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Two-Level Attention Module Based on Spurious-3D Residual Networks for Human Action Recognition
Bo Chen1,2, Fangzhou Meng1,2, Hongying Tang1
1Science and Technology on Microsystem Laboratory, Shanghai Institute of Microsystem and Information Technology, Chinese Academy of Sciences, Shanghai 201800, China.
Sensors (Basel, Switzerland)
|February 11, 2023
Summary
This study introduces an improved deep learning model for video action recognition. By using attention modules, the model effectively identifies important frames and spatial regions, enhancing spatiotemporal feature extraction for better accuracy.
Area of Science:
- Computer Science
- Artificial Intelligence
- Machine Learning
Background:
- Deep learning models are advanced in video action recognition.
- Current models often overlook frame and spatial importance, hindering spatiotemporal feature extraction.
Purpose of the Study:
- To propose an improved deep learning method for video action recognition.
- To enhance spatiotemporal feature extraction by emphasizing critical frames and regions.
Main Methods:
- Developed a novel action recognition method using improved residual convolutional neural networks (CNNs).
- Incorporated video frame and spatial attention modules to guide feature emphasis.
- Utilized a two-level attention mechanism to focus on temporal and spatial dimensions.
Main Results:
- The proposed network effectively highlights important video frames and spatial regions.
- Attention modules achieve emphasis with minimal computational overhead.
- Demonstrated strong performance on the UCF-101 and HMDB-51 datasets.
Conclusions:
- The developed attention-based CNN method significantly improves video action recognition.
- The approach enhances the model's ability to extract relevant spatiotemporal features.
- This method offers a more effective way to process video data for action recognition tasks.
