Summary of Grefel: Geometry-aware Reliable Facial Expression Learning Under Bias and Imbalanced Data Distribution, by Azmine Toushik Wasi and Taki Hasan Rafi and Raima Islam and Karlo Serbetar and Dong Kyu Chae
GReFEL: Geometry-Aware Reliable Facial Expression Learning under Bias and Imbalanced Data Distribution
by Azmine Toushik Wasi, Taki Hasan Rafi, Raima Islam, Karlo Serbetar, Dong Kyu Chae
First submitted to arxiv on: 21 Oct 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary In this paper, researchers tackle the challenge of learning reliable facial expressions (FEL) despite variability in people’s facial structures, movements, tones, and demographics. They introduce GReFEL, a Vision Transformer-based model that incorporates a facial geometry-aware anchor-based reliability balancing module to address imbalanced data distributions, bias, and uncertainty. The approach integrates local and global data with anchors that learn different facial features, adjusting biased emotions caused by intra-class disparity, inter-class similarity, and scale sensitivity. Experimental results demonstrate the outperformance of GReFEL compared to state-of-the-art methodologies on various datasets. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to make computers better at recognizing people’s facial expressions. Right now, computer systems struggle with this task because everyone’s face is different. Some people have more expressive faces, while others are more neutral. The data used to train these systems can also be biased or imbalanced, which means the predictions made by the system might not be accurate or fair. To fix this problem, the researchers created a new model called GReFEL that uses special anchors to help the computer understand different facial features and adjust for any biases in the data. The results show that GReFEL works better than other current approaches. |
Keywords
» Artificial intelligence » Vision transformer
