Bag of Tricks for Multimodal AutoML with Image, Text, and Tabular Data

• URL: http://arxiv.org/abs/2412.16243v1
• Date: Thu, 19 Dec 2024 20:52:10 GMT
• Title: Bag of Tricks for Multimodal AutoML with Image, Text, and Tabular Data
• Authors: Zhiqiang Tang, Zihan Zhong, Tong He, Gerald Friedland,
• Abstract summary: This paper studies the best practices for automatic machine learning (AutoML)<n>We curate a benchmark comprising 22 multimodal datasets from diverse real-world applications.<n>Through extensive experimentation and analysis, we distill a collection of effective strategies and consolidate them into a unified pipeline.
• Score: 9.325441307607225
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: This paper studies the best practices for automatic machine learning (AutoML). While previous AutoML efforts have predominantly focused on unimodal data, the multimodal aspect remains under-explored. Our study delves into classification and regression problems involving flexible combinations of image, text, and tabular data. We curate a benchmark comprising 22 multimodal datasets from diverse real-world applications, encompassing all 4 combinations of the 3 modalities. Across this benchmark, we scrutinize design choices related to multimodal fusion strategies, multimodal data augmentation, converting tabular data into text, cross-modal alignment, and handling missing modalities. Through extensive experimentation and analysis, we distill a collection of effective strategies and consolidate them into a unified pipeline, achieving robust performance on diverse datasets.

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