LANISTR: Multimodal Learning from Structured and Unstructured Data

TL;DR

LANISTR is a novel attention-based framework that effectively learns from multimodal data including language, images, and structured data, demonstrating significant improvements in real-world tasks with missing modalities.

Contribution

It introduces a masking-based training method and a similarity-based loss for cross-modal learning from large-scale multimodal data with missing modalities.

Findings

Achieves 6.6% AUROC improvement on healthcare data

Achieves 14% accuracy improvement on retail data

Robust to high ratios of missing modality samples

Abstract

Multimodal large-scale pretraining has shown impressive performance for unstructured data such as language and image. However, a prevalent real-world scenario involves structured data types, tabular and time-series, along with unstructured data. Such scenarios have been understudied. To bridge this gap, we propose LANISTR, an attention-based framework to learn from LANguage, Image, and STRuctured data. The core of LANISTR's methodology is rooted in \textit{masking-based} training applied across both unimodal and multimodal levels. In particular, we introduce a new similarity-based multimodal masking loss that enables it to learn cross-modal relations from large-scale multimodal data with missing modalities. On two real-world datasets, MIMIC-IV (from healthcare) and Amazon Product Review (from retail), LANISTR demonstrates remarkable improvements, 6.6\% (in AUROC) and 14\% (in accuracy) when fine-tuned with 0.1\% and 0.01\% of labeled data, respectively, compared to the state-of-the-art alternatives. Notably, these improvements are observed even with very high ratio of samples (35.7\% and 99.8\% respectively) not containing all modalities, underlining the robustness of LANISTR to practical missing modality challenge. Our code and models will be available at https://github.com/google-research/lanistr