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<!DOCTYPE html>
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<title>OmniParser</title>
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<section class="hero">
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<div class="container is-max-desktop">
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<h1 class="title is-1 publication-title">OmniParser for Pure Vision Based GUI Agent</h1>
<div class="is-size-5 publication-authors">
<!-- Paper authors -->
<span class="author-block">
<a href="https://adamlu123.github.io/" target="_blank">Yadong Lu</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="https://jwyang.github.io/" target="_blank">Jianwei Yang</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=S6OFEFEAAAAJ&hl=en" target="_blank">Yelong Shen</a><sup>2</sup>,
</span>
<span class="author-block">
<a href="https://www.microsoft.com/en-us/research/people/hassanam/?from=https://research.microsoft.com/en-us/um/people/hassanam/&type=exact" target="_blank">Ahmed Awadallah</a><sup>1</sup>,
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block">
<sup>1</sup>Microsoft Research,
<sup>2</sup>Microsoft Gen AI,<br>
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<span>Code</span>
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class="external-link button is-normal is-rounded is-dark">
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<i class="ai ai-arxiv"></i>
</span>
<span>arXiv</span>
</a>
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<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
The recent success of large vision language models shows great potential in driving the agent system operating on user interfaces. However, we argue that the
power multimodal models like GPT-4V as a general agent on multiple operating
systems across different applications is largely underestimated due to the lack of
a robust screen parsing technique capable of: 1. reliably identifying interactable
icons within the user interface, and 2. understanding the semantics of various
elements in a screenshot and accurately associate the intended action with the
corresponding region on the screen. To fill these gaps, we introduce OMNIPARSER,
a comprehensive method for parsing user interface screenshots into structured
elements, which significantly enhances the ability of GPT-4V to generate actions
that can be accurately grounded in the corresponding regions of the interface. We
first curated an interactable icon detection dataset using popular webpages and
an icon description dataset. These datasets were utilized to fine-tune specialized
models: a detection model to parse interactable regions on the screen and a caption
model to extract the functional semantics of the detected elements. OMNIPARSER
significantly improves GPT-4V's performance on ScreenSpot benchmark. And
on Mind2Web and AITW benchmark, OMNIPARSER with screenshot only input
outperforms the GPT-4V baselines requiring additional information outside of
screenshot
</p>
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</div>
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</section>
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<!-- Your image here -->
<img src="static/images/flow_merged_0.png" alt="Result mobile" />
<img src="static/images/flow_merged_1.png" alt="Result mobile" />
<img src="static/images/flow_merged_2.png" alt="Result mobile" />
<h2 class="subtitle">
Examples of parsed screenshot image and local semantics by \textsc{OmniParser}. The inputs to OmniParse are user task and UI screenshot, from which it will produce: 1. parsed screenshot image with bounding boxes and numeric IDs overlayed, and 2. local semantics contains both text extracted and icon description.
</h2>
</div>
<!-- </div> -->
</div>
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<!-- End image carousel -->
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<div class="container is-max-desktop">
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<h2 class="title is-3">Curated Dataset for Interactable Region Detection and Icon Functionality Description</h2>
<br>
<b>Key Takeaways:</b> The addition of one or more contexts results in a performance gain over the no-context baseline in the vast majority of cases. Furthermore, the use of multiple contexts results in a performance boost in a majority of cases.
<div class="item">
<img src="static/images/curated_data.png" alt="Species Classification results on iWildCam2020-WILDS (OOD) dataset" />
<p>
<b>Examples from the Interactable Region Detection dataset. </b>. TThe bounding boxes are based on the interactable region extracted from the DOM tree of the webpage.
</p>
</div>
</div>
</div>
</div>
</div>
</section>
<section class="hero is-small"></section>
<div class="hero-body">
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-3">Curated Dataset for Interactable Region Detection and Icon Functionality Description</h2>
<br>
<b>Key Takeaways:</b> The addition of one or more contexts results in a performance gain over the no-context baseline in the vast majority of cases. Furthermore, the use of multiple contexts results in a performance boost in a majority of cases.
<div class="item">
<img src="static/images/curated_data.png" alt="Species Classification results on iWildCam2020-WILDS (OOD) dataset" />
<p>
<b>Examples from the Interactable Region Detection dataset. </b>. TThe bounding boxes are based on the interactable region extracted from the DOM tree of the webpage.
</p>
</div>
</div>
</div>
</div>
</div>
</section>
<br>
<section class="hero is-light is-small">
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<div class="container">
<div id="results-carousel" class="carousel results-carousel">
<div class="item item-steve">
<video poster="" id="steve" autoplay controls muted loop playsinline height="100%">
<source src="./static/videos/web_5_demo_nocap.mp4"
type="video/mp4">
</video>
</div>
<div class="item item-chair-tp">
<video poster="" id="chair-tp" autoplay controls muted loop playsinline height="100%">
<source src="./static/videos/web_22_demo_nocap.mp4"
type="video/mp4">
</video>
</div>
</div>
</div>
</div>
</section>
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<h2 class="title">Citation</h2>
<!-- Please cite our paper if you use our code, data, model or results: -->
<!-- <br> -->
<pre><code>@misc{lu2024omniparserpurevisionbased,
title={OmniParser for Pure Vision Based GUI Agent},
author={Yadong Lu and Jianwei Yang and Yelong Shen and Ahmed Awadallah},
year={2024},
eprint={2408.00203},
archivePrefix={arXiv},
primaryClass={cs.CV},
url={https://arxiv.org/abs/2408.00203},
}
</code></pre>
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