Hierarchical Attention Network for Action Recognition in Videos
arXiv:1607.06416
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
References in corpus (6)
- Very Deep Convolutional Networks for Large-Scale Image Recognition
- ADADELTA: An Adaptive Learning Rate Method
- UCF101: A Dataset of 101 Human Actions Classes From Videos in The Wild
- Caffe: Convolutional Architecture for Fast Feature Embedding
- Recurrent Neural Network Regularization
- DRAW: A Recurrent Neural Network For Image Generation
Cited by in corpus (6)
- A General Survey on Attention Mechanisms in Deep Learning
- SR-LSTM: State Refinement for LSTM towards Pedestrian Trajectory Prediction
- Video-based Human Action Recognition using Deep Learning: A Review
- Hierarchical Multi-scale Attention Networks for Action Recognition
- DAVID: Dual-Attentional Video Deblurring
- Learning Comprehensive Motion Representation for Action Recognition