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.

本文提出了一种利用测试时可用但之前未被利用的额外信息为每个视频自适应模型的方法，包括使用简单的微调和集成元学习等，以只进行单次梯度更新且不增加额外参数的方式获得显著的性能提升。 同时，我们表明此元学习框架可轻松应用于任何视频帧插值网络，并能在多个基准数据集上持续提高其性能。

元学习实现的场景自适应视频帧插值