多模态学习系统中的互信息分析
Mutual Information Analysis in Multimodal Learning Systems
AI总结：
针对多模态学习系统中模态关系影响任务性能的问题，研究人员借助互信息概念开发InfoMeter系统估计模态间MI，实验发现模态间低MI可提升自动驾驶三维目标检测准确率，为多模态系统开发提供新方向。
摘要：
近年来，多模态信号处理与分析的应用大幅增长，这主要得益于多模态数据集的日益普及以及多模态学习系统的快速发展。知名应用案例包括自动驾驶汽车、音视频生成系统、视觉-语言系统等。这类系统整合文本、语音、图像、视频、激光雷达（LiDAR）等多种信号模态以执行各类任务。理解这类系统的一个关键问题是不同模态之间的关系及其对任务性能的影响。本文中，我们利用互信息（mutual information, MI）的概念来深入探究这一问题。借助熵建模与估计领域的最新进展，我们开发了名为InfoMeter的系统，用于估计多模态学习系统中各模态之间的互信息。随后，我们将InfoMeter应用于分析一个基于大规模自动驾驶数据集的多模态三维目标检测系统。针对该系统的实验表明，模态间较低的互信息有助于提升检测准确率。这一新见解或能推动未来多模态学习系统的开发改进。
英文摘要：
In recent years, there has been a significant increase in applications of multimodal signal processing and analysis, largely driven by the increased availability of multimodal datasets and the rapid progress in multimodal learning systems. Well-known examples include autonomous vehicles, audiovisual generative systems, vision-language systems, and so on. Such systems integrate multiple signal modalities: text, speech, images, video, LiDAR, etc., to perform various tasks. A key issue for understanding such systems is the relationship between various modalities and how it impacts task performance. In this paper, we employ the concept of mutual information (MI) to gain insight into this issue. Taking advantage of the recent progress in entropy modeling and estimation, we develop a system called InfoMeter to estimate MI between modalities in a multimodal learning system. We then apply InfoMeter to analyze a multimodal 3D object detection system over a large-scale dataset for autonomous driving. Our experiments on this system suggest that a lower MI between modalities is beneficial for detection accuracy. This new insight may facilitate improvements in the development of future multimodal learning systems.
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。
