  Optical flow has achieved great success under clean scenes, but suffers from
restricted performance under foggy scenes. To bridge the clean-to-foggy domain
gap, the existing methods typically adopt the domain adaptation to transfer the
motion knowledge from clean to synthetic foggy domain. However, these methods
unexpectedly neglect the synthetic-to-real domain gap, and thus are erroneous
when applied to real-world scenes. To handle the practical optical flow under
real foggy scenes, in this work, we propose a novel unsupervised cumulative
domain adaptation optical flow (UCDA-Flow) framework: depth-association motion
adaptation and correlation-alignment motion adaptation. Specifically, we
discover that depth is a key ingredient to influence the optical flow: the
deeper depth, the inferior optical flow, which motivates us to design a
depth-association motion adaptation module to bridge the clean-to-foggy domain
gap. Moreover, we figure out that the cost volume correlation shares similar
distribution of the synthetic and real foggy images, which enlightens us to
devise a correlation-alignment motion adaptation module to distill motion
knowledge of the synthetic foggy domain to the real foggy domain. Note that
synthetic fog is designed as the intermediate domain. Under this unified
framework, the proposed cumulative adaptation progressively transfers knowledge
from clean scenes to real foggy scenes. Extensive experiments have been
performed to verify the superiority of the proposed method.
