Ferret-UI 2: Mastering Universal User Interface Understanding Across Platforms
Zhangheng LI Keen You Haotian Zhang Di Feng Harsh Agrawal Xiujun Li Mohana Prasad Sathya Moorthy Jeffrey Nichols Yinfei Yang Zhe Gan
Building a generalist model for user interface (UI) understanding is challenging due to various foundational issues, such as platform diversity, resolution variation, and data limitation. In this paper, we introduce Ferret-UI 2, a multimodal large language model (MLLM) designed for universal UI understanding across a wide range of platforms, including iPhone, Android, iPad, Webpage, and AppleTV. Building on the foundation of Ferret-UI, Ferret-UI 2 introduces three key innovations: support for multiple platform types, high-resolution perception through adaptive scaling, and advanced task training data generation powered by GPT-4o with set-of-mark visual prompting. These advancements enable Ferret-UI 2 to perform complex, user-centered interactions, making it highly versatile and adaptable for the expanding diversity of platform ecosystems. Extensive empirical experiments on referring, grounding, user-centric advanced tasks (comprising 9 subtasks $\times$ 5 platforms), GUIDE next-action prediction dataset, and GUI-World multi-platform benchmark demonstrate that Ferret-UI 2 significantly outperforms Ferret-UI, and also shows strong cross-platform transfer capabilities.
Multimodal LLM, UI Understanding
A multimodal large language model (MLLM) designed for universal UI understanding across diverse platforms, including iPhone, Android, iPad, Webpages, and AppleTV.