We present a new method for finding video CNN architectures that capture rich
spatio-temporal information in videos. Previous work, taking advantage of 3D
convolutions, obtained promising results by manually designing video CNN
architectures. We here develop a novel evolutionary search algorithm that
automatically explores models with different types and combinations of layers
to jointly learn interactions between spatial and temporal aspects of video
representations. We demonstrate the generality of this algorithm by applying it
to two meta-architectures, obtaining new architectures superior to manually
designed architectures. Further, we propose a new component, the iTGM layer,
which more efficiently utilizes its parameters to allow learning of space-time
interactions over longer time horizons. The iTGM layer is often preferred by
the evolutionary algorithm and allows building cost-efficient networks. The
proposed approach discovers new and diverse video architectures that were
previously unknown. More importantly they are both more accurate and faster
than prior models, and outperform the state-of-the-art results on multiple
datasets we test, including HMDB, Kinetics, and Moments in Time. We will open
source the code and models, to encourage future model development.

基于进化搜索算法的视频 CNN 结构自动探索方法可以在较短的时间范围内自动地发现新的、更好的视频体系结构，实现了空间和时间交互作用的学习，并提出了一种称为 iTGM 层的组件，可以更加有效地使用其参数，允许学习更长时间范围的空间 - 时间交互作用，并在多个测试数据集上表现出比现有模型更准确、更快的性能。