Hierarchical Attention Network for Action Recognition in Videos

TL;DR

This paper introduces a Hierarchical Attention Network that effectively captures long-range temporal structures and important spatial regions in videos, significantly improving action recognition performance on standard benchmarks.

Contribution

The novel Hierarchical Attention Network integrates static, short-term, and long-term information with attention mechanisms for enhanced video action understanding.

Findings

Outperforms state-of-the-art on UCF-101

Outperforms state-of-the-art on HMDB-51

Efficiently models long-range temporal dependencies

Abstract

Understanding human actions in wild videos is an important task with a broad range of applications. In this paper we propose a novel approach named Hierarchical Attention Network (HAN), which enables to incorporate static spatial information, short-term motion information and long-term video temporal structures for complex human action understanding. Compared to recent convolutional neural network based approaches, HAN has following advantages (1) HAN can efficiently capture video temporal structures in a longer range; (2) HAN is able to reveal temporal transitions between frame chunks with different time steps, i.e. it explicitly models the temporal transitions between frames as well as video segments and (3) with a multiple step spatial temporal attention mechanism, HAN automatically learns important regions in video frames and temporal segments in the video. The proposed model is trained and evaluated on the standard video action benchmarks, i.e., UCF-101 and HMDB-51, and it significantly outperforms the state-of-the arts