This paper introduces the M&M model, a novel multimodal-multitask learning framework, applied to the AVCAffe dataset for cognitive load assessment (CLA). M&M uniquely integrates audiovisual cues through a dual-pathway architecture, featuring specialized streams for audio and video inputs. A key innovation lies in its cross-modality multihead attention mechanism, fusing the different modalities for synchronized multitasking. Another notable feature is the model's three specialized branches, each tailored to a specific cognitive load label, enabling nuanced, task-specific analysis. While it shows modest performance compared to the AVCAffe's single-task baseline, M\&M demonstrates a promising framework for integrated multimodal processing. This work paves the way for future enhancements in multimodal-multitask learning systems, emphasizing the fusion of diverse data types for complex task handling.
@article{arxiv.2403.09451,
title = {M&M: Multimodal-Multitask Model Integrating Audiovisual Cues in Cognitive Load Assessment},
author = {Long Nguyen-Phuoc and Renald Gaboriau and Dimitri Delacroix and Laurent Navarro},
journal= {arXiv preprint arXiv:2403.09451},
year = {2024}
}