Abhinav Dhall, Vrushank Ahire, Kunal Shah, Lownish Rai Sookha, Nikhil Pakhale, M. A. Ganaie, Mudasir Nazir Khan
We have not lifted any functions out of this paper's repositories yet, so there is nothing we have run. If it links a repository, it is listed below.
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
The same record, over MCP at https://syntology.ai/mcp:
get_harvested_code_for_paper("2503.12623")
get_code_for_paper("2503.12623")
have("2503.12623")
Connect an agent — have() is free.