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<!DOCTYPE html>
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content="Use co-occurrence to discover region-word pairs from image-text pairs for open-vocabulary object detection pre-training.">
<meta name="keywords" content="CoDet, Co-occurrence, Open-vocabulary Detection">
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<h1 class="title is-3 publication-title">CoDet: Co-Occurrence Guided Region-Word Alignment for Open-Vocabulary Object Detection</h1>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://machuofan.github.io/">Chuofan Ma</a><sup>1,2</sup>,</span>
<span class="author-block">
<a href="https://enjoyyi.github.io/">Yi Jiang</a><sup>2</sup>,</span>
<span class="author-block">
<a href="https://wen-xin.info/">Xin Wen</a><sup>1</sup>,
</span>
<span class="author-block">
Zehuan Yuan<sup>2</sup>,
</span>
<span class="author-block">
<a href="https://xjqi.github.io/">Xiaojuan Qi</a><sup>1</sup>,
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><sup>1</sup>University of Hong Kong,</span>
<span class="author-block"><sup>2</sup>ByteDance</span>
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<h2 class="subtitle has-text-centered">
CoDet leverages concept co-occurrence among image-text pairs to discover pseudo region-word pairs for open-vocabulary detection pre-training.
</h2>
</div>
</div>
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<h2 class="title is-3">Breaking the Chicken-and-Egg Problem</h2>
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<p>
Previous studies typically rely on region-text similarity to discover pseudo region-text pairs from image-text pairs.
But a tricky thing is that, to get accurate similarity estimation, you need a region-level aligned vision-language sapce,
which in turn requires abundant region-text pairs to train.
<br><br>
To break the chicken-and-egg problem, we introduce co-occurrence based region-word alignment,
which solely relies on region-region similarity to discover pseudo region-text pairs.
A nice property of co-occurrence is that you only need to scale up visual pre-training to get higher-quality pseudo-labels.
</p>
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<h2 class="title is-3">Abstract</h2>
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<p>
Deriving reliable region-word alignment from image-text pairs is critical to learn object-level
vision-language representations for open-vocabulary object detection. Existing methods typically rely on
pre-trained or self-trained vision-language models for alignment, which are prone to limitations in
localization accuracy or generalization capabilities. In this paper, we propose CoDet, a novel approach
that overcomes the reliance on pre-aligned vision-language space by reformulating region-word alignment
as a co-occurring object discovery problem. Intuitively, by grouping images that mention a shared concept
in their captions, objects corresponding to the shared concept shall exhibit high co-occurrence among the group.
CoDet then leverages visual similarities to discover the co-occurring objects and align them with the shared concept.
Extensive experiments demonstrate that CoDet has superior performances and compelling scalability
in open-vocabulary detection, e.g., by scaling up the visual backbone, CoDet achieves 37.0 mask AP for novel classes
and 44.7 mask AP for all classes on OV-LVIS, surpassing the previous SoTA by 4.2 and 9.8 mask AP, respectively.
</p>
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<h2 class="title is-3">Pseudo-Label Quality</h2>
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</section>
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<h2 class="title is-3">Benchmark Results</h2>
<img src="./static/images/ov_lvis_results.png"/>
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</div>
</section>
<section class="section" id="BibTeX">
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<h2 class="title">BibTeX</h2>
<pre><code>@inproceedings{ma2023codet,
title={CoDet: Co-Occurrence Guided Region-Word Alignment for Open-Vocabulary Object Detection},
author={Ma, Chuofan and Jiang, Yi and Wen, Xin and Yuan, Zehuan and Qi, Xiaojuan},
booktitle={Advances in Neural Information Processing Systems},
year={2023}
}
}</code></pre>
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