Summary of Spatio-temporal Fuzzy-oriented Multi-modal Meta-learning For Fine-grained Emotion Recognition, by Jingyao Wang et al.
Spatio-Temporal Fuzzy-oriented Multi-Modal Meta-Learning for Fine-grained Emotion Recognition
by Jingyao Wang, Yuxuan Yang, Wenwen Qiang, Changwen Zheng, Hui Xiong
First submitted to arxiv on: 18 Dec 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG); Neural and Evolutionary Computing (cs.NE)
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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 A novel Spatio-Temporal Fuzzy-oriented Multi-modal Meta-learning framework (ST-F2M) is proposed for fine-grained emotion recognition (FER), tackling challenges in real-world applications. Existing methods rely on large amounts of annotated data, assume temporal correlation within sampling periods, and neglect spatial heterogeneity. ST-F2M addresses these limitations by dividing multi-modal videos into views, using integrated modules with spatial and temporal convolutions to encode data, and adding fuzzy semantic information based on generalized rules. The framework learns emotion-related general meta-knowledge through meta-recurrent neural networks for fast and robust FER. Experimental results demonstrate that ST-F2M outperforms state-of-the-art methods in terms of accuracy and model efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Fine-grained emotion recognition plays a crucial role in various fields, including disease diagnosis and personalized recommendations. However, current methods face three key challenges: they require large amounts of annotated data, cannot capture changing emotion patterns, and neglect different FER scenarios. To address these issues, researchers propose a new framework called ST-F2M. This framework divides multi-modal videos into views, encodes each view with spatial and temporal convolutions, and adds fuzzy semantic information to handle emotions’ complexity. The framework learns general knowledge for fast and accurate emotion recognition. Experimental results show that ST-F2M performs better than other methods in terms of accuracy and efficiency. |
Keywords
» Artificial intelligence » Meta learning » Multi modal
