2024-10-09-tang24a.md

title	openreview	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models	irStSm9waW	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.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	tang24a	0	AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models	15/1	35	15/1-35	15	false	Tang, Zhiqiang and Fang, Haoyang and Zhou, Su and Yang, Taojiannan and Zhong, Zihan and Hu, Cuixiong and Kirchhoff, Katrin and Karypis, George	given family Zhiqiang Tang given family Haoyang Fang given family Su Zhou given family Taojiannan Yang given family Zihan Zhong given family Cuixiong Hu given family Katrin Kirchhoff given family George Karypis	2024-10-09		Proceedings of the Third International Conference on Automated Machine Learning	256	inproceedings	date-parts 2024 10 9	https://raw.githubusercontent.com/mlresearch/v256/main/assets/tang24a/tang24a.pdf