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<title>Sketching without Worrying: Noise-Tolerant Sketch-Based Image Retrieval</title>
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<!-- <center><span style="font-size:44px;font-weight:bold;"></span></center><br/> -->
<center><span style="font-size:44px;font-weight:bold;">Sketching without Worrying:</span>
</center>
<center><span style="font-size:32px;font-weight:bold;">Noise-Tolerant Sketch-Based Image Retrieval</span>
</center><br />
<table align=center width=1200px>
<tr>
<td align=center width=270px>
<center><span style="font-size:22px"><a href="http://ayankumarbhunia.github.io/" target="_blank">Ayan
Kumar Bhunia</a></span></center>
</td>
<td align=center width=200px>
<center><span style="font-size:22px"><a href="https://subhadeepkoley.github.io/"
target="_blank">Subhadeep Koley</a></span></center>
</td>
<td align=center width=180px>
<center><span style="font-size:22px"><a
href="https://scholar.google.com/citations?user=9C5TEbkAAAAJ&hl=en/"
target="_blank">Abdullah Khilji</a></span></center>
</td>
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<center><span style="font-size:22px"><a href="https://aneeshan95.github.io/" target="_blank">Aneeshan
Sain</a></span></center>
</td>
<td align=center width=200px>
<center><span style="font-size:22px"><a href="https://rohit-kundu.github.io//" target="_blank">Pinaki
Chowdhury</a></span></center>
</td>
<td align=center width=130px>
<center><span style="font-size:22px"><a
href="http://personal.ee.surrey.ac.uk/Personal/T.Xiang/index.html/" target="_blank">Tao
Xiang</a></span></center>
</td>
<td align=center width=180px>
<center><span style="font-size:22px"><a href="http://personal.ee.surrey.ac.uk/Personal/Y.Song/"
target="_blank">Yi-Zhe Song</a></span></center>
</td>
<tr />
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<!-- <center><span style="font-size:20px">SketchX, CVSSP, University of Surrey, United Kingdom</span></center></td>-->
<!-- <td align=center width=250px>-->
<!-- <center><span style="font-size:20px">CMU</span></center></td>-->
<!-- <td align=center width=150px>-->
<!-- <center><span style="font-size:20px">CMU/FAIR</span></center></td>-->
<!-- <td align=center width=150px>-->
<!-- <center><span style="font-size:20px">UIUC</span></center></td>-->
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<center><span style="font-size:22px">SketchX, Centre for Vision Speech and Signal Processing,
<br /> University of Surrey, United Kingdom </span></center>
</td>
<tr />
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<tr>
<td align=center width=700px>
<center><span style="font-size:22px">Published at <a href="http://cvpr2022.thecvf.com/">CVPR
2022</a> </span></center>
</td>
<tr />
</table><br />
<table align=center width=700px>
<tr>
<td align=center width=70px>
<center><span style="font-size:28px"><a
href="https://arxiv.org/pdf/2203.14817v1.pdf">[Paper]</a></span></center>
</td>
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<td align=center width=100px>
<center><span style="font-size:28px"><a
href="https://github.com/AyanKumarBhunia/NoiseTolerant-SBIR">[GitHub]</a></span>
</center>
</td>
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<center><img src="./images/fig1.JPG" height="300px"></img><br></center>
</td>
</tr>
</table>
<br>
<div style="width:800px; margin:0 auto; text-align=justify">
Figure: (a) While the average ranking percentile increases as the sketching proceeds from starting
towards completion, unwanted sudden drops have been noticed for many individual sketches due to
noisy/irrelevant strokes drawn. (b) The same thing is visualised with number of samples in the third
axis to get an overall statistics on QMUL-Shoe-V2 dataset.</div>
<br>
<hr>
<center>
<h1>Framework</h1>
</center>
<div style="width:800px; margin:0 auto; text-align=justify">
Sketching enables many exciting applications, notably, image retrieval. The fear-to-sketch problem
(i.e., "I can't sketch") has however proven to be fatal for its widespread adoption. This paper tackles
this "fear" head on, and for the first time, proposes an auxiliary module for existing retrieval models
that predominantly lets the users sketch without having to worry. We first conducted a pilot study that
revealed the secret lies in the existence of noisy strokes, but not so much of the "I can't sketch". We
consequently design a stroke subset selector that detects noisy strokes, leaving only those which make a
positive contribution towards successful retrieval. Our Reinforcement Learning based formulation
quantifies the importance of each stroke present in a given subset, based on the extend to which that
stroke contributes to retrieval. When combined with pre-trained retrieval models as a pre-processing
module, we achieve a significant gain of 8%-10% over standard baselines and in turn report new
state-of-the-art performance. Last but not least, we demonstrate the selector once trained, can also be
used in a plug-and-play manner to empower various sketch applications in ways that were not previously
possible.
</div><br />
<table align=center width=1000px>
<p style="margin-top:4px;"></p>
<tr>
<td width=1200px>
<center><a href="./images/fig2.JPG"><img src="./images/fig2.JPG" width="800px"></img></a><br>
</center>
</td>
</tr>
</table>
Illustration of Noise Tolerant FG-SBIR framework. Stroke Subset Selector X(·) acts as a pre-processing
module in the sketch vector space to eliminate the noisy strokes. Selected stroke subset is then
rasterized and fed through an existing pre-trained FG-SBIR model for reward calculation, which is
optimised by Proximal Policy Optimisation. For brevity, actor-only version is shown here.
<br />
<hr>
<!--https://youtu.be/JVyFexPEu-U-->
<table align=center width=850px>
<center>
<h1>Bibtex</h1>
</center>
<tr>
<td width=200px align=left>
<!-- <p style="margin-top:4px;"></p> -->
<!-- <a href="https://arxiv.org/pdf/2002.10310.pdf"><img style="width:200px" src="./images/4367-teaser.gif"/></a> -->
<center>
<!-- <span style="font-size:20pt"><a href="https://arxiv.org/pdf/2002.10310.pdf">[Paper]</a> -->
<!-- <span style="font-size:20pt"><a href="https://arxiv.org/abs/1902.05546v2">[ArXiv]</a>-->
<!-- <span style="font-size:20pt"><a href="resources/slides.pdf">[Slides]</a></span>-->
<!-- <span style="font-size:20pt"><a href="resources/poster.pdf">[Poster]</a></span>-->
</center>
</td>
<td width=50px align=center>
</td>
<td width=550px align=left>
<!-- <p style="margin-top:4px;"></p> -->
<!-- Chaplot, D.S., Gandhi, D., Gupta, S., Gupta, A. and Salakhutdinov, R., 2020. Learning To Explore Using Active Neural SLAM. In International Conference on Learning Representations (ICLR).-->
<p style="text-align:left;"><b><span style="font-size:20pt">Citation</span></b><br /><span
style="font-size:6px;"> <br /></span> <span style="font-size:15pt"> Sketching
without Worrying: Noise-Tolerant Sketch-Based Image Retrieval. In
CVPR 2022.</span></p>
<!-- <p style="margin-top:20px;"></p> -->
<span style="font-size:20pt"><a shape="rect" href="javascript:togglebib('assemblies19_bib')"
class="togglebib">[Bibtex]</a></span>
<div class="paper" id="assemblies19_bib">
<pre xml:space="preserve">
@InProceedings{strokesubset,
author = {Ayan Kumar Bhunia and Subhadeep Koley and Abdullah Faiz Ur Rahman Khilji and Aneeshan Sain and Pinaki Nath Chowdhury and Tao Xiang and Yi-Zhe Song},
title = {Sketching without Worrying: Noise-Tolerant Sketch-Based Image Retrieval},
booktitle = {The IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
month = {June},
year = {2022}
}
</pre>
</div>
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<center><a href="./images/4367-teaser.gif"><img src = "./images/4367-teaser.gif" width="512px"></img></a><br></center>
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<!-- <center><h1>Acknowledgements</h1></center>
<br>
Website template from <a href="https://richzhang.github.io/colorization">here</a> and <a href="https://pathak22.github.io/modular-assemblies/">here</a>. <br>
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