LANISTR: Multimodal Learning from Structured and Unstructured Data

• URL: http://arxiv.org/abs/2305.16556v3
• Date: Wed, 24 Apr 2024 17:37:52 GMT
• Title: LANISTR: Multimodal Learning from Structured and Unstructured Data
• Authors: Sayna Ebrahimi, Sercan O. Arik, Yihe Dong, Tomas Pfister,
• Abstract summary: LANISTR is an attention-based framework to learn from LANguage, Image, and STRuctured data. 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.
• Score: 33.73687295669768
• License: http://creativecommons.org/licenses/by/4.0/
• 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

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