Attention-driven GUI Grounding: Leveraging Pretrained Multimodal Large Language Models without Fine-Tuning

TL;DR

This paper introduces a tuning-free method called TAG that uses attention patterns in pretrained multimodal large language models to accurately identify GUI components without additional training, achieving performance comparable to fine-tuned models.

Contribution

The paper presents a novel attention-driven grounding approach that leverages pretrained MLLMs' inherent attention, eliminating the need for fine-tuning in GUI component localization.

Findings

Attention maps effectively localize GUI components.

TAG achieves comparable performance to fine-tuned models.

Attention-based grounding outperforms direct localization predictions.

Abstract

Recent advancements in Multimodal Large Language Models (MLLMs) have generated significant interest in their ability to autonomously interact with and interpret Graphical User Interfaces (GUIs). A major challenge in these systems is grounding-accurately identifying critical GUI components such as text or icons based on a GUI image and a corresponding text query. Traditionally, this task has relied on fine-tuning MLLMs with specialized training data to predict component locations directly. However, in this paper, we propose a novel Tuning-free Attention-driven Grounding (TAG) method that leverages the inherent attention patterns in pretrained MLLMs to accomplish this task without the need for additional fine-tuning. Our method involves identifying and aggregating attention maps from specific tokens within a carefully constructed query prompt. Applied to MiniCPM-Llama3-V 2.5, a state-of-the-art MLLM, our tuning-free approach achieves performance comparable to tuning-based methods, with notable success in text localization. Additionally, we demonstrate that our attention map-based grounding technique significantly outperforms direct localization predictions from MiniCPM-Llama3-V 2.5, highlighting the potential of using attention maps from pretrained MLLMs and paving the way for future innovations in this domain.