  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 foundation 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.
