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<li><a href="#home"><img src="assets/icon_GN.png" alt="Home"
style="height:30px; width:30px; vertical-align:middle; margin-right:5px;"> <strong>GUI Action Narrator</strong> </a>
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style="height:80px; width:80px; vertical-align:middle; margin-right:5px;">GUI Action Narrator:<br> Where and When Did That Action Take Place?
</h1>
<div class="is-size-5 publication-authors">
<div class="author-block">
Qinchen Wu<sup>1</sup>,</div>
<div class="author-block">
Difei Gao<sup>1</sup>,</div>
<div class="author-block">
Kevin Qinghong Lin<sup>1</sup>,</div>
<div class="author-block">
Zhuoyu Wu<sup>2</sup>,
</div>
<br>
<div class="author-block">
Xiangwu Guo<sup>1</sup>,</div>
<div class="author-block">
Peiran Li<sup>1</sup>,</div>
<div class="author-block">
Weichen Zhang<sup>1</sup>,</div>
<div class="author-block">
Hengxu Wang<sup>1</sup>,</div>
<div class="author-block">
Mike Zheng Shou<sup>1</sup>
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<span class="author-block"><sup>♢</sup>Chinese Academy of Sciences, Shenzhen</span> -->
<span class="author-block"><sup>1</sup>Show Lab, National University of Singapore,</span>
<span class="author-block"><sup>2</sup>Chinese Academy of Sciences, Shenzhen</span>
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<a href="assets/demonstrate_video.gif">
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<p class="caption" style="margin-bottom: 1px; text-align: justify">
We introduce GUI action dataset Act2Cap as well as an effective framework: GUI Narrator for GUI video captioning that utilizes the cursor as a visual prompt to enhance the interpretation of high-resolution screenshots.
</p>
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PV3D is able to generate diverse videos with multi-view consistency and detailed dynamic 3D geometry.
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<p>
The advent of Multimodal LLMs has significantly enhanced image OCR recognition capabilities, making GUI automation a viable reality for increasing efficiency
in digital tasks. One fundamental aspect of developing a GUI automation system is
understanding primitive GUI actions. This comprehension is crucial as it enables
agents to learn from user demonstrations, an essential element of automation. To
rigorously evaluate such capabilities, we developed a video captioning benchmark
for GUI actions, comprising 4,189 diverse video captioning samples. This task
presents unique challenges compared to natural scene video captioning: 1) GUI
screenshots typically contain denser information than natural scenes, and 2) events
within GUIs are subtler and occur more rapidly, requiring precise attention to the
appropriate time span and spatial region for accurate understanding. To address
these challenges, we introduce our GUI action dataset Act2Cap as well as a
simple yet effective framework, GUI Narrator , for GUI video captioning that uti
lizes the cursor as a visual prompt to enhance the interpretation of high-resolution
screenshots. Specifically, a cursor detector is trained on our dataset, and a mul
timodal LLM model with mechanisms for selecting keyframes and key regions
generates the captions. Experimental results indicate that even for today’s most
advanced multimodal models, such as GPT-4o, the task remains highly challenging.
Additionally, our evaluations show that our strategy effectively enhances model
performance, whether integrated into the fine-tuning of open-source models or
employed as a prompting strategy in closed-source models. Moreover, we propose an advanced Actor-Critic Embodied Agent framework, which incorporates a sophisticated GUI parser driven by an LLM-agent and an enhanced reasoning mechanism adept at handling lengthy procedural tasks. Our experimental results reveal that our GUI Parser and Reasoning mechanism outshine existing methods in performance. Nevertheless, the potential remains substantial, with the best model attaining only a 46% success rate on our benchmark. We conclude with a thorough analysis of the current methods' limitations, setting the stage for future breakthroughs in this domain.
</p>
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<h2 class="title is-3">Main contributions</h2>
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<div class="content has-text-justified">
<p>
Our work places emphasis on the following three aspects
<ul>
<li><strong>Dataset:</strong> Act2Cap contains 4K+ GUI video (Action frames), caption pairs collected from GUI layouts including WORD, EXCEL, PPT, AE, PR, WEB through automatic pipeline and human demonstration. </li>
<li><strong>Benchmark:</strong> Metric for evaluating the quality of narration generated from LLMs. </li>
<li><strong>Model baseline:</strong> Two stage model effectively designed for narrating actions in GUI. </li>
<!-- <li><strong>Critic:</strong> Assess every previous action to help Actor adjust the following steps.</li> -->
</ul>
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<source src="./assets/AssistGUI_method.png" type="video/mp4">
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<h2 class="title">BibTeX</h2>
<pre><code>@article{gao2023assistgui,
title = {GUI Action Narrator: Where and When Did That Action Take Place?},
author = {Qinchen Wu and Difei Gao and Kevin Qinghong Lin and Zhuoyu Wu and Xiangwu Guo and Peiran Li and Weichen Zhang and Hengxu Wang and Mike Zheng Shou},
year = {2024}, -->
</code></pre>
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