Mobile phones, tablets, computers, TVs - with screens proliferating and operations becoming increasingly complex, does it leave you feeling overwhelmed? Apple has recently dropped a bombshell - Ferret-UI2, an ultra-powerful UI understanding model, claiming it will unify the market!

This isn't just hot air; Ferret-UI2 aims to become a true hexagon warrior, capable of understanding user interfaces across various platforms, be it iPhone, Android, iPad, web, or Apple TV, it can handle them all with ease.

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One of the standout features of Ferret-UI2 is its support for multiple platforms. Unlike Ferret-UI which is limited to mobile platforms, Ferret-UI2 can understand UI screens from tablets, web, and smart TVs. This multi-platform support allows it to adapt to today's diverse device ecosystems, offering users a wider range of application scenarios.

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To enhance UI perception capabilities, Ferret-UI2 introduces dynamic high-resolution image encoding technology and employs an enhanced method called "adaptive grid." Through this method, Ferret-UI2 can maintain perception abilities at the original resolution of UI screenshots, more accurately identifying visual elements and their relationships.

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Additionally, Ferret-UI2 utilizes high-quality training data to learn basic and advanced tasks. For basic tasks, Ferret-UI2 converts simple references and positioning data into conversational forms, enabling the model to establish a basic understanding of various UI screens. For more user-experience-focused advanced tasks, Ferret-UI2 employs a **GPT-4o-based "token set visual prompt"** technique to generate training data, replacing previous methods' simple click instructions with single-step user-centric interactions.

To evaluate Ferret-UI2's performance, researchers constructed 45 benchmark tests covering five platforms, including six basic tasks and three advanced tasks for each platform. They also used public benchmarks like GUIDE and GUI-World. Results show that Ferret-UI2 outperforms Ferret-UI in all tested benchmarks, especially making significant strides in advanced tasks, demonstrating its versatility in handling cross-platform UI understanding tasks.

Ablation studies further indicate that the architectural and dataset improvements of Ferret-UI2 both contribute to performance enhancements, with the new dataset having a more significant impact on more challenging tasks. Additionally, Ferret-UI2 excels in cross-platform transfer learning, particularly showing good generalization capabilities between iPhone, iPad, and Android platforms.

Model address: https://huggingface.co/jadechoghari/Ferret-UI-Llama8b

Paper address: https://arxiv.org/pdf/2410.18967