# AutoGluon-Multimodal (AutoMM): Supercharging Multimodal AutoML with Foundation Models

arXiv: https://arxiv.org/abs/2404.16233 · HTML: https://mlsanity.com/paper/2404.16233/

## Key facts

- Cited by 30 papers in the map (data through 14 Oct 2026).
- Builds on 71 of its 128 references that are in the map.
- First posted on arXiv 24 Apr 2024.

## Builds on (most cited first, 30 of 71)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [Learning Transferable Visual Models From Natural Language Supervision](https://mlsanity.com/paper/2103.00020/) (2021)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Microsoft COCO: Common Objects in Context](https://mlsanity.com/paper/1405.0312/) (2014)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Swin Transformer: Hierarchical Vision Transformer using Shifted Windows](https://mlsanity.com/paper/2103.14030/) (2021)
- [Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks](https://mlsanity.com/paper/1908.10084/) (2019)
- [Segment Anything](https://mlsanity.com/paper/2304.02643/) (2023)
- [The Cityscapes Dataset for Semantic Urban Scene Understanding](https://mlsanity.com/paper/1604.01685/) (2016)
- [Fashion-MNIST: a Novel Image Dataset for Benchmarking Machine Learning Algorithms](https://mlsanity.com/paper/1708.07747/) (2017)
- [Parameter-Efficient Transfer Learning for NLP](https://mlsanity.com/paper/1902.00751/) (2019)
- [On the Opportunities and Risks of Foundation Models](https://mlsanity.com/paper/2108.07258/) (2021)
- [Scaling Instruction-Finetuned Language Models](https://mlsanity.com/paper/2210.11416/) (2022)
- [A Broad-Coverage Challenge Corpus for Sentence Understanding through Inference](https://mlsanity.com/paper/1704.05426/) (2017)
- [Visual Genome: Connecting Language and Vision Using Crowdsourced Dense Image Annotations](https://mlsanity.com/paper/1602.07332/) (2016)
- [Character-level Convolutional Networks for Text Classification](https://mlsanity.com/paper/1509.01626/) (2015)
- [MMDetection: Open MMLab Detection Toolbox and Benchmark](https://mlsanity.com/paper/1906.07155/) (2019)
- [Deep High-Resolution Representation Learning for Human Pose Estimation](https://mlsanity.com/paper/1902.09212/) (2019)
- [Continual Lifelong Learning with Neural Networks: A Review](https://mlsanity.com/paper/1802.07569/) (2018)
- [DINO: DETR with Improved DeNoising Anchor Boxes for End-to-End Object Detection](https://mlsanity.com/paper/2203.03605/) (2022)
- [DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing](https://mlsanity.com/paper/2111.09543/) (2021)
- [BitFit: Simple Parameter-efficient Fine-tuning for Transformer-based Masked Language-models](https://mlsanity.com/paper/2106.10199/) (2021)
- [Mixed Precision Training](https://mlsanity.com/paper/1710.03740/) (2017)
- [YOLOX: Exceeding YOLO Series in 2021](https://mlsanity.com/paper/2107.08430/) (2021)
- [Multimodal Machine Learning: A Survey and Taxonomy](https://mlsanity.com/paper/1705.09406/) (2017)
- [Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning](https://mlsanity.com/paper/2205.05638/) (2022)
- [TabPFN: A Transformer That Solves Small Tabular Classification Problems in a Second](https://mlsanity.com/paper/2207.01848/) (2022)

## Built on by (most cited first, 30 of 30)

- [AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoML](https://mlsanity.com/paper/2410.02958/) (2024)
- [ML-Master: Towards AI-for-AI via Integration of Exploration and Reasoning](https://mlsanity.com/paper/2506.16499/) (2025)
- [R&D-Agent: An LLM-Agent Framework Towards Autonomous Data Science](https://mlsanity.com/paper/2505.14738/) (2025)
- [ML-Agent: Reinforcing LLM Agents for Autonomous Machine Learning Engineering](https://mlsanity.com/paper/2505.23723/) (2025)
- [Kolb-Based Experiential Learning for Generalist Agents with Human-Level Kaggle Data Science Performance](https://mlsanity.com/paper/2411.03562/) (2024)
- [SELA: Tree-Search Enhanced LLM Agents for Automated Machine Learning](https://mlsanity.com/paper/2410.17238/) (2024)
- [MLZero: A Multi-Agent System for End-to-end Machine Learning Automation](https://mlsanity.com/paper/2505.13941/) (2025)
- [Beyond IID: How General Are Tabular Foundation Models, Really?](https://mlsanity.com/paper/2606.30410/) (2026)
- [TabSTAR: A Tabular Foundation Model for Tabular Data with Text Fields](https://mlsanity.com/paper/2505.18125/) (2025)
- [I-MCTS: Enhancing Agentic AutoML via Introspective Monte Carlo Tree Search](https://mlsanity.com/paper/2502.14693/) (2025)
- [LLM-based feature generation from text for interpretable machine learning](https://mlsanity.com/paper/2409.07132/) (2024)
- [MultiModalPFN: Extending Prior-Data Fitted Networks for Multimodal Tabular Learning](https://mlsanity.com/paper/2602.20223/) (2026)
- [TREX: Automating LLM Fine-tuning via Agent-Driven Tree-based Exploration](https://mlsanity.com/paper/2604.14116/) (2026)
- [Bag of Tricks for Multimodal AutoML with Image, Text, and Tabular Data](https://mlsanity.com/paper/2412.16243/) (2024)
- [Kaputt: A Large-Scale Dataset for Visual Defect Detection](https://mlsanity.com/paper/2510.05903/) (2025)
- [Transfer Learning for Finetuning Large Language Models](https://mlsanity.com/paper/2411.01195/) (2024)
- [Automated Ensemble Multimodal Machine Learning for Healthcare](https://mlsanity.com/paper/2407.18227/) (2024)
- [Multimodal Representation Learning and Fusion](https://mlsanity.com/paper/2506.20494/) (2025)
- [MulTaBench: Benchmarking Multimodal Tabular Learning with Text and Image](https://mlsanity.com/paper/2605.10616/) (2026)
- [Generative Distribution Prediction: A Unified Approach to Multimodal Learning](https://mlsanity.com/paper/2502.07090/) (2025)
- [AutoIntent: AutoML for Text Classification](https://mlsanity.com/paper/2509.21138/) (2025)
- [Towards Cross-Modal Error Detection with Tables and Images](https://mlsanity.com/paper/2510.12383/) (2025)
- [AutoML Algorithms for Online Generalized Additive Model Selection: Application to Electricity Demand Forecasting](https://mlsanity.com/paper/2503.24019/) (2025)
- [Quickly Tuning Foundation Models for Image Segmentation](https://mlsanity.com/paper/2508.17283/) (2025)
- [ML2B: Benchmarking LLMs on Cross-Lingual ML Pipeline Generation](https://mlsanity.com/paper/2509.22768/) (2025)
- [BioAutoML-NAS: An End-to-End AutoML Framework for Multimodal Insect Classification via Neural Architecture Search on Large-Scale Biodiversity Data](https://mlsanity.com/paper/2510.05888/) (2025)
- [Cost Savings from Automatic Quality Assessment of Generated Images](https://mlsanity.com/paper/2510.16179/) (2025)
- [SNAC-Pack 2.0: Scaled-Out Surrogate Neural Architecture Codesign](https://mlsanity.com/paper/2605.16138/) (2026)
- [Modular Multimodal Classification Without Fine-Tuning: A Simple Compositional Approach](https://mlsanity.com/paper/2605.20674/) (2026)
- [Converting Expert Deliberation into Financial Signals Through A Context-Aware NLP Pipeline](https://mlsanity.com/paper/2608.18911/) (2026)

## Abstract (arXiv)

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.