Summary of Emotion Recognition Using Transformers with Masked Learning, by Seongjae Min et al.
Emotion Recognition Using Transformers with Masked Learning
by Seongjae Min, Junseok Yang, Sangjun Lim, Junyong Lee, Sangwon Lee, Sejoon Lim
First submitted to arxiv on: 19 Mar 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 A novel Transformer-based framework is proposed to estimate Valence-Arousal (VA) from facial expressions, recognizing various emotions and detecting Action Units (AU). Building on recent advancements in deep learning for human emotion analysis, this study leverages Vision Transformer (ViT) and Transformer models to surpass traditional Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) methods. The framework introduces a random frame masking technique and Focal loss adapted for imbalanced data, enhancing accuracy and applicability in real-world settings. This approach contributes to the advancement of emotional computing and deep learning methodologies. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This study uses special computer models to understand how people feel and behave. It’s like training a super smart AI to read emotions from faces! They use two types of Transformer models, called Vision Transformer (ViT) and Transformer, which are better than old methods that used other kinds of models. To make it even better, they come up with new tricks: hiding some parts of the face and adjusting how the model learns from the data. This helps the AI get really good at understanding emotions in real-life situations. |
Keywords
» Artificial intelligence » Deep learning » Lstm » Transformer » Vision transformer » Vit
