  Uncertainty estimation is an important research area to make deep neural
networks (DNNs) more trustworthy. While extensive research on uncertainty
estimation has been conducted with unimodal data, uncertainty estimation for
multimodal data remains a challenge. Neural processes (NPs) have been
demonstrated to be an effective uncertainty estimation method for unimodal data
by providing the reliability of Gaussian processes with efficient and powerful
DNNs. While NPs hold significant potential for multimodal uncertainty
estimation, the adaptation of NPs for multimodal data has not been carefully
studied. To bridge this gap, we propose Multimodal Neural Processes (MNPs) by
generalising NPs for multimodal uncertainty estimation. Based on the framework
of NPs, MNPs consist of several novel and principled mechanisms tailored to the
characteristics of multimodal data. In extensive empirical evaluation, our
method achieves state-of-the-art multimodal uncertainty estimation performance,
showing its appealing robustness against noisy samples and reliability in
out-of-distribution detection with faster computation time compared to the
current state-of-the-art multimodal uncertainty estimation method.
