ArticleSensors (Basel, Switzerland)2018
Action Recognition by an Attention-Aware Temporal Weighted Convolutional Neural Network.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- TriQuery: A Query-Based Model for Surgical Triplet Recognition.Sensors (Basel, Switzerland) · 2025Article
- 3DMesh-GAR: 3D Human Body Mesh-Based Method for Group Activity Recognition.Sensors (Basel, Switzerland) · 2022Article
- Improved Deep CNN with Parameter Initialization for Data Analysis of Near-Infrared Spectroscopy Sensors.Sensors (Basel, Switzerland) · 2020Article
- Fusion of Video and Inertial Sensing for Deep Learning-Based Human Action Recognition.Sensors (Basel, Switzerland) · 2019Article
- Feature Fusion of Deep Spatial Features and Handcrafted Spatiotemporal Features for Human Action Recognition.Sensors (Basel, Switzerland) · 2019Article
- Improved Convolutional Pose Machines for Human Pose Estimation Using Image Sensor Data.Sensors (Basel, Switzerland) · 2019Article
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Authors and funding
6 authors.
Funding
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Abstract
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-aware Temporal Weighted CNN (ATW CNN) for action recognition in videos, 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 CNN framework is capable of end-to-end training, with both network parameters and temporal weights optimized by stochastic gradient descent (SGD) with back-propagation. Our experimental results on the UCF-101 and HMDB-51 datasets showed that the proposed attention mechanism contributes substantially to the performance gains with the more discriminative snippets by focusing on more relevant video segments.
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