AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models
Abstract
AutoGluon-Multimodal (AutoMM) is introduced as an open-source AutoML library designed specifically for multimodal learning. Distinguished by its exceptional ease of use, AutoMM enables fine-tuning of foundational models with just three lines of code. Supporting various modalities including image, text, and tabular data, both independently and in combination, the library offers a comprehensive suite of functionalities spanning classification, regression, object detection, semantic matching, and image segmentation. Experiments across diverse datasets and tasks showcases AutoMM’s superior performance in basic classification and regression tasks compared to existing AutoML tools, while also demonstrating competitive results in advanced tasks, aligning with specialized toolboxes designed for such purposes.
Cite
Text
Tang et al. "AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models." Proceedings of the Third International Conference on Automated Machine Learning, 2024. doi:10.48550/arXiv.2404.16233Markdown
[Tang et al. "AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models." Proceedings of the Third International Conference on Automated Machine Learning, 2024.](https://mlanthology.org/automl/2024/tang2024automl-autogluonmultimodal/) doi:10.48550/arXiv.2404.16233BibTeX
@inproceedings{tang2024automl-autogluonmultimodal,
title = {{AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models}},
author = {Tang, Zhiqiang and Fang, Haoyang and Zhou, Su and Yang, Taojiannan and Zhong, Zihan and Hu, Cuixiong and Kirchhoff, Katrin and Karypis, George},
booktitle = {Proceedings of the Third International Conference on Automated Machine Learning},
year = {2024},
pages = {15/1-35},
doi = {10.48550/arXiv.2404.16233},
volume = {256},
url = {https://mlanthology.org/automl/2024/tang2024automl-autogluonmultimodal/}
}