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V-Zen: Efficient GUI Understanding and Precise Grounding With A Novel Multimodal LLM

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

TL;DR

V-Zen presents a dual-resolution multimodal architecture for GUI understanding and precise grounding, integrating a low-resolution visual encoder, a multimodal projection pathway, a visual-expert-enhanced LLM, a high-resolution cross-visual module, and a DINO-based grounding head. The GUIDE dataset complements training with real-world GUI images, action histories, and chain-of-thought annotations to support specialized fine-tuning. Empirical results show strong performance in next-action prediction and grounding, outperforming several state-of-the-art models and demonstrating the value of open-set grounding at higher resolutions. The work aims to enable self-operating GUI agents and invites open collaboration through released code, data, and models to accelerate multimodal GUI automation research.

Abstract

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 an invitation to the research community to join this exciting journey, shaping the future of GUI automation. In the spirit of open science, our code, data, and model will be made publicly available, paving the way for multimodal dialogue scenarios with intricate and precise interactions.

V-Zen: Efficient GUI Understanding and Precise Grounding With A Novel Multimodal LLM

TL;DR

V-Zen presents a dual-resolution multimodal architecture for GUI understanding and precise grounding, integrating a low-resolution visual encoder, a multimodal projection pathway, a visual-expert-enhanced LLM, a high-resolution cross-visual module, and a DINO-based grounding head. The GUIDE dataset complements training with real-world GUI images, action histories, and chain-of-thought annotations to support specialized fine-tuning. Empirical results show strong performance in next-action prediction and grounding, outperforming several state-of-the-art models and demonstrating the value of open-set grounding at higher resolutions. The work aims to enable self-operating GUI agents and invites open collaboration through released code, data, and models to accelerate multimodal GUI automation research.

Abstract

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 an invitation to the research community to join this exciting journey, shaping the future of GUI automation. In the spirit of open science, our code, data, and model will be made publicly available, paving the way for multimodal dialogue scenarios with intricate and precise interactions.
Paper Structure (13 sections, 5 figures, 3 tables)

This paper contains 13 sections, 5 figures, 3 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Proposed Architecture
  4. Low-Resolution Visual Feature Extractor
  5. Multimodal Projection Adapter
  6. Pretrained Language Model with Visual Expert
  7. High-Resolution Cross Visual Module
  8. High-Precision Visual Grounding Module
  9. Experiments and Results
  10. Training Procedure
  11. GUIDE Dataset
  12. Results And Discussion
  13. Conclusion

Figures (5)

  • Figure 1: A Sample Case of GUI Automation Difficulty. In order to build intelligent systems capable of interacting seamlessly with various applications, identifying relevant UI components is crucial. As shown in this Gmail example, specifying tasks and their logical continuations requires a precise understanding of underlying GUI structures, predicting the next action, and precisely performing the grounding task. Our approach addresses these challenges effectively.
  • Figure 2: A timeline of SOTA MLLMs
  • Figure 3: Proposed Architecture Of V-Zen.
  • Figure 4: Some samples of the GUIDE dataset: Notice how the next action is predicted along with the bounding box locations, demonstrating the dataset's utility in guiding Multimodal Large Language Models for GUI automation tasks.
  • Figure 5: Qualitative Results on GUIDE Samples Using V-Zen. Demonstrates the effectiveness of our developed model in predicting the next actions and bounding box locations for achieving a given task.