CycDA: Unsupervised Cycle Domain Adaptation from Image to Video

Understand

Although action recognition has achieved impressive results over recent years, both collection and annotation of video training data are still time-consuming and cost intensive.

• Therefore, image-to-video adaptation has been proposed to exploit labeling-free web image source for adapting on unlabeled target videos.
• This poses two major challenges: (1) spatial domain shift between web images and video frames; (2) modality gap between image and video data.
• To address these challenges, we propose Cycle Domain Adaptation (CycDA), a cycle-based approach for unsupervised image-to-video domain adaptation by leveraging the joint spatial information in images and videos on the one hand and, on the other hand, training an independent spatio-temporal model to bridge the modality gap.