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<section class="hero">
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<div class="container is-max-desktop">
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<!-- TODO: Replace with your paper title -->
<h1 class="title is-1 publication-title">
CAReFuseNet: Cross Attention Fusion Network for Referring Camouflaged Object Detection
</h1>
<div class="is-size-5 publication-authors">
<!-- TODO: Replace with your paper authors and their personal links -->
<span class="author-block">
<a href="https://in.linkedin.com/in/sekhar-mandalaparthi" target="_blank">Sekhar Mandalaparthi</a><sup>1,2</sup>,</span>
<span class="author-block">
<a href="https://scholar.google.com/citations?user=xqJYZ00AAAAJ&hl=en" target="_blank">Arup Dasgupta</a><sup>1,2</sup>,</span>
<span class="author-block">
<a href="https://anirbanchakraborty.github.io/" target="_blank">Anirban Chakraborty</a><sup>3</sup>
</span>
</div>
<div class="is-size-5 publication-authors">
<!-- TODO: Replace with your institution and conference/journal info -->
<span class="author-block"><sup>1</sup>Homi Bhabha National Institute</span>
<span class="author-block"><sup>2</sup>Indira Gandhi Centre for Atomic Research</span>
<span class="author-block"><sup>3</sup>Indian Institute of Science</span>
<br>
<!-- TODO: Remove this line if no equal contribution -->
<!--<span class="eql-cntrb"><small><br><sup>*</sup>Indicates Equal Contribution</small></span> -->
<span class="paper-block">
<b style="color: #f41c1c;">TMLR 2026</b>
</span>
</div>
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</span>
<span>YouTube</span>
</a>
</span>
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<span>Paper</span>
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<span>Code</span>
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<!-- Figure: First (teaser) figure -->
<figure class="result-figure narrow">
<img src="static/images/CAReFuseNet_Fig1.png" alt="CAReFuseNet Capability">
<figcaption>
Despite variation in pose, appearance, shape and size between reference and target object instances, our CAReFuseNet segments camouflaged objects in the input image under the guidance of the salient reference image.
</figcaption>
</figure>
</div>
</div>
</div>
</section>
<!-- End teaser -->
<!-- Paper abstract -->
<section class="section hero is-light">
<div class="container is-max-desktop">
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<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<!-- TODO: Replace with your paper abstract -->
<p>
The Referring Camouflaged Object Detection (Ref-COD) task aims to generate a binary segmentation mask to detect camouflaged objects
of a specified category in an image, guided by reference image(s) containing salient example(s) of the target object of the same category.
With only a few methods (e.g., R2CNet and UAT) proposed to date, Ref-COD remains challenging due to the similarity of camouflaged objects
to their backgrounds and substantial feature gaps with salient references. At the same time, recent state-of-the-art approaches often
rely on heavy transformer-based encoder–decoder stacks or large frozen vision backbones, resulting in substantial parameter footprints
that hinder efficient deployment.
<br>
This work proposes `CAReFuseNet’, a novel framework featuring a cross-attention based reference feature fusion module that effectively extracts
reference-conditioned feature representations from camouflaged images while targeting parameter efficiency. The proposed CAReFuse module
leverages global interactions between reference and camouflaged image features via cross-attention, but constrains all fusion and decoding operations
to a lower dimensional feature space and employs a lightweight convolutional decoder. Combined with a frozen Ref-Image Encoder,
this design yields a compact Ref-COD model without sacrificing accuracy.
<br>
Extensive experiments on the R2C7K dataset show that our method achieves competitive performance against the state-of-the-art,
while using significantly fewer parameters. Further evaluations across multiple backbone architectures, including Swin Transformer,
ConvNeXt, EfficientNet, and ResNet, demonstrate that the proposed reference feature fusion module provides a general and parameter-efficient
building block for the referring camouflaged object detection task.
</p>
</div>
</div>
</div>
</div>
</section>
<!-- End paper abstract -->
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<h2 class="title is-3">Method</h2>
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<!-- Figure: CAReFuseNet Architecture -->
<figure class="result-figure">
<img src="static/images/CAReFuseNet.png" alt="CAReFuseNet Architecture">
<figcaption>
<strong>Overall architecture of CAReFuseNet:</strong>
Ref-Image Encoder extracts features from reference images
containing salient target objects. Ref-Feature Combiner
combines individual reference image features to form a common feature
representation, <em>E</em>, of the target object.
Camo-Image Encoder extracts multi-scale features from
input camouflaged image <em>I<sup> camo</sup></em>.
CAReFuse Module fuses feature representations of the
salient target object with multi-scale features of
<em>I<sup> camo</sup></em>. Segmentation Decoder
generates a segmentation mask using the reference-conditioned feature
representations.
</figcaption>
</figure>
<br>
<!-- Figure: CAReFuse Module -->
<figure class="result-figure">
<img src="static/images/CAReFuse_Module.png" alt="CAReFuse Module">
<figcaption>
<strong>CAReFuse Module</strong>: cross-attention-based feature
fusion module that fuses reference image features with multi-scale
camouflaged image features. (a) <em>Cross-Attention Fusion (CAF)
Module:</em> fuses reference image features <em>E</em> with multi-scale
camouflaged image features <em>F</em><sub>1</sub>, <em>F</em><sub>2</sub>,
<em>F</em><sub>3</sub>, and <em>F</em><sub>4</sub> individually.
(b) <em>Cross-Attention (CA) Module:</em> performs multi-head
cross-attention using camouflaged image features for <em>queries</em> and
reference image features for <em>keys</em> and <em>values</em>.
(c) <em>Multi-Scale Fusion (MSF) Module:</em> fuses
reference-conditioned multi-scale features of the camouflaged image into
a single feature volume through cross-scale fusion.
(d) <em>Cross-Scale Fusion (CSF) Module:</em> fuses a feature volume
of lower spatial dimension with a feature volume of higher spatial
dimension through convolution and upsampling operations.
⊕ — element-wise sum, ⊗ — element-wise multiplication, and
ⓒ — concatenation operation.
</figcaption>
</figure>
</div>
</div>
</div>
</section>
<!-- End Method -->
<!-- Results -->
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<div class="container is-max-desktop">
<div class=hero-body>
<h2 class="title is-3">Results</h2>
<h3 class="subtitle">Quantitative Results:</h3>
<div class="content has-text-justified">
<!-- Table: Comparison with other Ref-COD models -->
<figure class="result-figure">
<img src="static/images/model_comparison.png" alt="Model Comparison">
<figcaption>
<strong>Comparison with other Ref-COD models.</strong>
<em>-Ref</em>: R2CNet's referring framework applied to the existing COD
model (results taken from <a href="#">Zhang et al., (2025)</a>).
<em>Tr.Params</em>: Number of trainable parameters in millions.
<em>Tot.Params</em>: Number of total parameters in millions.
<em>MACs</em>: Multiply-accumulate operations in billions.
↑: the higher the better.
↓: the lower the better.
Settings: image size 352 × 352, number of reference images <em>K</em> = 5.
For Ours, metrics are reported as mean (± std) over three random seed runs.
The best two values are shown in <strong>bold</strong> and
<u>underline</u>, respectively. Results of R2CNet and UAT are taken from
their respective prior works.
</figcaption>
</figure>
<br>
<!-- Table: Comparison with CIRCOD -->
<figure class="result-figure narrow">
<img src="static/images/comparison_with_circod.png" alt="Comparison with CIRCOD">
<figcaption>
<strong>Comparison with CIRCOD (under the Ref-COD setting):</strong> Settings: image size
512 × 512, number of reference images <em>K</em> = 1. The best two values
are shown in <strong>bold</strong> and <u>underline</u>, respectively.
The results of CIRCOD are taken from the prior work
<a href="#">Gupta et al., (2025)</a>.
</figcaption>
</figure>
<br>
<!-- Table: Ablation on cd -->
<figure class="result-figure">
<img src="static/images/ablation_on_cd.png" alt="Ablation on Cd">
<figcaption>
<strong>Ablation on channel dimension (i.e., <em>c</em><sub>d</sub>):</strong> The best values
are shown in <strong>bold</strong>. 256 is chosen as the default channel dimension, striking a balance between
performance and computational efficiency.
</figcaption>
</figure>
<br>
<!-- Figure: Ablation on K -->
<figure class="result-figure">
<img src="static/images/ablation_on_K.svg" alt="Ablation on K">
<figcaption>
<strong>Ablation on <em>K</em>:</strong> Comparison among Ours-P, UAT, and R2CNet with respect to the ablation
experiment on the number of reference images (i.e., <em>K</em>), where
<em>K</em> is varied consistently between training and testing.
</figcaption>
</figure>
<br>
<!-- Two images side by side (stacked on mobile) -->
<div class="image-pair">
<figure class="result-figure">
<img src="static/images/ablation_on_RefFeatCombiner.png" alt="Ablation on Ref-Feat-Combiner">
<figcaption>
<strong>Ablation on Ref Feature Combiner:</strong>
<em>CA</em>: Channel Attention, <em>SA</em>: Spatial Attention, <em>CWA</em>: Channel-wise Average.
Metrics are reported as mean(±std) over three random seed runs.
</figcaption>
</figure>
<figure class="result-figure">
<img src="static/images/ablation_on_CarefuseModule.png" alt="Ablation on CAReFuse module">
<figcaption>
<strong>Ablation on CAReFuse module:</strong>
<em>CAF</em>: Cross Attention Fusion, <em>MSF</em>: Multi Scale Fusion.
Metrics are reported as mean(±std) over three random seed runs.
</figcaption>
</figure>
</div>
</div>
<br>
<h3 class="subtitle">Qualitative Results:</h3>
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<!-- Figure: Qualitative Evaluation -->
<figure class="result-figure">
<img src="static/images/Qualitative_Eval.svg" alt="Qualitative Evaluation">
<figcaption>
<strong>Qaulitative Evaluation:</strong> Visual comparison between the predicted masks of CAReFuseNet (Ours-P) and other Ref-COD methods (<em>i.e.</em> R2CNet, UAT).
Our method outperforms the rest.
</figcaption>
</figure>
<br>
<!-- Figure: High quality edges -->
<figure class="result-figure narrow">
<img src="static/images/High_Quality_Edges.svg" alt="High quality edges">
<figcaption>
<strong>High Quality masks:</strong> Comparison between the predicted masks of R2CNet, UAT, and Ours-P for camouflaged objects with high-frequency edges,
highlighting the superior mask quality and finer detail capture of Ours-P.
</figcaption>
</figure>
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</section>
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<h2 class="title">BibTeX</h2>
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<pre id="bibtex-code">
<code>@article{sekhar2026carefusenet,
title={{CAReFuseNet}: Cross Attention Fusion Network for Referring Camouflaged Object Detection},
author={Sekhar Mandalaparthi and Arup Dasgupta and Anirban Chakraborty},
journal={Transactions on Machine Learning Research},
year={2026},
url={https://vcl-iisc.github.io/CAReFuseNet/}
}</code>
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