  Future frame prediction in videos is a challenging problem because videos
include complicated movements and large appearance changes. Learning-based
future frame prediction approaches have been proposed in kinds of literature. A
common limitation of the existing learning-based approaches is a mismatch of
training data and test data. In the future frame prediction task, we can obtain
the ground truth data by just waiting for a few frames. It means we can update
the prediction model online in the test phase. Then, we propose an adaptive
update framework for the future frame prediction task. The proposed adaptive
updating framework consists of a pre-trained prediction network, a
continuous-updating prediction network, and a weight estimation network. We
also show that our pre-trained prediction model achieves comparable performance
to the existing state-of-the-art approaches. We demonstrate that our approach
outperforms existing methods especially for dynamically changing scenes.
