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Although action recognition has achieved impressive results over recentyears, both collection and annotation of video training data are stilltime-consuming and cost intensive. Therefore, image-to-video adaptation hasbeen proposed to exploit labeling-free web image source for adapting onunlabeled target videos. This poses two major challenges: (1) spatial domainshift between web images and video frames; (2) modality gap between image andvideo data. To address these challenges, we propose Cycle Domain Adaptation(CycDA), a cycle-based approach for unsupervised image-to-video domainadaptation by leveraging the joint spatial information in images and videos onthe one hand and, on the other hand, training an independent spatio-temporalmodel to bridge the modality gap. We alternate between the spatial andspatio-temporal learning with knowledge transfer between the two in each cycle.We evaluate our approach on benchmark datasets for image-to-video as well asfor mixed-source domain adaptation achieving state-of-the-art results anddemonstrating the benefits of our cyclic adaptation. Code is available at\url{https://github.com/wlin-at/CycDA}.

CycDA: Unsupervised Cycle Domain Adaptation from Image to Video