DNN-based frame interpolation, which generates intermediate frames from two
consecutive frames, is often dependent on model architectures with a large
number of features, preventing their deployment on systems with limited
resources, such as mobile devices. We present a compression-driven network
design for frame interpolation that leverages model pruning through
sparsity-inducing optimization to greatly reduce the model size while attaining
higher performance. Concretely, we begin by compressing the recently proposed
AdaCoF model and demonstrating that a 10 times compressed AdaCoF performs
similarly to its original counterpart, where different strategies for using
layerwise sparsity information as a guide are comprehensively investigated
under a variety of hyperparameter settings. We then enhance this compressed
model by introducing a multi-resolution warping module, which improves visual
consistency with multi-level details. As a result, we achieve a considerable
performance gain with a quarter of the size of the original AdaCoF. In
addition, our model performs favorably against other state-of-the-art
approaches on a wide variety of datasets. We note that the suggested
compression-driven framework is generic and can be easily transferred to other
DNN-based frame interpolation algorithms. The source code is available at
this https URL

提出了一种基于压缩的网络设计用于帧插值，通过稀疏感知优化实现模型修剪，引入多分辨率均值模块，大幅减小模型规模并取得了很高的性能。