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arXiv · 2212.05561

Using Multiple Instance Learning to Build Multimodal Representations

Peiqi Wang·William M. Wells·Seth Berkowitz·Steven Horng·Polina Golland
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Abstract

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.

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Peiqi Wang, William M. Wells, Seth Berkowitz, Steven Horng, Polina Golland. 2022-12-11. Using Multiple Instance Learning to Build Multimodal Representations. https://doi.org/10.1007/978-3-031-34048-2_35

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