Summary of Emotion Recognition with Facial Attention and Objective Activation Functions, by Andrzej Miskow and Abdulrahman Altahhan
Emotion Recognition with Facial Attention and Objective Activation Functions
by Andrzej Miskow, Abdulrahman Altahhan
First submitted to arxiv on: 23 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper explores the impact of introducing channel and spatial attention mechanisms, including SEN-Net, ECA-Net, and CBAM, to existing CNN-based vision models like VGGNet, ResNet, and ResNetV2 for Facial Emotion Recognition tasks. It reveals that not only can attention enhance model performance but also combining it with a different activation function can lead to further improvements. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper looks at how adding channel and spatial attention helps existing computer vision models recognize facial emotions better. By testing different attention mechanisms like SEN-Net, ECA-Net, and CBAM, the study shows that using attention makes these models work better. It also finds that combining attention with a new way of calculating activation functions can make them even better at recognizing emotions. |
Keywords
» Artificial intelligence » Attention » Cnn » Resnet
