AMEX: Android Multi-annotation Expo Dataset for Mobile GUI Agents

TL;DR

AMEX is a large-scale, multi-annotation dataset for mobile GUI-control agents, enabling better understanding and interaction with mobile app interfaces through detailed annotations and natural language instructions.

Contribution

Introduces AMEX, a comprehensive dataset with multi-level annotations for mobile GUI understanding, and demonstrates its effectiveness by fine-tuning a baseline model.

Findings

AMEX contains over 104K annotated screenshots from mobile apps.

Fine-tuned SPHINX Agent shows improved performance on GUI tasks.

AMEX enhances research in mobile GUI interaction and understanding.

Abstract

AI agents have drawn increasing attention mostly on their ability to perceive environments, understand tasks, and autonomously achieve goals. To advance research on AI agents in mobile scenarios, we introduce the Android Multi-annotation EXpo (AMEX), a comprehensive, large-scale dataset designed for generalist mobile GUI-control agents which are capable of completing tasks by directly interacting with the graphical user interface (GUI) on mobile devices. AMEX comprises over 104K high-resolution screenshots from popular mobile applications, which are annotated at multiple levels. Unlike existing GUI-related datasets, e.g., Rico, AitW, etc., AMEX includes three levels of annotations: GUI interactive element grounding, GUI screen and element functionality descriptions, and complex natural language instructions with stepwise GUI-action chains. We develop this dataset from a more instructive and detailed perspective, complementing the general settings of existing datasets. Additionally, we finetune a baseline model SPHINX Agent and illustrate the effectiveness of AMEX.The project is available at https://yxchai.com/AMEX/.