  Estimating per-pixel motion between video frames, known as optical flow, is a
long-standing problem in video understanding and analysis. Most contemporary
optical flow techniques largely focus on addressing the cross-image matching
with feature similarity, with few methods considering how to explicitly reason
over the given scene for achieving a holistic motion understanding. In this
work, taking a fresh perspective, we introduce a novel graph-based approach,
called adaptive graph reasoning for optical flow (AGFlow), to emphasize the
value of scene/context information in optical flow. Our key idea is to decouple
the context reasoning from the matching procedure, and exploit scene
information to effectively assist motion estimation by learning to reason over
the adaptive graph. The proposed AGFlow can effectively exploit the context
information and incorporate it within the matching procedure, producing more
robust and accurate results. On both Sintel clean and final passes, our AGFlow
achieves the best accuracy with EPE of 1.43 and 2.47 pixels, outperforming
state-of-the-art approaches by 11.2% and 13.6%, respectively.
