  Optical flow estimation aims to find the 2D motion field by identifying
corresponding pixels between two images. Despite the tremendous progress of
deep learning-based optical flow methods, it remains a challenge to accurately
estimate large displacements with motion blur. This is mainly because the
correlation volume, the basis of pixel matching, is computed as the dot product
of the convolutional features of the two images. The locality of convolutional
features makes the computed correlations susceptible to various noises. On
large displacements with motion blur, noisy correlations could cause severe
errors in the estimated flow. To overcome this challenge, we propose a new
architecture "CRoss-Attentional Flow Transformer" (CRAFT), aiming to revitalize
the correlation volume computation. In CRAFT, a Semantic Smoothing Transformer
layer transforms the features of one frame, making them more global and
semantically stable. In addition, the dot-product correlations are replaced
with transformer Cross-Frame Attention. This layer filters out feature noises
through the Query and Key projections, and computes more accurate correlations.
On Sintel (Final) and KITTI (foreground) benchmarks, CRAFT has achieved new
state-of-the-art performance. Moreover, to test the robustness of different
models on large motions, we designed an image shifting attack that shifts input
images to generate large artificial motions. Under this attack, CRAFT performs
much more robustly than two representative methods, RAFT and GMA. The code of
CRAFT is is available at https://github.com/askerlee/craft.
