论文标题
活动活动：一种深层的多模式融合体系结构，用于活动识别
EmbraceNet for Activity: A Deep Multimodal Fusion Architecture for Activity Recognition
论文作者
论文摘要
近几十年来，使用多个传感器的人类活动识别是一项具有挑战性但有前途的任务。在本文中，我们提出了一个基于最近提出的特征融合体系结构的深层多模式融合模型，以识别embraceNet。我们的模型独立处理每个传感器数据，将功能与EmbraceNet架构结合在一起，并后处理融合功能以预测活动。此外，我们提出了其他过程来提高模型的性能。我们将从我们提出的模型获得的结果提交给SHL识别挑战，并以团队名称“ Yonsei-MCML”。
Human activity recognition using multiple sensors is a challenging but promising task in recent decades. In this paper, we propose a deep multimodal fusion model for activity recognition based on the recently proposed feature fusion architecture named EmbraceNet. Our model processes each sensor data independently, combines the features with the EmbraceNet architecture, and post-processes the fused feature to predict the activity. In addition, we propose additional processes to boost the performance of our model. We submit the results obtained from our proposed model to the SHL recognition challenge with the team name "Yonsei-MCML."
