MAVEN: Multi-modal Attention for Valence-Arousal Emotion Network

• URL: http://arxiv.org/abs/2503.12623v2
• Date: Fri, 02 May 2025 07:17:44 GMT
• Title: MAVEN: Multi-modal Attention for Valence-Arousal Emotion Network
• Authors: Vrushank Ahire, Kunal Shah, Mudasir Nazir Khan, Nikhil Pakhale, Lownish Rai Sookha, M. A. Ganaie, Abhinav Dhall,
• Abstract summary: The proposed Multi-modal Attention for Valence-Arousal Emotion Network (MAVEN) integrates visual, audio, and textual modalities.<n>MAVEN uses modality-specific encoders to extract features from synchronized video frames, audio segments, and transcripts.<n>The architecture captures the subtle and transient nature of emotional expressions in conversational videos and improves emotion recognition in real-world situations.
• Score: 6.304608172789466
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Dynamic emotion recognition in the wild remains challenging due to the transient nature of emotional expressions and temporal misalignment of multi-modal cues. Traditional approaches predict valence and arousal and often overlook the inherent correlation between these two dimensions. The proposed Multi-modal Attention for Valence-Arousal Emotion Network (MAVEN) integrates visual, audio, and textual modalities through a bi-directional cross-modal attention mechanism. MAVEN uses modality-specific encoders to extract features from synchronized video frames, audio segments, and transcripts, predicting emotions in polar coordinates following Russell's circumplex model. The evaluation of the Aff-Wild2 dataset using MAVEN achieved a concordance correlation coefficient (CCC) of 0.3061, surpassing the ResNet-50 baseline model with a CCC of 0.22. The multistage architecture captures the subtle and transient nature of emotional expressions in conversational videos and improves emotion recognition in real-world situations. The code is available at: https://github.com/Vrushank-Ahire/MAVEN_8th_ABAW

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