Hierarchical Multi-scale Attention Networks for Action Recognition

TL;DR

This paper introduces a Hierarchical Multi-scale Attention Network (HM-AN) that combines hierarchical RNNs and attention mechanisms with Gumbel-softmax for improved action recognition in videos.

Contribution

It proposes a novel HM-AN model integrating hierarchical RNNs and attention with a new gradient estimation method, enhancing action recognition performance.

Findings

HM-AN outperforms LSTM with attention on vision tasks.

The model effectively captures hierarchical temporal structures.

Attention regions and hierarchical features are visualized successfully.

Abstract

Recurrent Neural Networks (RNNs) have been widely used in natural language processing and computer vision. Among them, the Hierarchical Multi-scale RNN (HM-RNN), a kind of multi-scale hierarchical RNN proposed recently, can learn the hierarchical temporal structure from data automatically. In this paper, we extend the work to solve the computer vision task of action recognition. However, in sequence-to-sequence models like RNN, it is normally very hard to discover the relationships between inputs and outputs given static inputs. As a solution, attention mechanism could be applied to extract the relevant information from input thus facilitating the modeling of input-output relationships. Based on these considerations, we propose a novel attention network, namely Hierarchical Multi-scale Attention Network (HM-AN), by combining the HM-RNN and the attention mechanism and apply it to action recognition. A newly proposed gradient estimation method for stochastic neurons, namely Gumbel-softmax, is exploited to implement the temporal boundary detectors and the stochastic hard attention mechanism. To amealiate the negative effect of sensitive temperature of the Gumbel-softmax, an adaptive temperature training method is applied to better the system performance. The experimental results demonstrate the improved effect of HM-AN over LSTM with attention on the vision task. Through visualization of what have been learnt by the networks, it can be observed that both the attention regions of images and the hierarchical temporal structure can be captured by HM-AN.