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<!DOCTYPE html>
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<h2>Beyond Universal Saliency: Personalized Saliency Prediction with Multi-task CNN</h2>
</center>
<center>
<h3>
<a href="">Yanyu Xu</a>
<a href="">Nianyi Li</a>
<a href="">Junru Wu</a>
<a href="">Jingyi Yu</a>
<a href="">Shenghua Gao</a>
</h3>
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<h3>
<a href="http://www.shanghaitech.edu.cn" target="_blank">ShanghaiTech University</a>
</h3>
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<h3 style="color: #000000">In IJCAI 2017 (Best Student Paper Run-upper)</h2>
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<h3>
<a href="https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7780439" target="_blank" style="color: #990000">[Paper]</a>
<!-- <a href="https://github.com/StevenLiuWen/ano_pred_cvpr2018" target="_blank" style="color: #990000">[Code (Tensorflow)]</a> -->
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<h2>Abstract</h2>
<div style="font-size:14px">
<p>Saliency detection is a long standing problem in computer vision. Tremendous efforts have been focused
on exploring a universal saliency model across users despite their differences in gender, race,
age, etc. Yet recent psychology studies suggest that saliency is highly specific than universal:
individuals exhibit heterogeneous gaze patterns when viewing an identical scene containing multiple
salient objects.
</p>
</div>
<h2>Introduction</h2>
<div style="font-size:14px">
<p>In this paper, we first show that such heterogeneity is common and critical for reliable saliency
prediction. Our study also produces the first database of personalized saliency maps (PSMs).
We model PSM based on universal saliency map (USM) shared by different participants and adopt
a multi-task CNN framework to estimate the discrepancy between PSM and USM. Comprehensive experiments
demonstrate that our new PSM model and prediction scheme are effective and reliable.
</p>
</div>
<h2>FrameWork</h2>
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<img src="../img/project/ijcai2017_xuyy1_2.png" width="1000">
</center>
<h2>Citation</h2>
If you find this useful, please cite our work as follows:
<pre>
@inproceedings{xu2017beyond,
title={Beyond universal saliency: personalized saliency prediction with multi-task CNN},
author={Xu, Yanyu and Li, Nianyi and Wu, Junru and Yu, Jingyi and Gao, Shenghua},
booktitle={Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence},
pages={3887--3893},
year={2017}
}</pre>
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