M&M：多模态-多任务模型整合视听线索用于认知负荷评估
M&M: Multimodal-Multitask Model Integrating Audiovisual Cues in Cognitive Load Assessment
AI总结：
本文提出M&M多模态-多任务模型，通过双路径架构和跨模态多头注意力整合视听线索，用于认知负荷评估，虽性能适中但为集成多模态处理提供新框架。
摘要：
本文介绍了M&M模型，一种新颖的多模态-多任务学习框架，应用于AVCAffe数据集进行认知负荷评估（CLA）。M&M通过双路径架构独特地整合了视听线索，具有针对音频和视频输入的专门流。一个关键创新在于其跨模态多头注意力机制，融合不同模态以实现同步多任务处理。另一个显著特点是模型的三个专门分支，每个分支针对特定的认知负荷标签，实现细致、任务特定的分析。尽管与AVCAffe的单任务基线相比性能适中，M&M展示了集成多模态处理的有前景框架。这项工作为多模态-多任务学习系统的未来增强铺平了道路，强调了融合多样数据类型以处理复杂任务。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
