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

Author:

Long Nguyen-Phuoc, Renald Gaboriau, Dimitri Delacroix, Laurent Navarro

Keyword:

Computer Science, Computer Vision and Pattern Recognition, Computer Vision and Pattern Recognition (cs.CV), Multimedia (cs.MM), Sound (cs.SD), Audio and Speech Processing (eess.AS)

journal:

Proceedings of the 19th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications - Volume 2 VISAPP: VISAPP, 869-876, 2024 , Rome, Italy

date:

2024-03-14 00:00:00

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.

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