Summary of Temporal Label Hierachical Network For Compound Emotion Recognition, by Sunan Li and Hailun Lian and Cheng Lu and Yan Zhao and Tianhua Qi and Hao Yang and Yuan Zong and Wenming Zheng
Temporal Label Hierachical Network for Compound Emotion Recognition
by Sunan Li, Hailun Lian, Cheng Lu, Yan Zhao, Tianhua Qi, Hao Yang, Yuan Zong, Wenming Zheng
First submitted to arxiv on: 17 Jul 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 advancements in recognizing compound emotions that commonly occur in practical applications. Existing methods struggle with this task despite progress in recognizing seven basic emotions. The authors participated in the 7th Field Emotion Behavior Analysis (ABAW) competition, leveraging pre-trained ResNet18 and Transformer networks as a foundation. To account for emotion continuity over time, they propose a time pyramid structure network for frame-level emotion prediction. Additionally, they address data scarcity in composite emotion recognition by utilizing fine-grained labels from the DFEW database to construct training data for emotion categories. The authors develop a classification framework that transitions from coarse to fine label spaces, taking into account valence and arousal characteristics of various complex emotions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps us better recognize emotions in real-life situations. Emotions are complex, and we often feel multiple emotions at once. The researchers developed new ways to predict these mixed emotions using computer networks called ResNet18 and Transformer. They also created a special network that looks at how emotions change over time. To improve their method, they used more detailed labels from the DFEW database to train their system. This paper is important because it can help us better understand people’s emotions and develop systems that can recognize emotions in everyday life. |
Keywords
» Artificial intelligence » Classification » Transformer
