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arXiv · 2306.16413

MultiZoo & MultiBench: A Standardized Toolkit for Multimodal Deep Learning

Paul Pu Liang·Yiwei Lyu·Xiang Fan·Arav Agarwal·Yun Cheng·Louis-Philippe Morency·Ruslan Salakhutdinov
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Abstract

Learning multimodal representations involves integrating information from multiple heterogeneous sources of data. In order to accelerate progress towards understudied modalities and tasks while ensuring real-world robustness, we release MultiZoo, a public toolkit consisting of standardized implementations of > 20 core multimodal algorithms and MultiBench, a large-scale benchmark spanning 15 datasets, 10 modalities, 20 prediction tasks, and 6 research areas. Together, these provide an automated end-to-end machine learning pipeline that simplifies and standardizes data loading, experimental setup, and model evaluation. To enable holistic evaluation, we offer a comprehensive methodology to assess (1) generalization, (2) time and space complexity, and (3) modality robustness. MultiBench paves the way towards a better understanding of the capabilities and limitations of multimodal models, while ensuring ease of use, accessibility, and reproducibility. Our toolkits are publicly available, will be regularly updated, and welcome inputs from the community.

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Paul Pu Liang, Yiwei Lyu, Xiang Fan, Arav Agarwal, Yun Cheng, Louis-Philippe Morency, Ruslan Salakhutdinov. 2023-06-28. MultiZoo & MultiBench: A Standardized Toolkit for Multimodal Deep Learning. https://arxiv.org/abs/2306.16413

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