  In optical flow estimation task, coarse-to-fine (C2F) warping strategy is
widely used to deal with the large displacement problem and provides efficiency
and speed. However, limited by the small search range between the first images
and warped second images, current coarse-to-fine optical flow networks fail to
capture small and fast-moving objects which disappear at coarse resolution
levels. To address this problem, we introduce a lightweight but effective
Global Matching Component (GMC) to grab global matching features. We propose a
new Hybrid Matching Optical Flow Network (HMFlow) by integrating GMC into
existing coarse-to-fine networks seamlessly. Besides keeping in high accuracy
and small model size, our proposed HMFlow can apply global matching features to
guide the network to discover the small and fast-moving objects mismatched by
local matching features. We also build a new dataset, named Small and
Fast-Moving Chairs (SFChairs), for evaluation. The experimental results show
that our proposed network achieves considerable performance, especially at
regions with small and fast-moving objects.
