Milmer: a Framework for Multiple Instance Learning based Multimodal Emotion Recognition

• URL: http://arxiv.org/abs/2502.00547v1
• Date: Sat, 01 Feb 2025 20:32:57 GMT
• Title: Milmer: a Framework for Multiple Instance Learning based Multimodal Emotion Recognition
• Authors: Zaitian Wang, Jian He, Yu Liang, Xiyuan Hu, Tianhao Peng, Kaixin Wang, Jiakai Wang, Chenlong Zhang, Weili Zhang, Shuang Niu, Xiaoyang Xie,
• Abstract summary: This study addresses the challenges of emotion recognition by integrating facial expression analysis with electroencephalogram (EEG) signals. The proposed framework employs a transformer-based fusion approach to effectively integrate visual and physiological modalities. A key innovation of this work is the adoption of a multiple instance learning (MIL) approach, which extracts meaningful information from multiple facial expression images.
• Score: 16.616341358877243
• License:
• Abstract: Emotions play a crucial role in human behavior and decision-making, making emotion recognition a key area of interest in human-computer interaction (HCI). This study addresses the challenges of emotion recognition by integrating facial expression analysis with electroencephalogram (EEG) signals, introducing a novel multimodal framework-Milmer. The proposed framework employs a transformer-based fusion approach to effectively integrate visual and physiological modalities. It consists of an EEG preprocessing module, a facial feature extraction and balancing module, and a cross-modal fusion module. To enhance visual feature extraction, we fine-tune a pre-trained Swin Transformer on emotion-related datasets. Additionally, a cross-attention mechanism is introduced to balance token representation across modalities, ensuring effective feature integration. A key innovation of this work is the adoption of a multiple instance learning (MIL) approach, which extracts meaningful information from multiple facial expression images over time, capturing critical temporal dynamics often overlooked in previous studies. Extensive experiments conducted on the DEAP dataset demonstrate the superiority of the proposed framework, achieving a classification accuracy of 96.72% in the four-class emotion recognition task. Ablation studies further validate the contributions of each module, highlighting the significance of advanced feature extraction and fusion strategies in enhancing emotion recognition performance. Our code are available at https://github.com/liangyubuaa/Milmer.

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