Scene-Adaptive Video Frame Interpolation via Meta-Learning
This work addresses the problem of improving interpolation accuracy for varied video content, offering a practical solution for applications like video processing, though it is incremental as it builds on existing networks.
The paper tackles the challenge of video frame interpolation across diverse scenarios by proposing a meta-learning approach for test-time adaptation, achieving significant performance gains with only a single gradient update and no extra parameters.
Video frame interpolation is a challenging problem because there are different scenarios for each video depending on the variety of foreground and background motion, frame rate, and occlusion. It is therefore difficult for a single network with fixed parameters to generalize across different videos. Ideally, one could have a different network for each scenario, but this is computationally infeasible for practical applications. In this work, we propose to adapt the model to each video by making use of additional information that is readily available at test time and yet has not been exploited in previous works. We first show the benefits of `test-time adaptation' through simple fine-tuning of a network, then we greatly improve its efficiency by incorporating meta-learning. We obtain significant performance gains with only a single gradient update without any additional parameters. Finally, we show that our meta-learning framework can be easily employed to any video frame interpolation network and can consistently improve its performance on multiple benchmark datasets.