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V-Zen: Efficient GUI Understanding and Precise Grounding With A Novel Multimodal LLM by Rahman, Abdur; Chawla, Rajat; Kumar, Muskaan; Datta, Arkajit; Jha, Adarsh; NS, Mukunda; Bhola, Ishaan is a scholarly article available to read on EtoBox.

What is V-Zen: Efficient GUI Understanding and Precise Grounding With A Novel Multimodal LLM about?

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). Despite these advancements, the nuanced interaction and understanding of GUIs pose a significant challenge, limiting the potential of existing models to enhance automation levels. To bridge this gap, this paper presents V-Zen, an innovative Multimodal Large Language Model (MLLM) meticulously crafted to revolutionise the domain of GUI understanding and grounding. Equipped with dual-resolution image encoders, V-Zen establishes new benchmarks in efficient grounding and next-action prediction, thereby laying the groundwork for self-operating computer systems. Complementing V-Zen is the GUIDE dataset, an extensive collection of real-world GUI elements and task-based sequences, serving as a catalyst for specialised fine-tuning. The successful integration of V-Zen and GUIDE marks the dawn of a new era in multimodal AI research, opening the door to intelligent, autonomous computing experiences. This paper extends

Author

Rahman, Abdur; Chawla, Rajat; Kumar, Muskaan; Datta, Arkajit; Jha, Adarsh; NS, Mukunda; Bhola, Ishaan

Published

2024

Language

EN