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Shuffle & Attend: Video Domain Adaptation
ECCV 2020 | We address the problem of domain adaptation in videos for the task of human action recognition. Inspired by image-based domain adaptation, we propose to (i) learn to align important (discriminative) clips to achieve improved representation for the target domain and (ii) employ a self-supervised task that encourages the model to focus on actions rather than scene-context information in order to learn representations, which are more robust to domain shifts.
Collaborators: Jinwoo Choi, Gaurav Sharma, Samuel Schulter, Jia-Bin Huang
