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Ferret-UI: Grounded Mobile UI Understanding with Multimodal LLMs

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Computer Vision – ECCV 2024 (ECCV 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15122))

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Abstract

Recent advancements in multimodal large language models (MLLMs) have been noteworthy, yet, these general-domain MLLMs often fall short in their ability to comprehend and interact effectively with user interface (UI) screens. In this paper, we present Ferret-UI, a new MLLM tailored for enhanced understanding of mobile UI screens, equipped with referring, grounding, and reasoning capabilities. Given that UI screens typically exhibit a more elongated aspect ratio and contain smaller objects of interest (e.g., icons, texts) than natural images, we incorporate “any resolution” on top of Ferret to magnify details and leverage enhanced visual features. Specifically, each screen is divided into 2 sub-images based on the original aspect ratio and sub-images are encoded separately as additional features. We meticulously gather training samples from an extensive range of elementary UI tasks, such as icon recognition, find text, and widget listing. These samples are formatted for instruction-following with region annotations to facilitate precise referring and grounding. To augment the model’s reasoning ability, we further compile a dataset for advanced tasks, including detailed description, conversations, and function inference. After training on the curated datasets, Ferret-UI exhibits outstanding comprehension of UI screens and the capability to execute open-ended instructions. For model evaluation, we establish a comprehensive benchmark encompassing all the aforementioned tasks. Ferret-UI excels not only beyond most open-source UI MLLMs, but also surpasses GPT-4V on all the elementary UI tasks.

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Notes

  1. 1.

    https://developer.apple.com/documentation/vision.

  2. 2.

    For Ferret, we include the pre-defined classes for icon classification and widget classification in the prompts while the remaining prompts are the same as Ferret-UI.

  3. 3.

    For GPT-4V, we sample a random subset of 100 instances for the Spotlight and elementary tasks for cost efficiency. For GPT-4V evaluation, we follow [24] by overlaying indexed bounding boxes of UI elements as visual prompts. Consequently, in grounding tasks, GPT-4V is enabled to make selections from among these candidate boxes. We detail the effort in the Appendix.

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Correspondence to Keen You .

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You, K. et al. (2025). Ferret-UI: Grounded Mobile UI Understanding with Multimodal LLMs. In: Leonardis, A., Ricci, E., Roth, S., Russakovsky, O., Sattler, T., Varol, G. (eds) Computer Vision – ECCV 2024. ECCV 2024. Lecture Notes in Computer Science, vol 15122. Springer, Cham. https://doi.org/10.1007/978-3-031-73039-9_14

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  • DOI: https://doi.org/10.1007/978-3-031-73039-9_14

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