Pretraining Data Diversity and Robustness in Multimodal Tabular-Text Versus Unimodal Models on TableShift

Abstract

Abstract: Sensing human motions through Inertial Measurement Units (IMUs) embedded in personal devices has enabled significant applications in health and wellness. Labeled IMU data is scarce, however, unlabeled or weakly labeled IMU data can be used to model human motions. For video or text modalities, the "pretrain and adapt" approach utilizes large volumes of unlabeled or weakly labeled data to build a strong feature extractor, followed by adaptation to specific tasks using limited labeled data. However, pretraining methods are poorly understood for IMU data, and pipelines are rarely evaluated on out-

Research Question

What is the impact of pretraining data diversity on the robustness of multimodal tabular-text models versus unimodal text models when evaluated on TableShift subpopulations?

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