most citedFerret-UI: Grounded Mobile UI Understanding with Multimodal LLMs

3 citations · 6 across the 6 of their papers we have counts for

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cs.CV2025

UltraCUA: A Foundation Model for Computer Use Agents with Hybrid Action

Yuhao Yang, Zhen Yang, Zi-Yi Dou +10

Computer-use agents face a fundamental limitation. They rely exclusively on primitive GUI actions (click, type, scroll), creating brittle execution chains prone to cascading failur…

cs.CV2025

Ferret-UI Lite: Lessons from Building Small On-Device GUI Agents

Zhen Yang, Zi-Yi Dou, Di Feng +13

Developing autonomous agents that effectively interact with Graphic User Interfaces (GUIs) remains a challenging open problem, especially for small on-device models. In this paper,…

cs.CV2024

Ferret-UI 2: Mastering Universal User Interface Understanding Across Platforms

Zhangheng Li, Keen You, Haotian Zhang +7

Building a generalist model for user interface (UI) understanding is challenging due to various foundational issues, such as platform diversity, resolution variation, and data limi…

cs.CV2024★ 2 cited

Contrastive Localized Language-Image Pre-Training

Hong-You Chen, Zhengfeng Lai, Haotian Zhang +7

Contrastive Language-Image Pre-training (CLIP) has been a celebrated method for training vision encoders to generate image/text representations facilitating various applications. R…

cs.CV2024★ 1 cited

MM1.5: Methods, Analysis & Insights from Multimodal LLM Fine-tuning

Haotian Zhang, Mingfei Gao, Zhe Gan +20

We present MM1.5, a new family of multimodal large language models (MLLMs) designed to enhance capabilities in text-rich image understanding, visual referring and grounding, and mu…

cs.CV2024★ 3 cited

Ferret-UI: Grounded Mobile UI Understanding with Multimodal LLMs

Keen You, Haotian Zhang, Eldon Schoop +5

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 e…