Action recognition using attention-based spatio-temporal VLAD networks and adaptive video sequences optimization.

In the field of human action recognition, it is a long-standing challenge to characterize the video-level spatio-temporal features effectively. This is attributable in part to the inability of CNN to model long-range temporal information, especially for actions that consist of multiple staged behaviors. In this paper, a novel attention-based spatio-temporal VLAD network (AST-VLAD) with self-attention model is developed to aggregate the informative deep features across the video according to the adaptive deep feature selected.