论文标题
基于全球工作空间理论的多模式数据融合
Multimodal Data Fusion based on the Global Workspace Theory
论文作者
论文摘要
我们提出了一种新颖的神经网络体系结构，称为全球工作区网络（GWN），该架构解决了多模式数据融合中动态和未指定不确定性的挑战。我们的GWN是跨时间和随时间发展的一种关注模型，并受到认知科学领域良好的全球工作空间理论的启发。 GWN的平均F1得分为疼痛患者和健康参与者的歧视得分为0.92，F1得分的平均F1得分= 0.75，用于对患者进行三个疼痛水平的进一步分类，这都是基于从患有慢性疼痛的人中捕获的多模态emopain数据集，而健康的人在无约束的设置中执行不同类型的运动运动。在这些任务中，GWN显着优于通过串联合并的典型融合方法。我们进一步提供了GWN的行为及其在多模式数据中解决不确定性（隐藏噪声）的能力的广泛分析。
We propose a novel neural network architecture, named the Global Workspace Network (GWN), which addresses the challenge of dynamic and unspecified uncertainties in multimodal data fusion. Our GWN is a model of attention across modalities and evolving through time, and is inspired by the well-established Global Workspace Theory from the field of cognitive science. The GWN achieved average F1 score of 0.92 for discrimination between pain patients and healthy participants and average F1 score = 0.75 for further classification of three pain levels for a patient, both based on the multimodal EmoPain dataset captured from people with chronic pain and healthy people performing different types of exercise movements in unconstrained settings. In these tasks, the GWN significantly outperforms the typical fusion approach of merging by concatenation. We further provide extensive analysis of the behaviour of the GWN and its ability to address uncertainties (hidden noise) in multimodal data.
