# Attention-based Temporal Weighted Convolutional Neural Network for Action Recognition

arXiv: https://arxiv.org/abs/1803.07179 · HTML: https://mlsanity.com/paper/1803.07179/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Mar 2018.

## Built on by (most cited first, 4 of 4)

- [Attention, please! A survey of Neural Attention Models in Deep Learning](https://mlsanity.com/paper/2103.16775/) (2021)
- [Spatio-Temporal Interaction Graph Parsing Networks for Human-Object Interaction Recognition](https://mlsanity.com/paper/2108.08633/) (2021)
- [Word-level Sign Language Recognition with Multi-stream Neural Networks Focusing on Local Regions and Skeletal Information](https://mlsanity.com/paper/2106.15989/) (2021)
- [Event and Activity Recognition in Video Surveillance for Cyber-Physical Systems](https://mlsanity.com/paper/2111.02064/) (2021)

## Abstract (arXiv)

Research in human action recognition has accelerated significantly since the introduction of powerful machine learning tools such as Convolutional Neural Networks (CNNs). However, effective and efficient methods for incorporation of temporal information into CNNs are still being actively explored in the recent literature. Motivated by the popular recurrent attention models in the research area of natural language processing, we propose the Attention-based Temporal Weighted CNN (ATW), which embeds a visual attention model into a temporal weighted multi-stream CNN. This attention model is simply implemented as temporal weighting yet it effectively boosts the recognition performance of video representations. Besides, each stream in the proposed ATW framework is capable of end-to-end training, with both network parameters and temporal weights optimized by stochastic gradient descent (SGD) with backpropagation. Our experiments show that the proposed attention mechanism contributes substantially to the performance gains with the more discriminative snippets by focusing on more relevant video segments.