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[2602.20530] Memory-guided Prototypical Co-occurrence Learning for Mixed Emotion Recognition
Summary
The paper presents a novel framework, Memory-guided Prototypical Co-occurrence Learning (MPCL), aimed at improving mixed emotion recognition by modeling emotion co-occurrence patterns through multi-modal signal fusion.
Why It Matters
As emotional experiences often involve multiple simultaneous feelings, this research addresses a significant gap in emotion recognition technology. By enhancing the accuracy of mixed emotion detection, the findings could have implications for various applications in affective computing, such as mental health monitoring and human-computer interaction.
Key Takeaways
- MPCL framework effectively models emotion co-occurrence patterns.
- Utilizes a multi-scale associative memory mechanism for signal fusion.
- Introduces emotion-specific prototype memory banks for better representation.
- Demonstrates superior performance over existing methods in mixed emotion recognition.
- Highlights the importance of capturing structured correlations among emotions.
Computer Science > Machine Learning arXiv:2602.20530 (cs) [Submitted on 24 Feb 2026] Title:Memory-guided Prototypical Co-occurrence Learning for Mixed Emotion Recognition Authors:Ming Li, Yong-Jin Liu, Fang Liu, Huankun Sheng, Yeying Fan, Yixiang Wei, Minnan Luo, Weizhan Zhang, Wenping Wang View a PDF of the paper titled Memory-guided Prototypical Co-occurrence Learning for Mixed Emotion Recognition, by Ming Li and 8 other authors View PDF HTML (experimental) Abstract:Emotion recognition from multi-modal physiological and behavioral signals plays a pivotal role in affective computing, yet most existing models remain constrained to the prediction of singular emotions in controlled laboratory settings. Real-world human emotional experiences, by contrast, are often characterized by the simultaneous presence of multiple affective states, spurring recent interest in mixed emotion recognition as an emotion distribution learning problem. Current approaches, however, often neglect the valence consistency and structured correlations inherent among coexisting emotions. To address this limitation, we propose a Memory-guided Prototypical Co-occurrence Learning (MPCL) framework that explicitly models emotion co-occurrence patterns. Specifically, we first fuse multi-modal signals via a multi-scale associative memory mechanism. To capture cross-modal semantic relationships, we construct emotion-specific prototype memory banks, yielding rich physiological and behavioral representations, a...
