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<!DOCTYPE html>
<html>
<head>
<meta charset="utf-8" />
<meta
name="description"
content="An Open Vocabulary Semantic Segmenter based on Diffusion Models."
/>
<meta
name="keywords"
content="DiffSegmenter, Diffusion, Segmentation, Open Vocabulary"
/>
<meta name="viewport" content="width=device-width, initial-scale=1" />
<title>
Diffusion Model is Secretly a Training-free Open Vocabulary Semantic
Segmenter
</title>
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role="button"
class="navbar-burger"
aria-label="menu"
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<a class="navbar-item" href="https://vcg-team.github.io/homepage">
<span class="icon">
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</span>
</a>
<div class="navbar-item has-dropdown is-hoverable">
<a class="navbar-link"> More Research </a>
<div class="navbar-dropdown">
<a
class="navbar-item"
href="https://vcg-team.github.io/DiffSegmenter-webpage"
>
DiffSegmenter
</a>
<a
class="navbar-item"
href="https://vcg-team.github.io/elbo-t2ialign-webpage"
>
ELBO-T2IAlign
</a>
</div>
</div>
</div>
</div>
</nav>
<section class="hero">
<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">
Diffusion Model is Secretly a Training-free Open Vocabulary
Semantic Segmenter
</h1>
<div class="is-size-5 publication-authors">
<span class="author-block"
><a href="https://github.com/wangjl-nb">Jinglong Wang</a
><sup>1*</sup>,</span
>
<span class="author-block"
><a href="https://github.com/Sunny599">Xiawei Li</a
><sup>1*</sup>,</span
>
<span class="author-block">
<a href="https://hellojing89.github.io/">Jing Zhang</a
><sup>1†</sup>,
</span>
<span class="author-block"
><a href="https://github.com/xu7yue">Qingyuan Xu</a
><sup>1</sup>,
</span>
<span class="author-block">
<a href="https://matrix53.github.io">Qin Zhou</a><sup>1</sup>,
</span>
<span class="author-block">
<a href="https://yuqian1023.github.io/">Qian Yu</a
><sup>1</sup>,
</span>
<span class="author-block">
<a
href="https://scholar.google.com/citations?hl=en&user=_8lB7xcAAAAJ"
>Lu Sheng</a
><sup>1</sup>,
</span>
<span class="author-block">
<a
href="https://scholar.google.com/citations?hl=en&user=7Hdu5k4AAAAJ"
>Dong Xu</a
><sup>2</sup>
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"
><sup>1</sup>Beihang University,</span
>
<span class="author-block"
><sup>2</sup>The University of Hong Kong</span
>
</div>
<div class="institution-logos" aria-label="Affiliated institutions">
<a
class="institution-logo institution-logo-beihang"
href="https://ev.buaa.edu.cn/"
target="_blank"
rel="noopener"
>
<img
src="./static/images/beihang-logo.svg"
alt="Beihang University emblem"
/>
<span class="institution-logo-text">Beihang University</span>
</a>
<a
class="institution-logo institution-logo-hku"
href="https://www.hku.hk/"
target="_blank"
rel="noopener"
>
<img
src="./static/images/hku-logo.svg"
alt="The University of Hong Kong logo"
/>
</a>
</div>
<div class="publication-status" aria-label="Publication status">
<span class="publication-venue"
>Accepted to IEEE Transactions on Image Processing (TIP),
2025</span
>
<span class="publication-awards">CCF-A</span>
</div>
<div class="is-size-7 publication-authors">
<span class="author-block"
><sup>*</sup>Equal Contribution,</span
>
<span class="author-block"
><sup>†</sup>Corresponding Author</span
>
</div>
<div class="column has-text-centered publication-link-column">
<div class="publication-links">
<span class="link-block">
<a
href="https://arxiv.org/pdf/2309.02773.pdf"
class="external-link button is-normal is-rounded is-dark"
>
<span class="icon">
<i class="fas fa-file-pdf"></i>
</span>
<span>Paper</span>
</a>
</span>
<span class="link-block">
<a
href="https://arxiv.org/abs/2309.02773"
class="external-link button is-normal is-rounded is-dark"
>
<span class="icon">
<i class="ai ai-arxiv"></i>
</span>
<span>arXiv</span>
</a>
</span>
<span class="link-block">
<a
href="https://github.com/VCG-team/DiffSegmenter"
class="external-link button is-normal is-rounded is-dark"
>
<span class="icon">
<i class="fab fa-github"></i>
</span>
<span>Code</span>
</a>
</span>
</div>
</div>
</div>
</div>
</div>
</div>
</section>
<section class="hero teaser">
<div class="container is-max-desktop">
<div class="hero-body">
<img
id="teaser"
src="./static/images/main_comparision.png"
alt="Main Comparision with Baseline"
/>
<h2 class="subtitle has-text-centered">
Segmentation score maps generated by our proposed
<span class="dnerf">DiffSegmenter</span> and previous discriminative
methods.
</h2>
</div>
</div>
</section>
<section class="hero is-light">
<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">Abstract</h2>
<div class="content has-text-justified">
<p>
Recent research has explored the utilization of pre-trained
text-image discriminative models, such as <i>CLIP</i>, to
tackle the challenges associated with open-vocabulary semantic
segmentation. However, it is worth noting that the alignment
process based on contrastive learning employed by these models
may unintentionally result in the loss of crucial localization
information and object completeness, which are essential for
achieving accurate semantic segmentation.
</p>
<p>
More recently, there has been an emerging interest in
extending the application of diffusion models beyond
text-to-image generation tasks, particularly in the domain of
semantic segmentation. These approaches utilize diffusion
models either for generating annotated data or for extracting
features to facilitate semantic segmentation. This typically
involves training segmentation models by generating a
considerable amount of synthetic data or incorporating
additional mask annotations. To this end, we uncover the
potential of generative text-to-image conditional diffusion
models as highly efficient open-vocabulary semantic
segmenters, and introduce a novel training-free approach named
<span class="dnerf">DiffSegmenter</span>.
</p>
<p>
Specifically, by feeding an input image and candidate classes
into an off-theshelf pre-trained conditional latent diffusion
model, the crossattention maps produced by the denoising
<i>U-Net</i>
are directly used as segmentation score maps, which are
further refined and completed by the followed self-attention
maps. Additionally, we carefully design effective textual
prompts and a category filtering mechanism to further enhance
the segmentation results. Extensive experiments on three
benchmark datasets show that the proposed
<span class="dnerf">DiffSegmenter</span> achieves impressive
results for open-vocabulary semantic segmentation.
</p>
</div>
</div>
</div>
</div>
</div>
</section>
<section class="hero is-small">
<div class="container is-max-desktop">
<div class="hero-body">
<div class="section-title">
<h2 class="title is-3 is-centered">Methodology</h2>
</div>
<div class="columns is-centered has-text-centered">
<div class="column">
<div class="publication-img">
<img src="static/images/pipeline.png" alt="Pipeline" />
</div>
</div>
</div>
<p>
An input image and enhanced candidate class tokens by the BLIP-based
prompt design module are fed into an off-the-shelf pre-trained
conditional latent diffusion model. The fused cross-attention maps
produced by the denoising U-Net are treated as the initial
segmentation score maps, which is further refined and completed by
the fused self-attention maps of the U-Net. Note that the parameters
of all the involved models are frozen without any tuning.
</p>
</div>
</div>
</section>
<section class="hero is-light is-small">
<div class="hero-body">
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<div class="section-title">
<h2 class="title is-3 is-centered">Better Segmentation</h2>
</div>
<div class="has-text-justified">
<p>
Here are some more segmentation results for open-vocabulary
and weakly-supervised semantic segmentation. We can see that
our proposed
<span class="dnerf">DiffSegmenter</span> can achieve better
segmentation results than previous methods.
</p>
</div>
<div class="carousel" style="overflow: hidden">
<div class="carousel-item-box">
<img
src="static/images/open_vocabulary.png"
alt="Open Vocabulary Segmentation Result"
/>
</div>
<div class="carousel-item-box">
<img
src="static/images/weakly_supervised.png"
alt="Weakly Supervised Segmentation Result"
/>
</div>
</div>
</div>
</div>
</div>
</div>
</section>
<section class="hero is-small">
<div class="hero-body">
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column is-four-fifths">
<div class="section-title">
<h2 class="title is-3 is-centered">
More Application: Improving Image Editing
</h2>
</div>
<div class="content has-text-justified">
<p>
Diffusion-based image editing methods(<a
href="https://github.com/google/prompt-to-prompt"
>Prompt-to-Prompt</a
>
etc.) highly rely on the quality of object segmentation masks.
Here we show that our proposed
<span class="dnerf">DiffSegmenter</span> can improve the
quality of such masks, and thus improve the image editing
results.
</p>
</div>
<div class="columns is-centered has-text-centered">
<div class="column">
<div class="publication-img">
<img
src="static/images/down1.png"
alt="Downstream Task Example 1"
/>
<img
src="static/images/down2.png"
alt="Downstream Task Example 2"
/>
<img
src="static/images/down3.png"
alt="Downstream Task Example 3"
/>
</div>
</div>
</div>
</div>
</div>
</div>
</div>
</section>
<section class="section" id="BibTeX">
<div class="container is-max-desktop content">
<h2 class="title">BibTeX</h2>
<pre><code>@article{wang2025diffusion,
title={Diffusion Model is Secretly a Training-Free Open Vocabulary Semantic Segmenter},
author={Wang, Jinglong and Li, Xiawei and Zhang, Jing and Xu, Qingyuan and Zhou, Qin and Yu, Qian and Sheng, Lu and Xu, Dong},
journal={IEEE Transactions on Image Processing},
volume={34},
pages={1895--1907},
year={2025},
doi={10.1109/TIP.2025.3551648}
}</code></pre>
</div>
</section>
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