Using Multiple Instance Learning To Build Multimodal Representations
Peiqi Wang, William M. Wells, Seth Berkowitz, Steven Horng, Polina Golland . Arxiv 2022 – 0 citations
[Paper]
Image-text multimodal representation learning aligns data across modalities and enables important medical applications, e.g., image classification, visual grounding, and cross-modal retrieval. In this work, we establish a connection between multimodal representation learning and multiple instance learning. Based on this connection, we propose a generic framework for constructing permutation-invariant score functions with many existing multimodal representation learning approaches as special cases. Furthermore, we use the framework to derive a novel contrastive learning approach and demonstrate that our method achieves state-of-the-art results in several downstream tasks.