Evolving Space-Time Neural Architectures for Videos

Understand

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.