The Space-Time Video Super-Resolution (STVSR) task aims to enhance the visual
quality of videos, by simultaneously performing video frame interpolation (VFI)
and video super-resolution (VSR). However, facing the challenge of the
additional temporal dimension and scale inconsistency, most existing STVSR
methods are complex and inflexible in dynamically modeling different motion
amplitudes. In this work, we find that choosing an appropriate processing scale
achieves remarkable benefits in flow-based feature propagation. We propose a
novel Scale-Adaptive Feature Aggregation (SAFA) network that adaptively selects
sub-networks with different processing scales for individual samples.
Experiments on four public STVSR benchmarks demonstrate that SAFA achieves
state-of-the-art performance. Our SAFA network outperforms recent
state-of-the-art methods such as TMNet and VideoINR by an average improvement
of over 0.5dB on PSNR, while requiring less than half the number of parameters
and only 1/3 computational costs.

通过选择适当的处理尺度，提出了一种自适应选择子网络的 SAFA 网络，用于进行时空视频超分辨率（STVSR）任务，取得了较好的性能。