demonstrate that GMFlowNet outperforms
RAFT, the most popular optimization-only method, by a large margin and achieves
state-of-the-art performance on standard benchmarks. Thanks to the matching and
overlapping attention, GMFlowNet obtains major improvements on the predictions
for textureless regions and large motions. Our code is made publicly available
at https://github.com/xiaofeng94/GMFlowNet
