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
@article{arxiv.2503.12623,
title = {MAVEN: Multi-modal Attention for Valence-Arousal Emotion Network},
author = {Vrushank Ahire and Kunal Shah and Mudasir Nazir Khan and Nikhil Pakhale and Lownish Rai Sookha and M. A. Ganaie and Abhinav Dhall},
journal= {arXiv preprint arXiv:2503.12623},
year = {2025}
}