Using Multiple Instance Learning to Build Multimodal Representations

• URL: http://arxiv.org/abs/2212.05561v1
• Date: Sun, 11 Dec 2022 18:01:11 GMT
• Title: Using Multiple Instance Learning to Build Multimodal Representations
• Authors: Peiqi Wang, William M. Wells, Seth Berkowitz, Steven Horng, Polina Golland
• Abstract summary: Image-text multimodal representation learning aligns data across modalities and enables important medical applications. We propose a generic framework for constructing permutation-invariant score functions with many existing multimodal representation learning approaches as special cases.
• Score: 3.354271620160378
• License: http://creativecommons.org/licenses/by/4.0/
• 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 on a number of downstream tasks.

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