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

• URL: http://arxiv.org/abs/2503.12623v1
• Date: Sun, 16 Mar 2025 19:32:32 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: MAVEN is a novel architecture for dynamic emotion recognition through dimensional modeling of affect.<n>Our approach employs modality-specific encoders to extract rich feature representations from synchronized video frames, audio segments, and transcripts.<n>MAVEN predicts emotions in a polar coordinate form, aligning with psychological models of the emotion circumplex.
• Score: 6.304608172789466
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This paper introduces MAVEN (Multi-modal Attention for Valence-Arousal Emotion Network), a novel architecture for dynamic emotion recognition through dimensional modeling of affect. The model uniquely integrates visual, audio, and textual modalities via a bi-directional cross-modal attention mechanism with six distinct attention pathways, enabling comprehensive interactions between all modality pairs. Our proposed approach employs modality-specific encoders to extract rich feature representations from synchronized video frames, audio segments, and transcripts. The architecture's novelty lies in its cross-modal enhancement strategy, where each modality representation is refined through weighted attention from other modalities, followed by self-attention refinement through modality-specific encoders. Rather than directly predicting valence-arousal values, MAVEN predicts emotions in a polar coordinate form, aligning with psychological models of the emotion circumplex. Experimental evaluation on the Aff-Wild2 dataset demonstrates the effectiveness of our approach, with performance measured using Concordance Correlation Coefficient (CCC). The multi-stage architecture demonstrates superior ability to capture the complex, nuanced nature of emotional expressions in conversational videos, advancing the state-of-the-art (SOTA) in continuous emotion recognition in-the-wild. Code can be found at: https://github.com/Vrushank-Ahire/MAVEN_8th_ABAW.

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