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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8">
<meta name="description"
content="Towards Natural Language-Guided Drones:
GeoText-1652 Benchmark with Spatial
Relation Matching.">
<meta name="keywords" content="Spatial Relation Matching, Geolocalization, Text Guid-
ance, Drone Navigation">
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<title>Towards Natural Language-Guided Drones:
GeoText-1652 Benchmark with Spatial Relation Matching</title>
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<div class="hero-body">
<div class="container is-max-desktop">
<div class="columns is-centered">
<div class="column has-text-centered">
<h1 class="title is-1 publication-title">Towards Natural Language-Guided Drones:
GeoText-1652 Benchmark with Spatial Relation Matching</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://www.linkedin.com/in/chu-meng-3698241a0/">Meng Chu</a><sup>1</sup>,</span>
<span class="author-block">
<a href="https://www.zdzheng.xyz/">Zhedong Zheng*</a><sup>2</sup>,</span>
<span class="author-block">
<a href="https://jiwei0523.github.io/">Wei Ji</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=wv3H-F4AAAAJ&hl=en">Tingyu Wang</a><sup>3</sup>,
</span>
<span class="author-block">
<a href="https://www.chuatatseng.com/">Tat-Seng Chua</a><sup>1</sup>
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><sup>1</sup>National University of Singapore,</span>
<span class="author-block"><sup>2</sup>University of Macau,</span>
<span class="author-block"><sup>3</sup>Hangzhou Dianzi University</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><sup>*</sup>Correspondence</span>
</div>
<div class="column has-text-centered">
<div class="publication-links">
<!-- Paper Link -->
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<a href="https://arxiv.org/pdf/2311.12751"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="ai ai-arxiv"></i>
</span>
<span>Paper</span>
</a>
</span>
<!-- Poster Link -->
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<a href="https://drive.google.com/file/d/1QtLl3vtwUl-_rC_Ma48Gnaw-bHCMB7Qw/view?usp=share_link"
class="external-link button is-normal is-rounded is-dark">
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<i class="fas fa-file-pdf"></i>
</span>
<span>Poster</span>
</a>
</span>
<!-- Github Website Link -->
<span class="link-block">
<a href="https://github.com/MultimodalGeo/GeoText-1652"
class="external-link button is-normal is-rounded is-dark">
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</span>
<span>Github Code</span>
</a>
</span>
<!-- Dataset Download Link -->
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class="external-link button is-normal is-rounded is-dark">
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</a>
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class="external-link button is-normal is-rounded is-dark">
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</span>
<span>HF Dataset</span>
</a>
</span>
<!-- Hugging Face Model Link -->
<span class="link-block">
<a href="https://huggingface.co/truemanv5666/GeoText1652_model"
class="external-link button is-normal is-rounded is-dark">
<span class="icon">
<i class="fas fa-brain"></i>
</span>
<span>HF Model</span>
</a>
</span>
</div>
</div>
</div>
</div>
</div>
</div>
</section>
<section class="section">
<div class="container is-max-desktop">
<!-- Abstract. -->
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<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
Navigating drones through natural language commands re-
mains challenging due to the dearth of accessible multi-modal datasets
and the stringent precision requirements for aligning visual and textual
data. To address this pressing need, we introduce GeoText-1652, a new
natural language-guided geolocalization benchmark. This dataset is sys-
tematically constructed through an interactive human-computer process
leveraging Large Language Model (LLM) driven annotation techniques
in conjunction with pre-trained vision models. GeoText-1652 extends the
established University-1652 image dataset with spatial-aware text anno-
tations, thereby establishing one-to-one correspondences between image,
text, and bounding box elements. We further introduce a new optimiza-
tion objective to leverage fine-grained spatial associations, called blend-
ing spatial matching, for region-level spatial relation matching. Extensive
experiments reveal that our approach maintains a competitive recall rate
comparing other prevailing cross-modality methods. This underscores the
promising potential of our approach in elevating drone control and nav-
igation through the seamless integration of natural language commands
in real-world scenarios.
</div>
</div>
</div>
<!--/ Abstract. -->
<!-- Paper video. -->
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-3">Dataset Property</h2>
<div class="publication-image">
<img src="./static/images/images/Fig2_1.jpg" alt="Dataset Property">
</div>
</div>
</div>
<!--/ Paper video. -->
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<div class="column is-four-fifths">
<h2 class="title is-3">Annotation Framework</h2>
<div class="publication-image">
<img src="./static/images/images/Fig3_1.jpg" alt="Dataset Property">
</div>
</div>
</div>
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-3">Model Structure</h2>
<div class="publication-image">
<img src="./static/images/images/Fig4.jpg" alt="Dataset Property">
</div>
</div>
</div>
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<h2 class="title is-3">Real-world Generalization</h2>
<div class="publication-image">
<img src="./static/images/images/Fig5_test_1.jpg" alt="Dataset Property">
</div>
</div>
</div>
</div>
</section>
<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">BibTeX</h2>
<pre><code>@inproceedings{chu2024towards,
title={Towards Natural Language-Guided Drones: GeoText-1652 Benchmark with Spatial Relation Matching},
author={Chu, Meng and Zheng, Zhedong and Ji, Wei and Wang, Tingyu and Chua, Tat-Seng},
booktitle={EECV},
year={2024}
}</code></pre>
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</section>
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