Summary of Exploring Facial Expression Recognition Through Semi-supervised Pretraining and Temporal Modeling, by Jun Yu et al.
Exploring Facial Expression Recognition through Semi-Supervised Pretraining and Temporal Modeling
by Jun Yu, Zhihong Wei, Zhongpeng Cai, Gongpeng Zhao, Zerui Zhang, Yongqi Wang, Guochen Xie, Jichao Zhu, Wangyuan Zhu
First submitted to arxiv on: 18 Mar 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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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 The paper presents an approach for the facial expression recognition task in the upcoming 6th Affective Behavior Analysis in-the-Wild (ABAW) competition. The limited size of the Facial Expression Recognition (FER) dataset hinders model generalization, leading to subpar recognition performance. To address this issue, the authors employ a semi-supervised learning technique generating pseudo-labels for unlabeled face data and implement debiased feedback learning to handle category imbalance in the dataset. Additionally, they introduce a Temporal Encoder to capture temporal relationships between neighboring expression image features. The proposed method achieves outstanding results on the official validation set, confirming its effectiveness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about how computers can recognize facial expressions. This is important because it helps us understand and analyze human emotions. Right now, there’s a problem with recognizing facial expressions from small datasets. To solve this issue, researchers used special learning techniques to generate labels for unlabeled face data. They also made sure the dataset was balanced and not biased. To make it even better, they added an extra step to capture how expressions change over time. The results are really good! |
Keywords
» Artificial intelligence » Encoder » Generalization » Semi supervised
