  Optical flow, which computes the apparent motion from a pair of video frames,
is a critical tool for scene motion estimation. Correlation volume is the
central component of optical flow computational neural models. It estimates the
pairwise matching costs between cross-frame features, and is then used to
decode optical flow. However, traditional correlation volume is frequently
noisy, outlier-prone, and sensitive to motion blur. We observe that, although
the recent RAFT algorithm also adopts the traditional correlation volume, its
additional context encoder provides semantically representative features to the
flow decoder, implicitly compensating for the deficiency of the correlation
volume. However, the benefits of this context encoder has been barely discussed
or exploited. In this paper, we first investigate the functionality of RAFT's
context encoder, then propose a new Context Guided Correlation Volume (CGCV)
via gating and lifting schemes. CGCV can be universally integrated with
RAFT-based flow computation methods for enhanced performance, especially
effective in the presence of motion blur, de-focus blur and atmospheric
effects. By incorporating the proposed CGCV with previous Global Motion
Aggregation (GMA) method, at a minor cost of 0.5% extra parameters, the rank of
GMA is lifted by 23 places on KITTI 2015 Leader Board, and 3 places on Sintel
Leader Board. Moreover, at a similar model size, our correlation volume
achieves competitive or superior performance to state of the art peer
supervised models that employ Transformers or Graph Reasoning, as verified by
extensive experiments.
