The video frame interpolation (VFI) model applies the convolution operation
to all locations, leading to redundant computations in regions with easy
motion. We can use dynamic spatial pruning method to skip redundant
computation, but this method cannot properly identify easy regions in VFI tasks
without supervision. In this paper, we develop an Uncertainty-Guided Spatial
Pruning (UGSP) architecture to skip redundant computation for efficient frame
interpolation dynamically. Specifically, pixels with low uncertainty indicate
easy regions, where the calculation can be reduced without bringing undesirable
visual results. Therefore, we utilize uncertainty-generated mask labels to
guide our UGSP in properly locating the easy region. Furthermore, we propose a
self-contrast training strategy that leverages an auxiliary non-pruning branch
to improve the performance of our UGSP. Extensive experiments show that UGSP
maintains performance but reduces FLOPs by 34%/52%/30% compared to baseline
without pruning on Vimeo90K/UCF101/MiddleBury datasets. In addition, our method
achieves state-of-the-art performance with lower FLOPs on multiple benchmarks.

本文提出了一种基于不确定性引导的空间剪枝（UGSP）架构，通过利用不确定性生成的遮罩标签来正确定位易区域，同时采用自对比训练策略提高 UGSP 性能。实验证明，与未剪枝基准相比，UGSP 在 Vimeo90K/UCF101/MiddleBury 数据集上分别减少了 34%/52%/30% 的 FLOPs，且在多个基准上以更低的 FLOPs 实现了最先进的性能。