Recently, dataset distillation has paved the way towards efficient machine
learning, especially for image datasets. However, the distillation for videos,
characterized by an exclusive temporal dimension, remains an underexplored
domain. In this work, we provide the first systematic study of video
distillation and introduce a taxonomy to categorize temporal compression. Our
investigation reveals that the temporal information is usually not well learned
during distillation , and the temporal dimension of synthetic data contributes
little. The observations motivate our unified framework of disentangling the
dynamic and static information in the videos. It first distills the videos into
still images as static memory and then compensates the dynamic and motion
information with a learnable dynamic memory block. Our method achieves
state-of-the-art on video datasets at different scales, with notably smaller
storage expenditure. Our code will be publicly available.

通过对视频进行数据蒸馏，提出了一种方法来解决视频中的时间维度问题，该方法首先将视频转化为静态图像作为静态内存，然后使用可学习的动态内存块补偿动态和运动信息，以在不同规模的视频数据集上实现最先进的表现。