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Optical flow estimation has made great progress, but usually suffers fromdegradation under adverse weather. Although semi/full-supervised methods havemade good attempts, the domain shift between the synthetic and real adverseweather images would deteriorate their performance. To alleviate this issue,our start point is to unsupervisedly transfer the knowledge from source cleandomain to target degraded domain. Our key insight is that adverse weather doesnot change the intrinsic optical flow of the scene, but causes a significantdifference for the warp error between clean and degraded images. In this work,we propose the first unsupervised framework for adverse weather optical flowvia hierarchical motion-boundary adaptation. Specifically, we first employimage translation to construct the transformation relationship between cleanand degraded domains. In motion adaptation, we utilize the flow consistencyknowledge to align the cross-domain optical flows into a motion-invariancecommon space, where the optical flow from clean weather is used as theguidance-knowledge to obtain a preliminary optical flow for adverse weather.Furthermore, we leverage the warp error inconsistency which measures the motionmisalignment of the boundary between the clean and degraded domains, andpropose a joint intra- and inter-scene boundary contrastive adaptation torefine the motion boundary. The hierarchical motion and boundary adaptationjointly promotes optical flow in a unified framework. Extensive quantitativeand qualitative experiments have been performed to verify the superiority ofthe proposed method.

Unsupervised Hierarchical Domain Adaptation for Adverse Weather Optical Flow