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Found 3 papers, 1 papers with code
V-Zen: Efficient GUI Understanding and Precise Grounding With A Novel Multimodal LLM
In the rapidly evolving landscape of AI research and application, Multimodal Large Language Models (MLLMs) have emerged as a transformative force, adept at interpreting and integrating information from diverse modalities such as text, images, and Graphical User Interfaces (GUIs).
GUIDE: Graphical User Interface Data for Execution
The dataset's multi-platform nature and coverage of diverse websites enable the exploration of cross-interface capabilities in automation tasks.
Veagle: Advancements in Multimodal Representation Learning
In response to the limitations observed in current Vision Language Models (VLMs) and Multimodal Large Language Models (MLLMs), our proposed model Veagle, incorporates a unique mechanism inspired by the successes and insights of previous works.
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