Uncertainty-aware Multi-modal Learning via Cross-modal Random Network Prediction

TL;DR

This paper introduces CRNP, a novel uncertainty estimation method for multi-modal learning that improves prediction accuracy by effectively combining modalities across various tasks.

Contribution

It presents the first use of random network prediction for uncertainty estimation and multi-modal data fusion, with a stable and adaptable training process.

Findings

Effective on 3D medical image segmentation tasks

Improves robustness in multi-modal classification

Demonstrates adaptability across different tasks

Abstract

Multi-modal learning focuses on training models by equally combining multiple input data modalities during the prediction process. However, this equal combination can be detrimental to the prediction accuracy because different modalities are usually accompanied by varying levels of uncertainty. Using such uncertainty to combine modalities has been studied by a couple of approaches, but with limited success because these approaches are either designed to deal with specific classification or segmentation problems and cannot be easily translated into other tasks, or suffer from numerical instabilities. In this paper, we propose a new Uncertainty-aware Multi-modal Learner that estimates uncertainty by measuring feature density via Cross-modal Random Network Prediction (CRNP). CRNP is designed to require little adaptation to translate between different prediction tasks, while having a stable training process. From a technical point of view, CRNP is the first approach to explore random network prediction to estimate uncertainty and to combine multi-modal data. Experiments on two 3D multi-modal medical image segmentation tasks and three 2D multi-modal computer vision classification tasks show the effectiveness, adaptability and robustness of CRNP. Also, we provide an extensive discussion on different fusion functions and visualization to validate the proposed model.