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<html>
<head>
<title>Content-aware Token Sharing for Efficient Semantic Segmentation with Vision Transformers</title>
<meta property="og:image" content="Path to my teaser.png"/> <!-- Facebook automatically scrapes this. Go to https://developers.facebook.com/tools/debug/ if you update and want to force Facebook to rescrape. -->
<meta property="og:title" content="Content-aware Token Sharing for Efficient Semantic Segmentation with Vision Transformers" />
<meta property="og:description" content="This paper introduces Content-aware Token Sharing (CTS), a token reduction approach that improves the computational efficiency of semantic segmentation networks that use Vision Transformers (ViTs)." />
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<body>
<br>
<center>
<span style="font-size:36px">Content-aware Token Sharing for Efficient Semantic Segmentation with Vision Transformers</span>
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<span style="font-size:24px"><a href="https://chenyang-lu.github.io/">Chenyang Lu</a>*</span>
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<span style="font-size:24px"><a href="http://daandegeus.com/">Daan de Geus</a>*</span>
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<span style="font-size:24px"><a href="#">Gijs Dubbelman</a></span>
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<span style="font-size:16px">*Both authors contributed equally.</span>
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<span style="font-size:24px"><a href='https://openaccess.thecvf.com/content/CVPR2023/papers/Lu_Content-Aware_Token_Sharing_for_Efficient_Semantic_Segmentation_With_Vision_Transformers_CVPR_2023_paper.pdf'>[Paper]</a></span>
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<span style="font-size:24px"><a href='https://github.com/tue-mps/cts-segmenter'>[GitHub]</a></span><br>
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<img class="round" style="width:720px" src="./resources/teaser.png"/>
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</table>
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This was a template originally made for <a href="http://richzhang.github.io/colorization/">Colorful Image Colorization</a>. The code can be found in this <a href="https://github.com/richzhang/webpage-template">repository</a>.
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<center><h1>Abstract</h1></center>
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This paper introduces Content-aware Token Sharing (CTS), a token reduction approach that improves the computational efficiency of semantic segmentation networks that use Vision Transformers (ViTs). Existing works have proposed token reduction approaches to improve the efficiency of ViT-based image classification networks, but these methods are not directly applicable to semantic segmentation, which we address in this work. We observe that, for semantic segmentation, multiple image patches can share a token if they contain the same semantic class, as they contain redundant information. Our approach leverages this by employing an efficient, class-agnostic policy network that predicts if image patches contain the same semantic class, and lets them share a token if they do. With experiments, we explore the critical design choices of CTS and show its effectiveness on the ADE20K, Pascal Context and Cityscapes datasets, various ViT backbones, and different segmentation decoders. With Content-aware Token Sharing, we are able to reduce the number of processed tokens by up to 44%, without diminishing the segmentation quality.
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<iframe width="660" height="395" src="https://www.youtube.com/embed/dQw4w9WgXcQ" frameborder="0" allow="autoplay; encrypted-media" allowfullscreen align="center"></iframe>
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<span style="font-size:28px"><a href=''>[Slides]</a>
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<center><h1>Code</h1></center>
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We publicly release te code of CTS applied to <a href="https://arxiv.org/abs/2105.05633">Segmenter</a> in <a href="https://github.com/tue-mps/cts-segmenter">this GitHub repository</a>.
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<span style="font-size:28px"> <a href='https://github.com/tue-mps/cts-segmenter'>[GitHub]</a>
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<center><h1>Paper and Supplementary Material</h1></center>
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<td><a href=""><img class="layered-paper-big" style="height:175px" src="./resources/paper.png"/></a></td>
<td><span style="font-size:14pt">C. Lu*, D. de Geus*, G. Dubbelman.<br>
<b>Content-aware Token Sharing for Efficient Semantic Segmentation with Vision Transformers.</b><br>
In IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2023.<br>
(<a href="https://openaccess.thecvf.com/content/CVPR2023/papers/Lu_Content-Aware_Token_Sharing_for_Efficient_Semantic_Segmentation_With_Vision_Transformers_CVPR_2023_paper.pdf">PDF</a>)<br>
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<span style="font-size:4pt"><a href=""><br></a>
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<a href="./resources/bibtex.txt">[Bibtex]</a>
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</table>
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<center><h1>Acknowledgements</h1></center>
This template was originally made by <a href="http://web.mit.edu/phillipi/">Phillip Isola</a> and <a href="http://richzhang.github.io/">Richard Zhang</a> for a <a href="http://richzhang.github.io/colorization/">colorful</a> ECCV project; the code can be found <a href="https://github.com/richzhang/webpage-template">here</a>.
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