Mutual Information Analysis in Multimodal Learning Systems
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.
Code (0)
등록된 구현이 없습니다.
Tasks
3D Object DetectionAutonomous DrivingAutonomous Vehiclesobject-detectionObject DetectionSimilar Papers 제목 키워드 기반
Multimodal Representations Learning Based on Mutual Information Maximization and Minimization and Identity Embedding for Multimodal Sentiment Analysis
Multimodal sentiment analysis (MSA) is a fundamental complex research problem due to the heterogeneity gap between different modalities and the ambiguity of human emotional expression. Although there have been many succe…
Multimodal Sentiment AnalysisSentiment AnalysisA Mutual Information Lower Bound for Multimodal Regression Active Learning
Active learning for continuous regression has lacked an acquisition function that targets epistemic uncertainty when the predictive distribution is multimodal: variance misses modal disagreement, and information-theoreti…
Active LearningImproving Multimodal Fusion with Hierarchical Mutual Information Maximization for Multimodal Sentiment Analysis
In multimodal sentiment analysis (MSA), the performance of a model highly depends on the quality of synthesized embeddings. These embeddings are generated from the upstream process called multimodal fusion, which aims to…
Multimodal Sentiment AnalysisSentiment AnalysisDenoising Bottleneck with Mutual Information Maximization for Video Multimodal Fusion
Video multimodal fusion aims to integrate multimodal signals in videos, such as visual, audio and text, to make a complementary prediction with multiple modalities contents. However, unlike other image-text multimodal ta…
DenoisingMultimodal Sentiment AnalysisSentiment AnalysisBalanced Multimodal Learning via Mutual Information
Multimodal learning has increasingly become a focal point in research, primarily due to its ability to integrate complementary information from diverse modalities. Nevertheless, modality imbalance, stemming from factors …
Knowledge Distillation