M&M: Multimodal-Multitask Model Integrating Audiovisual Cues in Cognitive Load Assessment

• URL: http://arxiv.org/abs/2403.09451v1
• Date: Thu, 14 Mar 2024 14:49:40 GMT
• Title: M&M: Multimodal-Multitask Model Integrating Audiovisual Cues in Cognitive Load Assessment
• Authors: Long Nguyen-Phuoc, Renald Gaboriau, Dimitri Delacroix, Laurent Navarro,
• Abstract summary: This paper introduces the M&M model, a novel multimodal-multitask learning framework, applied to the AVCAffe dataset for cognitive load assessment. 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.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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