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
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.