Two-stream Convolutional Networks (ConvNets) have shown strong performance for human action recognition in videos. Recently, Residual Networks (ResNets) have arisen as a new technique to train extremely deep architectures. In this paper, we introduce spatiotemporal ResNets as a combination of these two approaches.

This paper presents a framework that improves two stream networks for video action recognition by extending residual network to combine information from two streams into one single network. It significantly improves over previous state-of-the-art on two popular video action recognition benchmark. The downside of this paper is the limited novelty. There are previous work tried to combine two streams into a single network [1,2], and the temporal convolution is not new either [3]. Although the way to combine two streams is slightly different from previous work, the proposed approach is still pretty straightforward.

Two-stream Convolutional Networks (ConvNets) have shown strong performance for human action recognition in videos. Recently, Residual Networks (ResNets) have arisen as a new technique to train extremely deep architectures. In this paper, we introduce spatiotemporal ResNets as a combination of these two approaches. First, we inject residual connections between the appearance and motion pathways of a two-stream architecture to allow spatiotemporal interaction between the two streams. Second, we transform pretrained image ConvNets into spatiotemporal networks by equipping these with learnable convolutional filters that are initialized as temporal residual connections and operate on adjacent feature maps in time.