We propose and demonstrate a representation learning approach by maximizing the mutual information between local features of images and text. The goal of this approach is to learn useful image representations by taking advantage of the rich information contained in the free text that describes the findings in the image. Our method trains image and text encoders by encouraging the resulting representations to exhibit high local mutual information. We make use of recent advances in mutual information estimation with neural network discriminators. We argue that the sum of local mutual information is typically a lower bound on the global mutual information. Our experimental results in the downstream image classification tasks demonstrate the advantages of using local features for image-text representation learning.
@article{arxiv.2103.04537,
title = {Multimodal Representation Learning via Maximization of Local Mutual Information},
author = {Ruizhi Liao and Daniel Moyer and Miriam Cha and Keegan Quigley and Seth Berkowitz and Steven Horng and Polina Golland and William M. Wells},
journal= {arXiv preprint arXiv:2103.04537},
year = {2021}
}
Comments
In Proceedings of International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), 2021