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    <title>Vision and Learning Lab @ UAlberta</title>
    <link>https://vision-and-learning-lab-ualberta.github.io/</link>
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    <description>Vision and Learning Lab @ UAlberta</description>
    <generator>Source Themes Academic (https://sourcethemes.com/academic/)</generator><language>en-us</language><copyright>© Vision and Learning Lab, Univerity of Alberta 2022</copyright><lastBuildDate>Tue, 04 Jan 2022 00:00:00 +0000</lastBuildDate>
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      <title>Vision and Learning Lab @ UAlberta</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/</link>
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    <item>
      <title>Example Page 1</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/courses/example/example1/</link>
      <pubDate>Sun, 05 May 2019 00:00:00 +0100</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/courses/example/example1/</guid>
      <description>&lt;p&gt;In this tutorial, I&amp;rsquo;ll share my top 10 tips for getting started with Academic:&lt;/p&gt;
&lt;h2 id=&#34;tip-1&#34;&gt;Tip 1&lt;/h2&gt;
&lt;p&gt;Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum. Sed ac faucibus dolor, scelerisque sollicitudin nisi. Cras purus urna, suscipit quis sapien eu, pulvinar tempor diam. Quisque risus orci, mollis id ante sit amet, gravida egestas nisl. Sed ac tempus magna. Proin in dui enim. Donec condimentum, sem id dapibus fringilla, tellus enim condimentum arcu, nec volutpat est felis vel metus. Vestibulum sit amet erat at nulla eleifend gravida.&lt;/p&gt;
&lt;p&gt;Nullam vel molestie justo. Curabitur vitae efficitur leo. In hac habitasse platea dictumst. Sed pulvinar mauris dui, eget varius purus congue ac. Nulla euismod, lorem vel elementum dapibus, nunc justo porta mi, sed tempus est est vel tellus. Nam et enim eleifend, laoreet sem sit amet, elementum sem. Morbi ut leo congue, maximus velit ut, finibus arcu. In et libero cursus, rutrum risus non, molestie leo. Nullam congue quam et volutpat malesuada. Sed risus tortor, pulvinar et dictum nec, sodales non mi. Phasellus lacinia commodo laoreet. Nam mollis, erat in feugiat consectetur, purus eros egestas tellus, in auctor urna odio at nibh. Mauris imperdiet nisi ac magna convallis, at rhoncus ligula cursus.&lt;/p&gt;
&lt;p&gt;Cras aliquam rhoncus ipsum, in hendrerit nunc mattis vitae. Duis vitae efficitur metus, ac tempus leo. Cras nec fringilla lacus. Quisque sit amet risus at ipsum pharetra commodo. Sed aliquam mauris at consequat eleifend. Praesent porta, augue sed viverra bibendum, neque ante euismod ante, in vehicula justo lorem ac eros. Suspendisse augue libero, venenatis eget tincidunt ut, malesuada at lorem. Donec vitae bibendum arcu. Aenean maximus nulla non pretium iaculis. Quisque imperdiet, nulla in pulvinar aliquet, velit quam ultrices quam, sit amet fringilla leo sem vel nunc. Mauris in lacinia lacus.&lt;/p&gt;
&lt;p&gt;Suspendisse a tincidunt lacus. Curabitur at urna sagittis, dictum ante sit amet, euismod magna. Sed rutrum massa id tortor commodo, vitae elementum turpis tempus. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Aenean purus turpis, venenatis a ullamcorper nec, tincidunt et massa. Integer posuere quam rutrum arcu vehicula imperdiet. Mauris ullamcorper quam vitae purus congue, quis euismod magna eleifend. Vestibulum semper vel augue eget tincidunt. Fusce eget justo sodales, dapibus odio eu, ultrices lorem. Duis condimentum lorem id eros commodo, in facilisis mauris scelerisque. Morbi sed auctor leo. Nullam volutpat a lacus quis pharetra. Nulla congue rutrum magna a ornare.&lt;/p&gt;
&lt;p&gt;Aliquam in turpis accumsan, malesuada nibh ut, hendrerit justo. Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Quisque sed erat nec justo posuere suscipit. Donec ut efficitur arcu, in malesuada neque. Nunc dignissim nisl massa, id vulputate nunc pretium nec. Quisque eget urna in risus suscipit ultricies. Pellentesque odio odio, tincidunt in eleifend sed, posuere a diam. Nam gravida nisl convallis semper elementum. Morbi vitae felis faucibus, vulputate orci placerat, aliquet nisi. Aliquam erat volutpat. Maecenas sagittis pulvinar purus, sed porta quam laoreet at.&lt;/p&gt;
&lt;h2 id=&#34;tip-2&#34;&gt;Tip 2&lt;/h2&gt;
&lt;p&gt;Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum. Sed ac faucibus dolor, scelerisque sollicitudin nisi. Cras purus urna, suscipit quis sapien eu, pulvinar tempor diam. Quisque risus orci, mollis id ante sit amet, gravida egestas nisl. Sed ac tempus magna. Proin in dui enim. Donec condimentum, sem id dapibus fringilla, tellus enim condimentum arcu, nec volutpat est felis vel metus. Vestibulum sit amet erat at nulla eleifend gravida.&lt;/p&gt;
&lt;p&gt;Nullam vel molestie justo. Curabitur vitae efficitur leo. In hac habitasse platea dictumst. Sed pulvinar mauris dui, eget varius purus congue ac. Nulla euismod, lorem vel elementum dapibus, nunc justo porta mi, sed tempus est est vel tellus. Nam et enim eleifend, laoreet sem sit amet, elementum sem. Morbi ut leo congue, maximus velit ut, finibus arcu. In et libero cursus, rutrum risus non, molestie leo. Nullam congue quam et volutpat malesuada. Sed risus tortor, pulvinar et dictum nec, sodales non mi. Phasellus lacinia commodo laoreet. Nam mollis, erat in feugiat consectetur, purus eros egestas tellus, in auctor urna odio at nibh. Mauris imperdiet nisi ac magna convallis, at rhoncus ligula cursus.&lt;/p&gt;
&lt;p&gt;Cras aliquam rhoncus ipsum, in hendrerit nunc mattis vitae. Duis vitae efficitur metus, ac tempus leo. Cras nec fringilla lacus. Quisque sit amet risus at ipsum pharetra commodo. Sed aliquam mauris at consequat eleifend. Praesent porta, augue sed viverra bibendum, neque ante euismod ante, in vehicula justo lorem ac eros. Suspendisse augue libero, venenatis eget tincidunt ut, malesuada at lorem. Donec vitae bibendum arcu. Aenean maximus nulla non pretium iaculis. Quisque imperdiet, nulla in pulvinar aliquet, velit quam ultrices quam, sit amet fringilla leo sem vel nunc. Mauris in lacinia lacus.&lt;/p&gt;
&lt;p&gt;Suspendisse a tincidunt lacus. Curabitur at urna sagittis, dictum ante sit amet, euismod magna. Sed rutrum massa id tortor commodo, vitae elementum turpis tempus. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Aenean purus turpis, venenatis a ullamcorper nec, tincidunt et massa. Integer posuere quam rutrum arcu vehicula imperdiet. Mauris ullamcorper quam vitae purus congue, quis euismod magna eleifend. Vestibulum semper vel augue eget tincidunt. Fusce eget justo sodales, dapibus odio eu, ultrices lorem. Duis condimentum lorem id eros commodo, in facilisis mauris scelerisque. Morbi sed auctor leo. Nullam volutpat a lacus quis pharetra. Nulla congue rutrum magna a ornare.&lt;/p&gt;
&lt;p&gt;Aliquam in turpis accumsan, malesuada nibh ut, hendrerit justo. Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Quisque sed erat nec justo posuere suscipit. Donec ut efficitur arcu, in malesuada neque. Nunc dignissim nisl massa, id vulputate nunc pretium nec. Quisque eget urna in risus suscipit ultricies. Pellentesque odio odio, tincidunt in eleifend sed, posuere a diam. Nam gravida nisl convallis semper elementum. Morbi vitae felis faucibus, vulputate orci placerat, aliquet nisi. Aliquam erat volutpat. Maecenas sagittis pulvinar purus, sed porta quam laoreet at.&lt;/p&gt;
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    <item>
      <title>Example Page 2</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/courses/example/example2/</link>
      <pubDate>Sun, 05 May 2019 00:00:00 +0100</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/courses/example/example2/</guid>
      <description>&lt;p&gt;Here are some more tips for getting started with Academic:&lt;/p&gt;
&lt;h2 id=&#34;tip-3&#34;&gt;Tip 3&lt;/h2&gt;
&lt;p&gt;Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum. Sed ac faucibus dolor, scelerisque sollicitudin nisi. Cras purus urna, suscipit quis sapien eu, pulvinar tempor diam. Quisque risus orci, mollis id ante sit amet, gravida egestas nisl. Sed ac tempus magna. Proin in dui enim. Donec condimentum, sem id dapibus fringilla, tellus enim condimentum arcu, nec volutpat est felis vel metus. Vestibulum sit amet erat at nulla eleifend gravida.&lt;/p&gt;
&lt;p&gt;Nullam vel molestie justo. Curabitur vitae efficitur leo. In hac habitasse platea dictumst. Sed pulvinar mauris dui, eget varius purus congue ac. Nulla euismod, lorem vel elementum dapibus, nunc justo porta mi, sed tempus est est vel tellus. Nam et enim eleifend, laoreet sem sit amet, elementum sem. Morbi ut leo congue, maximus velit ut, finibus arcu. In et libero cursus, rutrum risus non, molestie leo. Nullam congue quam et volutpat malesuada. Sed risus tortor, pulvinar et dictum nec, sodales non mi. Phasellus lacinia commodo laoreet. Nam mollis, erat in feugiat consectetur, purus eros egestas tellus, in auctor urna odio at nibh. Mauris imperdiet nisi ac magna convallis, at rhoncus ligula cursus.&lt;/p&gt;
&lt;p&gt;Cras aliquam rhoncus ipsum, in hendrerit nunc mattis vitae. Duis vitae efficitur metus, ac tempus leo. Cras nec fringilla lacus. Quisque sit amet risus at ipsum pharetra commodo. Sed aliquam mauris at consequat eleifend. Praesent porta, augue sed viverra bibendum, neque ante euismod ante, in vehicula justo lorem ac eros. Suspendisse augue libero, venenatis eget tincidunt ut, malesuada at lorem. Donec vitae bibendum arcu. Aenean maximus nulla non pretium iaculis. Quisque imperdiet, nulla in pulvinar aliquet, velit quam ultrices quam, sit amet fringilla leo sem vel nunc. Mauris in lacinia lacus.&lt;/p&gt;
&lt;p&gt;Suspendisse a tincidunt lacus. Curabitur at urna sagittis, dictum ante sit amet, euismod magna. Sed rutrum massa id tortor commodo, vitae elementum turpis tempus. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Aenean purus turpis, venenatis a ullamcorper nec, tincidunt et massa. Integer posuere quam rutrum arcu vehicula imperdiet. Mauris ullamcorper quam vitae purus congue, quis euismod magna eleifend. Vestibulum semper vel augue eget tincidunt. Fusce eget justo sodales, dapibus odio eu, ultrices lorem. Duis condimentum lorem id eros commodo, in facilisis mauris scelerisque. Morbi sed auctor leo. Nullam volutpat a lacus quis pharetra. Nulla congue rutrum magna a ornare.&lt;/p&gt;
&lt;p&gt;Aliquam in turpis accumsan, malesuada nibh ut, hendrerit justo. Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Quisque sed erat nec justo posuere suscipit. Donec ut efficitur arcu, in malesuada neque. Nunc dignissim nisl massa, id vulputate nunc pretium nec. Quisque eget urna in risus suscipit ultricies. Pellentesque odio odio, tincidunt in eleifend sed, posuere a diam. Nam gravida nisl convallis semper elementum. Morbi vitae felis faucibus, vulputate orci placerat, aliquet nisi. Aliquam erat volutpat. Maecenas sagittis pulvinar purus, sed porta quam laoreet at.&lt;/p&gt;
&lt;h2 id=&#34;tip-4&#34;&gt;Tip 4&lt;/h2&gt;
&lt;p&gt;Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum. Sed ac faucibus dolor, scelerisque sollicitudin nisi. Cras purus urna, suscipit quis sapien eu, pulvinar tempor diam. Quisque risus orci, mollis id ante sit amet, gravida egestas nisl. Sed ac tempus magna. Proin in dui enim. Donec condimentum, sem id dapibus fringilla, tellus enim condimentum arcu, nec volutpat est felis vel metus. Vestibulum sit amet erat at nulla eleifend gravida.&lt;/p&gt;
&lt;p&gt;Nullam vel molestie justo. Curabitur vitae efficitur leo. In hac habitasse platea dictumst. Sed pulvinar mauris dui, eget varius purus congue ac. Nulla euismod, lorem vel elementum dapibus, nunc justo porta mi, sed tempus est est vel tellus. Nam et enim eleifend, laoreet sem sit amet, elementum sem. Morbi ut leo congue, maximus velit ut, finibus arcu. In et libero cursus, rutrum risus non, molestie leo. Nullam congue quam et volutpat malesuada. Sed risus tortor, pulvinar et dictum nec, sodales non mi. Phasellus lacinia commodo laoreet. Nam mollis, erat in feugiat consectetur, purus eros egestas tellus, in auctor urna odio at nibh. Mauris imperdiet nisi ac magna convallis, at rhoncus ligula cursus.&lt;/p&gt;
&lt;p&gt;Cras aliquam rhoncus ipsum, in hendrerit nunc mattis vitae. Duis vitae efficitur metus, ac tempus leo. Cras nec fringilla lacus. Quisque sit amet risus at ipsum pharetra commodo. Sed aliquam mauris at consequat eleifend. Praesent porta, augue sed viverra bibendum, neque ante euismod ante, in vehicula justo lorem ac eros. Suspendisse augue libero, venenatis eget tincidunt ut, malesuada at lorem. Donec vitae bibendum arcu. Aenean maximus nulla non pretium iaculis. Quisque imperdiet, nulla in pulvinar aliquet, velit quam ultrices quam, sit amet fringilla leo sem vel nunc. Mauris in lacinia lacus.&lt;/p&gt;
&lt;p&gt;Suspendisse a tincidunt lacus. Curabitur at urna sagittis, dictum ante sit amet, euismod magna. Sed rutrum massa id tortor commodo, vitae elementum turpis tempus. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Aenean purus turpis, venenatis a ullamcorper nec, tincidunt et massa. Integer posuere quam rutrum arcu vehicula imperdiet. Mauris ullamcorper quam vitae purus congue, quis euismod magna eleifend. Vestibulum semper vel augue eget tincidunt. Fusce eget justo sodales, dapibus odio eu, ultrices lorem. Duis condimentum lorem id eros commodo, in facilisis mauris scelerisque. Morbi sed auctor leo. Nullam volutpat a lacus quis pharetra. Nulla congue rutrum magna a ornare.&lt;/p&gt;
&lt;p&gt;Aliquam in turpis accumsan, malesuada nibh ut, hendrerit justo. Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Quisque sed erat nec justo posuere suscipit. Donec ut efficitur arcu, in malesuada neque. Nunc dignissim nisl massa, id vulputate nunc pretium nec. Quisque eget urna in risus suscipit ultricies. Pellentesque odio odio, tincidunt in eleifend sed, posuere a diam. Nam gravida nisl convallis semper elementum. Morbi vitae felis faucibus, vulputate orci placerat, aliquet nisi. Aliquam erat volutpat. Maecenas sagittis pulvinar purus, sed porta quam laoreet at.&lt;/p&gt;
</description>
    </item>
    
    <item>
      <title>Investigating Pose Representations and Motion Contexts Modeling for 3D Motion Prediction</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/shuang_investigating_2022/</link>
      <pubDate>Tue, 04 Jan 2022 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/shuang_investigating_2022/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Our paper &#34;Investigating Pose Representations and Motion Contexts Modeling for 3D Motion Prediction&#34; is accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence!</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/post/shuang_tpami2022/</link>
      <pubDate>Tue, 04 Jan 2022 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/post/shuang_tpami2022/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Joint Semantic Mining for Weakly Supervised RGB-D Salient Object Detection</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/jingjing_joint_2021/</link>
      <pubDate>Mon, 06 Dec 2021 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/jingjing_joint_2021/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Our paper &#34;Joint Semantic Mining for Weakly Supervised RGB-D Salient Object Detection&#34; is accepted by NeurIPS 2021</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/post/jingjing_neurips2021/</link>
      <pubDate>Mon, 06 Dec 2021 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/post/jingjing_neurips2021/</guid>
      <description></description>
    </item>
    
    <item>
      <title>3D Pose Estimation and Future Motion Prediction from 2D Images</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/ji_3d_2021/</link>
      <pubDate>Mon, 01 Nov 2021 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/ji_3d_2021/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Our paper &#34;3D pose estimation and future motion prediction from 2D images&#34; is accepted by Pattern Recogntion</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/post/ji_pr_2021/</link>
      <pubDate>Mon, 01 Nov 2021 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/post/ji_pr_2021/</guid>
      <description></description>
    </item>
    
    <item>
      <title>CHASE: Robust Visual Tracking via Cell-Level Differentiable Neural Architecture Search</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/mojtaba_chase_2021/</link>
      <pubDate>Sun, 26 Sep 2021 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/mojtaba_chase_2021/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Enhancing Human Motion Assessment by Self-supervised Representation Learning</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/mahdiar_enhancing_2021/</link>
      <pubDate>Sun, 26 Sep 2021 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/mahdiar_enhancing_2021/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Our paper &#34;CHASE: Robust Visual Tracking via Cell-Level Differentiable Neural Architecture Search&#34; is accepted by BMVC 2021</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/post/mojtaba_bmvc2021/</link>
      <pubDate>Sun, 26 Sep 2021 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/post/mojtaba_bmvc2021/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Our paper &#34;Enhancing Human Motion Assessment by Self-supervised Representation Learning&#34; is accepted by BMVC 2021</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/post/mahdiar_bmvc2021/</link>
      <pubDate>Sun, 26 Sep 2021 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/post/mahdiar_bmvc2021/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Automated Generation of Accurate and Fluent Medical X-ray Reports</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/hoang_automated_emnlp/</link>
      <pubDate>Fri, 27 Aug 2021 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/hoang_automated_emnlp/</guid>
      <description></description>
    </item>
    
    <item>
      <title>EventHPE: Event-based 3D Human Pose and Shape Estimation</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/shihao_eventhpe_2021/</link>
      <pubDate>Fri, 27 Aug 2021 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/shihao_eventhpe_2021/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Joint Visual and Audio Learning for Video Highlight Detection</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/tk_joint_2021/</link>
      <pubDate>Fri, 27 Aug 2021 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/tk_joint_2021/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Our paper &#34;Automated Generation of Accurate &amp; Fluent Medical X-ray Reports&#34; is accepted by EMNLP 2021</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/post/hoang_emnlp2021/</link>
      <pubDate>Fri, 27 Aug 2021 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/post/hoang_emnlp2021/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Dual Learning Music Composition and Dance Choreography</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/shuangwu_dual_2021/</link>
      <pubDate>Mon, 26 Jul 2021 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/shuangwu_dual_2021/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Our paper &#34;Dual Learning Music Composition and Dance Choreography&#34; is accepted by ACM Multimedia 2021</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/post/shuang_acmmm2021/</link>
      <pubDate>Mon, 26 Jul 2021 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/post/shuang_acmmm2021/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Our paper &#34;EventHPE: Event-based 3-D Human Pose Estimation&#34; is accepted by ICCV 2021</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/post/shihao_iccv2021/</link>
      <pubDate>Mon, 26 Jul 2021 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/post/shihao_iccv2021/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Our paper &#34;Joint Visual and Audio Learning for Video Highlight Detection&#34; is accepted by ICCV 2021</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/post/tk_iccv2021/</link>
      <pubDate>Mon, 26 Jul 2021 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/post/tk_iccv2021/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Our paper &#34;Calibrated RGB-D Salient Object Detection&#34; is accepted by IEEE Conference on Computer Vision and Pattern Recognition 2021</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/post/wei_cvpr_2021a/</link>
      <pubDate>Tue, 16 Mar 2021 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/post/wei_cvpr_2021a/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Our paper &#34;Learning Calibrated Medical Image Segmentation via Multi-rater Agreement Modeling&#34; is accepted by IEEE Conference on Computer Vision and Pattern Recognition 2021 (Best Paper Candidate)</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/post/wei_cvpr_2021b/</link>
      <pubDate>Tue, 16 Mar 2021 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/post/wei_cvpr_2021b/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Calibrated RGB-D Salient Object Detection</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/wei_cvpr21_dcf/</link>
      <pubDate>Mon, 01 Mar 2021 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/wei_cvpr21_dcf/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Learning Calibrated Medical Image Segmentation via Multi-rater Agreement Modeling</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/wei_cvpr21_mrnet/</link>
      <pubDate>Mon, 01 Mar 2021 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/wei_cvpr21_mrnet/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Our paper &#34;Deep Learning for Visual Tracking: A Comprehensive Survey&#34; is accepted by IEEE Transactions on Intelligent Transportation Systems (T-ITS)</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/post/mojtaba_tits2020/</link>
      <pubDate>Fri, 27 Nov 2020 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/post/mojtaba_tits2020/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Action2Motion: Conditioned Generation of 3D Human Motions</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/chuan_action2motion_mm/</link>
      <pubDate>Sat, 01 Aug 2020 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/chuan_action2motion_mm/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Our paper &#34;Action2Motion: Conditioned Generation of 3-D Human Motions&#34; is accepted by ACM Multimedia 2020</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/post/chuan_acmmm2020/</link>
      <pubDate>Sat, 25 Jul 2020 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/post/chuan_acmmm2020/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Our paper &#34;3D Human Shape Reconstruction from a Polarization Image&#34; is accepted by ECCV 2020</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/post/zou_eccv2020/</link>
      <pubDate>Sat, 04 Jul 2020 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/post/zou_eccv2020/</guid>
      <description></description>
    </item>
    
    <item>
      <title>3D Human Shape Reconstruction from a Polarization Image</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/shihaozou_polarization_2020/</link>
      <pubDate>Thu, 02 Jul 2020 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/shihaozou_polarization_2020/</guid>
      <description>&lt;!-- &lt;div class=&#34;alert alert-note&#34;&gt;
  &lt;div&gt;
    Click the &lt;em&gt;Cite&lt;/em&gt; button above to demo the feature to enable visitors to import publication metadata into their reference management software.
  &lt;/div&gt;
&lt;/div&gt;


&lt;div class=&#34;alert alert-note&#34;&gt;
  &lt;div&gt;
    Click the &lt;em&gt;Slides&lt;/em&gt; button above to demo Academic&amp;rsquo;s Markdown slides feature.
  &lt;/div&gt;
&lt;/div&gt;


Supplementary notes can be added here, including [code and math](https://sourcethemes.com/academic/docs/writing-markdown-latex/). --&gt;
</description>
    </item>
    
    <item>
      <title>FALCONS: FAst Learner-grader for CONtorted poses in Sports</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/mahdiar_falcons_2020/</link>
      <pubDate>Fri, 19 Jun 2020 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/mahdiar_falcons_2020/</guid>
      <description></description>
    </item>
    
    <item>
      <title>SparseFusion: Dynamic Human Avatar Modeling from Sparse RGBD Images</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/xinxin_tmm_2020/</link>
      <pubDate>Wed, 10 Jun 2020 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/xinxin_tmm_2020/</guid>
      <description></description>
    </item>
    
    <item>
      <title>3D Pose Estimation and Future Motion Prediction from 2D Images</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/project/jiyang_multitask_2020/</link>
      <pubDate>Mon, 01 Jun 2020 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/project/jiyang_multitask_2020/</guid>
      <description>&lt;p&gt;Lorem ipsum dolor sit amet, consectetur adipiscing elit. Duis posuere tellus ac convallis placerat. Proin tincidunt magna sed ex sollicitudin condimentum. Sed ac faucibus dolor, scelerisque sollicitudin nisi. Cras purus urna, suscipit quis sapien eu, pulvinar tempor diam. Quisque risus orci, mollis id ante sit amet, gravida egestas nisl. Sed ac tempus magna. Proin in dui enim. Donec condimentum, sem id dapibus fringilla, tellus enim condimentum arcu, nec volutpat est felis vel metus. Vestibulum sit amet erat at nulla eleifend gravida.&lt;/p&gt;
&lt;p&gt;Nullam vel molestie justo. Curabitur vitae efficitur leo. In hac habitasse platea dictumst. Sed pulvinar mauris dui, eget varius purus congue ac. Nulla euismod, lorem vel elementum dapibus, nunc justo porta mi, sed tempus est est vel tellus. Nam et enim eleifend, laoreet sem sit amet, elementum sem. Morbi ut leo congue, maximus velit ut, finibus arcu. In et libero cursus, rutrum risus non, molestie leo. Nullam congue quam et volutpat malesuada. Sed risus tortor, pulvinar et dictum nec, sodales non mi. Phasellus lacinia commodo laoreet. Nam mollis, erat in feugiat consectetur, purus eros egestas tellus, in auctor urna odio at nibh. Mauris imperdiet nisi ac magna convallis, at rhoncus ligula cursus.&lt;/p&gt;
&lt;p&gt;Cras aliquam rhoncus ipsum, in hendrerit nunc mattis vitae. Duis vitae efficitur metus, ac tempus leo. Cras nec fringilla lacus. Quisque sit amet risus at ipsum pharetra commodo. Sed aliquam mauris at consequat eleifend. Praesent porta, augue sed viverra bibendum, neque ante euismod ante, in vehicula justo lorem ac eros. Suspendisse augue libero, venenatis eget tincidunt ut, malesuada at lorem. Donec vitae bibendum arcu. Aenean maximus nulla non pretium iaculis. Quisque imperdiet, nulla in pulvinar aliquet, velit quam ultrices quam, sit amet fringilla leo sem vel nunc. Mauris in lacinia lacus.&lt;/p&gt;
&lt;p&gt;Suspendisse a tincidunt lacus. Curabitur at urna sagittis, dictum ante sit amet, euismod magna. Sed rutrum massa id tortor commodo, vitae elementum turpis tempus. Lorem ipsum dolor sit amet, consectetur adipiscing elit. Aenean purus turpis, venenatis a ullamcorper nec, tincidunt et massa. Integer posuere quam rutrum arcu vehicula imperdiet. Mauris ullamcorper quam vitae purus congue, quis euismod magna eleifend. Vestibulum semper vel augue eget tincidunt. Fusce eget justo sodales, dapibus odio eu, ultrices lorem. Duis condimentum lorem id eros commodo, in facilisis mauris scelerisque. Morbi sed auctor leo. Nullam volutpat a lacus quis pharetra. Nulla congue rutrum magna a ornare.&lt;/p&gt;
&lt;p&gt;Aliquam in turpis accumsan, malesuada nibh ut, hendrerit justo. Cum sociis natoque penatibus et magnis dis parturient montes, nascetur ridiculus mus. Quisque sed erat nec justo posuere suscipit. Donec ut efficitur arcu, in malesuada neque. Nunc dignissim nisl massa, id vulputate nunc pretium nec. Quisque eget urna in risus suscipit ultricies. Pellentesque odio odio, tincidunt in eleifend sed, posuere a diam. Nam gravida nisl convallis semper elementum. Morbi vitae felis faucibus, vulputate orci placerat, aliquet nisi. Aliquam erat volutpat. Maecenas sagittis pulvinar purus, sed porta quam laoreet at.&lt;/p&gt;
</description>
    </item>
    
    <item>
      <title>Fully automated leg movement tracking in freely moving insects using Feature Learning Leg Segmentation and Tracking (FLLIT)</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/ban-et-al-jo-ve-20/</link>
      <pubDate>Wed, 01 Jan 2020 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/ban-et-al-jo-ve-20/</guid>
      <description></description>
    </item>
    
    <item>
      <title>IDRiD: Diabetic Retinopathy - Segmentation and Grading Challenge</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/lyu-et-al-mia-20/</link>
      <pubDate>Wed, 01 Jan 2020 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/lyu-et-al-mia-20/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Improving retinal vessel segmentation with joint local loss by matting</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/zha-et-al-pr-20/</link>
      <pubDate>Wed, 01 Jan 2020 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/zha-et-al-pr-20/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Least Squares Approximation via Sparse Subsampled Randomized Hadamard Transform</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/ten-et-al-tbd-20/</link>
      <pubDate>Wed, 01 Jan 2020 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/ten-et-al-tbd-20/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Towards Natural and Accurate Future Motion Prediction of Humans and Animals</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/shuangwu_hmr_2019/</link>
      <pubDate>Mon, 01 Jul 2019 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/shuangwu_hmr_2019/</guid>
      <description>&lt;!-- &lt;div class=&#34;alert alert-note&#34;&gt;
  &lt;div&gt;
    Click the &lt;em&gt;Cite&lt;/em&gt; button above to demo the feature to enable visitors to import publication metadata into their reference management software.
  &lt;/div&gt;
&lt;/div&gt;


&lt;div class=&#34;alert alert-note&#34;&gt;
  &lt;div&gt;
    Click the &lt;em&gt;Slides&lt;/em&gt; button above to demo Academic&amp;rsquo;s Markdown slides feature.
  &lt;/div&gt;
&lt;/div&gt;


Supplementary notes can be added here, including [code and math](https://sourcethemes.com/academic/docs/writing-markdown-latex/). --&gt;
</description>
    </item>
    
    <item>
      <title>Hierarchical Motion Recurrent Network for Future Motion Prediction of Humans and Animals</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/project/shuangwu_hmr_2019/</link>
      <pubDate>Sat, 27 Apr 2019 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/project/shuangwu_hmr_2019/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Slides</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/slides/example/</link>
      <pubDate>Tue, 05 Feb 2019 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/slides/example/</guid>
      <description>&lt;h1 id=&#34;create-slides-in-markdown-with-academic&#34;&gt;Create slides in Markdown with Academic&lt;/h1&gt;
&lt;p&gt;
&lt;a href=&#34;https://sourcethemes.com/academic/&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;Academic&lt;/a&gt; | 
&lt;a href=&#34;https://sourcethemes.com/academic/docs/managing-content/#create-slides&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;Documentation&lt;/a&gt;&lt;/p&gt;
&lt;hr&gt;
&lt;h2 id=&#34;features&#34;&gt;Features&lt;/h2&gt;
&lt;ul&gt;
&lt;li&gt;Efficiently write slides in Markdown&lt;/li&gt;
&lt;li&gt;3-in-1: Create, Present, and Publish your slides&lt;/li&gt;
&lt;li&gt;Supports speaker notes&lt;/li&gt;
&lt;li&gt;Mobile friendly slides&lt;/li&gt;
&lt;/ul&gt;
&lt;hr&gt;
&lt;h2 id=&#34;controls&#34;&gt;Controls&lt;/h2&gt;
&lt;ul&gt;
&lt;li&gt;Next: &lt;code&gt;Right Arrow&lt;/code&gt; or &lt;code&gt;Space&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;Previous: &lt;code&gt;Left Arrow&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;Start: &lt;code&gt;Home&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;Finish: &lt;code&gt;End&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;Overview: &lt;code&gt;Esc&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;Speaker notes: &lt;code&gt;S&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;Fullscreen: &lt;code&gt;F&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;Zoom: &lt;code&gt;Alt + Click&lt;/code&gt;&lt;/li&gt;
&lt;li&gt;
&lt;a href=&#34;https://github.com/hakimel/reveal.js#pdf-export&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;PDF Export&lt;/a&gt;: &lt;code&gt;E&lt;/code&gt;&lt;/li&gt;
&lt;/ul&gt;
&lt;hr&gt;
&lt;h2 id=&#34;code-highlighting&#34;&gt;Code Highlighting&lt;/h2&gt;
&lt;p&gt;Inline code: &lt;code&gt;variable&lt;/code&gt;&lt;/p&gt;
&lt;p&gt;Code block:&lt;/p&gt;
&lt;pre&gt;&lt;code class=&#34;language-python&#34;&gt;porridge = &amp;quot;blueberry&amp;quot;
if porridge == &amp;quot;blueberry&amp;quot;:
    print(&amp;quot;Eating...&amp;quot;)
&lt;/code&gt;&lt;/pre&gt;
&lt;hr&gt;
&lt;h2 id=&#34;math&#34;&gt;Math&lt;/h2&gt;
&lt;p&gt;In-line math: $x + y = z$&lt;/p&gt;
&lt;p&gt;Block math:&lt;/p&gt;
&lt;p&gt;$$
f\left( x \right) = ;\frac{{2\left( {x + 4} \right)\left( {x - 4} \right)}}{{\left( {x + 4} \right)\left( {x + 1} \right)}}
$$&lt;/p&gt;
&lt;hr&gt;
&lt;h2 id=&#34;fragments&#34;&gt;Fragments&lt;/h2&gt;
&lt;p&gt;Make content appear incrementally&lt;/p&gt;
&lt;pre&gt;&lt;code&gt;{{% fragment %}} One {{% /fragment %}}
{{% fragment %}} **Two** {{% /fragment %}}
{{% fragment %}} Three {{% /fragment %}}
&lt;/code&gt;&lt;/pre&gt;
&lt;p&gt;Press &lt;code&gt;Space&lt;/code&gt; to play!&lt;/p&gt;
&lt;p&gt;&lt;span class=&#34;fragment &#34; &gt;
One 
&lt;/span&gt;
&lt;span class=&#34;fragment &#34; &gt;
&lt;strong&gt;Two&lt;/strong&gt; 
&lt;/span&gt;
&lt;span class=&#34;fragment &#34; &gt;
Three 
&lt;/span&gt;&lt;/p&gt;
&lt;hr&gt;
&lt;p&gt;A fragment can accept two optional parameters:&lt;/p&gt;
&lt;ul&gt;
&lt;li&gt;&lt;code&gt;class&lt;/code&gt;: use a custom style (requires definition in custom CSS)&lt;/li&gt;
&lt;li&gt;&lt;code&gt;weight&lt;/code&gt;: sets the order in which a fragment appears&lt;/li&gt;
&lt;/ul&gt;
&lt;hr&gt;
&lt;h2 id=&#34;speaker-notes&#34;&gt;Speaker Notes&lt;/h2&gt;
&lt;p&gt;Add speaker notes to your presentation&lt;/p&gt;
&lt;pre&gt;&lt;code class=&#34;language-markdown&#34;&gt;{{% speaker_note %}}
- Only the speaker can read these notes
- Press `S` key to view
{{% /speaker_note %}}
&lt;/code&gt;&lt;/pre&gt;
&lt;p&gt;Press the &lt;code&gt;S&lt;/code&gt; key to view the speaker notes!&lt;/p&gt;
&lt;aside class=&#34;notes&#34;&gt;
  &lt;ul&gt;
&lt;li&gt;Only the speaker can read these notes&lt;/li&gt;
&lt;li&gt;Press &lt;code&gt;S&lt;/code&gt; key to view&lt;/li&gt;
&lt;/ul&gt;

&lt;/aside&gt;
&lt;hr&gt;
&lt;h2 id=&#34;themes&#34;&gt;Themes&lt;/h2&gt;
&lt;ul&gt;
&lt;li&gt;black: Black background, white text, blue links (default)&lt;/li&gt;
&lt;li&gt;white: White background, black text, blue links&lt;/li&gt;
&lt;li&gt;league: Gray background, white text, blue links&lt;/li&gt;
&lt;li&gt;beige: Beige background, dark text, brown links&lt;/li&gt;
&lt;li&gt;sky: Blue background, thin dark text, blue links&lt;/li&gt;
&lt;/ul&gt;
&lt;hr&gt;
&lt;ul&gt;
&lt;li&gt;night: Black background, thick white text, orange links&lt;/li&gt;
&lt;li&gt;serif: Cappuccino background, gray text, brown links&lt;/li&gt;
&lt;li&gt;simple: White background, black text, blue links&lt;/li&gt;
&lt;li&gt;solarized: Cream-colored background, dark green text, blue links&lt;/li&gt;
&lt;/ul&gt;
&lt;hr&gt;

&lt;section data-noprocess data-shortcode-slide
  
      
      data-background-image=&#34;/img/boards.jpg&#34;
  &gt;

&lt;h2 id=&#34;custom-slide&#34;&gt;Custom Slide&lt;/h2&gt;
&lt;p&gt;Customize the slide style and background&lt;/p&gt;
&lt;pre&gt;&lt;code class=&#34;language-markdown&#34;&gt;{{&amp;lt; slide background-image=&amp;quot;/img/boards.jpg&amp;quot; &amp;gt;}}
{{&amp;lt; slide background-color=&amp;quot;#0000FF&amp;quot; &amp;gt;}}
{{&amp;lt; slide class=&amp;quot;my-style&amp;quot; &amp;gt;}}
&lt;/code&gt;&lt;/pre&gt;
&lt;hr&gt;
&lt;h2 id=&#34;custom-css-example&#34;&gt;Custom CSS Example&lt;/h2&gt;
&lt;p&gt;Let&amp;rsquo;s make headers navy colored.&lt;/p&gt;
&lt;p&gt;Create &lt;code&gt;assets/css/reveal_custom.css&lt;/code&gt; with:&lt;/p&gt;
&lt;pre&gt;&lt;code class=&#34;language-css&#34;&gt;.reveal section h1,
.reveal section h2,
.reveal section h3 {
  color: navy;
}
&lt;/code&gt;&lt;/pre&gt;
&lt;hr&gt;
&lt;h1 id=&#34;questions&#34;&gt;Questions?&lt;/h1&gt;
&lt;p&gt;
&lt;a href=&#34;https://spectrum.chat/academic&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;Ask&lt;/a&gt;&lt;/p&gt;
&lt;p&gt;
&lt;a href=&#34;https://sourcethemes.com/academic/docs/managing-content/#create-slides&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;Documentation&lt;/a&gt;&lt;/p&gt;
</description>
    </item>
    
    <item>
      <title>Fully automated leg tracking of Drosophila neurodegeneration models reveals distinct conserved movement signatures</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/wu-et-al-plos-bio-19/</link>
      <pubDate>Tue, 01 Jan 2019 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/wu-et-al-plos-bio-19/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Multivariate Regression with Gross Errors on Manifold-valued Data</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/zha-et-al-tpami-19/</link>
      <pubDate>Tue, 01 Jan 2019 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/zha-et-al-tpami-19/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Multi-modal Multi-task Learning for Automatic Dietary Assessment</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/liu-et-al-aaai-18/</link>
      <pubDate>Mon, 01 Jan 2018 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/liu-et-al-aaai-18/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Supervised Segmentation of Un-annotated Retinal Fundus Images by Synthesis</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/zha-et-al-tmi-18/</link>
      <pubDate>Mon, 01 Jan 2018 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/zha-et-al-tmi-18/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Synthesizing Retinal and Neuronal Images with Generative Adversarial Nets</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/zha-et-al-med-ia-18/</link>
      <pubDate>Mon, 01 Jan 2018 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/zha-et-al-med-ia-18/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Too Far to See? Not Really! Pedestrian Detection with Scale-aware Localization Policy</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/zha-et-al-tip-18/</link>
      <pubDate>Mon, 01 Jan 2018 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/zha-et-al-tip-18/</guid>
      <description></description>
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      <title>Transduction on Directed Graphs via Absorbing Random Walks</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/de-et-al-tpami-18/</link>
      <pubDate>Mon, 01 Jan 2018 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/de-et-al-tpami-18/</guid>
      <description></description>
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      <title>Fusion of Magnetic and Vision sensors for indoor localization: Infrastructure-free and More Effective</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/liu-et-al-tmm-17/</link>
      <pubDate>Sun, 01 Jan 2017 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/liu-et-al-tmm-17/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Hand Action Detection from Ego-centric Depth Sequences</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/xu-gov-che-pr-17/</link>
      <pubDate>Sun, 01 Jan 2017 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/xu-gov-che-pr-17/</guid>
      <description></description>
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      <title>Lie-X: Depth Image Based Articulated Object Pose Estimation, Tracking, and Action Recognition on Lie Groups</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/xu-et-al-ijcv-17/</link>
      <pubDate>Sun, 01 Jan 2017 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/xu-et-al-ijcv-17/</guid>
      <description></description>
    </item>
    
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      <title>Multiview and Multimodal Pervasive Indoor Localization</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/liu-et-al-mm-17/</link>
      <pubDate>Sun, 01 Jan 2017 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/liu-et-al-mm-17/</guid>
      <description></description>
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      <title>Pose Estimation from Line Correspondences: A Complete Analysis and A Series of Solutions</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/xu-et-al-tpami-17/</link>
      <pubDate>Sun, 01 Jan 2017 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/xu-et-al-tpami-17/</guid>
      <description></description>
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      <title>Quantitative 3D analysis of complex single border cell behaviors in coordinated collective cell migration</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/cli-et-al-nc-17/</link>
      <pubDate>Sun, 01 Jan 2017 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/cli-et-al-nc-17/</guid>
      <description></description>
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    <item>
      <title>Quantitative localization of a Golgi protein by imaging its fluorescence center of mass</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/tie-et-al-jo-ve-17/</link>
      <pubDate>Sun, 01 Jan 2017 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/tie-et-al-jo-ve-17/</guid>
      <description></description>
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    <item>
      <title>Segment 2D and 3D Filaments by Learning Structured and Contextual Features</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/gu-et-al-tmi-17/</link>
      <pubDate>Sun, 01 Jan 2017 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/gu-et-al-tmi-17/</guid>
      <description></description>
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    <item>
      <title></title>
      <link>https://vision-and-learning-lab-ualberta.github.io/archive/opening_postdoc/</link>
      <pubDate>Wed, 20 Apr 2016 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/archive/opening_postdoc/</guid>
      <description>&lt;h2 id=&#34;joining-us&#34;&gt;Joining us&amp;hellip;&lt;/h2&gt;
&lt;p&gt;The Vision &amp;amp; Learning Lab at the ECE Dept. University of Alberta, is inviting outstanding Postdoc candidates to join us.&lt;/p&gt;
&lt;p&gt;We are setting up a new lab at the ECE Dept., University of Alberta, focusing on exciting research topics in computer vision and machine learning. We are looking for exceptional Postdocs to join us.&lt;/p&gt;
&lt;p&gt;Further information about the PI, Dr. Li Cheng, can be found below, or via 
&lt;a href=&#34;https://www.ece.ualberta.ca/~lcheng5/&#34; target=&#34;_blank&#34; rel=&#34;noopener&#34;&gt;https://www.ece.ualberta.ca/~lcheng5/&lt;/a&gt; or by direct inquiries to 
&lt;a href=&#34;mailto:lcheng5@ualberta.ca&#34;&gt;lcheng5@ualberta.ca&lt;/a&gt;.&lt;/p&gt;
&lt;p&gt;Potential candidates are requested to email their CVs (in PDF) to Li Cheng (
&lt;a href=&#34;mailto:lcheng5@ualberta.ca&#34;&gt;lcheng5@ualberta.ca&lt;/a&gt;).&lt;/p&gt;
&lt;h2 id=&#34;about-the-pi&#34;&gt;About the PI&amp;hellip;&lt;/h2&gt;
&lt;p&gt;Li CHENG is an associate professor with the ECE Dept., University of Alberta, Canada. His research expertise is mainly in computer vision and machine learning, with application focus in both visual behavior analysis and biomedical image analysis. His research work has resulted in over 90 referred papers including those published at journals such as IEEE Trans. Pattern Analysis and Machine Intelligence, International Journal of Computer Vision, as well as conferences such as ICML, NIPS, ICCV, CVPR, MICCAI, and AAAI. He is a senior member of IEEE.&lt;/p&gt;
</description>
    </item>
    
    <item>
      <title>A Graph-theoretical Approach for Tracing Filamentary Structures in Neuronal and Retinal Images</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/de-et-al-tmi-16/</link>
      <pubDate>Fri, 01 Jan 2016 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/de-et-al-tmi-16/</guid>
      <description></description>
    </item>
    
    <item>
      <title>A novel imaging method for quantitative Golgi localization reveals differential intra-Golgi trafficking of secretory cargos</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/tie-et-al-m-bo-c-16/</link>
      <pubDate>Fri, 01 Jan 2016 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/tie-et-al-m-bo-c-16/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Action Recognition in Still Images with Minimum Annotation Efforts</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/zha-et-al-tip-16/</link>
      <pubDate>Fri, 01 Jan 2016 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/zha-et-al-tip-16/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Estimate Hand Poses Efficiently from Single Depth Images</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/xu-et-al-ijcv-16/</link>
      <pubDate>Fri, 01 Jan 2016 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/xu-et-al-ijcv-16/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Incremental Regularized Least Squares for Dimensionality Reduction of Large-Scale Data</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/zha-et-al-sisc-16/</link>
      <pubDate>Fri, 01 Jan 2016 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/zha-et-al-sisc-16/</guid>
      <description></description>
    </item>
    
    <item>
      <title>NeuronCyto II: An Automatic and Quantitative Solution for Crossover Neural Cells in High Throughput Screening</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/ong-et-al-cyto-16/</link>
      <pubDate>Fri, 01 Jan 2016 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/ong-et-al-cyto-16/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Recognizing Complex Activities by a Probabilistic Interval-based Model</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/liu-et-al-aaai-16/</link>
      <pubDate>Fri, 01 Jan 2016 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/liu-et-al-aaai-16/</guid>
      <description></description>
    </item>
    
    <item>
      <title>An Efficient Self-Tuning Multiclass Classification Approach</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/qia-gon-che-cai-15/</link>
      <pubDate>Thu, 01 Jan 2015 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/qia-gon-che-cai-15/</guid>
      <description></description>
    </item>
    
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      <title>Automated Image Based Prominent Nucleoli Detection</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/yap-et-al-jpi-15/</link>
      <pubDate>Thu, 01 Jan 2015 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/yap-et-al-jpi-15/</guid>
      <description></description>
    </item>
    
    <item>
      <title>GHand: A GPU algorithm for realtime hand pose estimation using depth camera</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/nan-chi-che-eurographics-15/</link>
      <pubDate>Thu, 01 Jan 2015 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/nan-chi-che-eurographics-15/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Integrated Foreground Segmentation and Boundary Matting for Live Videos</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/gon-qia-che-tip-15/</link>
      <pubDate>Thu, 01 Jan 2015 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/gon-qia-che-tip-15/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Learning to Boost Filamentary Structure Segmentation</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/gu-che-iccv-15/</link>
      <pubDate>Thu, 01 Jan 2015 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/gu-che-iccv-15/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Robust Multivariate Regression with Grossly Corrupted Observations and Its Application to Personality Prediction</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/zha-che-zhu-acml-15/</link>
      <pubDate>Thu, 01 Jan 2015 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/zha-che-zhu-acml-15/</guid>
      <description></description>
    </item>
    
    <item>
      <title>A Random-Forest Random Field Approach for Cellular Image Segmentation</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/jin-et-al-isbi-14/</link>
      <pubDate>Wed, 01 Jan 2014 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/jin-et-al-isbi-14/</guid>
      <description></description>
    </item>
    
    <item>
      <title>A retinal vessel boundary tracking method based on Bayesian theory and multi-scale line detection</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/zha-et-al-cmig-14/</link>
      <pubDate>Wed, 01 Jan 2014 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/zha-et-al-cmig-14/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Myopia in Asian Subjects with Primary Angle Closure: Implications for Glaucoma Trends in East Asia</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/yon-et-al-optho-14/</link>
      <pubDate>Wed, 01 Jan 2014 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/yon-et-al-optho-14/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Recognizing Flu-like Symptoms from Videos</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/thi-et-al-bmcbioinfo-14/</link>
      <pubDate>Wed, 01 Jan 2014 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/thi-et-al-bmcbioinfo-14/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Semi-supervised Domain Adaptation on Manifolds</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/che-pan-tnnls-14/</link>
      <pubDate>Wed, 01 Jan 2014 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/che-pan-tnnls-14/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Tracing Retinal Blood Vessels by Matrix-Forest Theorem of Directed Graphs</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/che-et-al-miccai-14/</link>
      <pubDate>Wed, 01 Jan 2014 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/che-et-al-miccai-14/</guid>
      <description></description>
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    <item>
      <title>Tracing retinal vessel trees by transductive inference</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/de-li-che-bmcbioinfo-14/</link>
      <pubDate>Wed, 01 Jan 2014 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/de-li-che-bmcbioinfo-14/</guid>
      <description></description>
    </item>
    
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      <title>Anterior segment optical coherence tomography parameters in subtypes of primary angle closure</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/guz-et-al-iovs-13/</link>
      <pubDate>Tue, 01 Jan 2013 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/guz-et-al-iovs-13/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Automated Tracing of Retinal Blood Vessels Using Graphical Models</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/jay-et-al-scia-13/</link>
      <pubDate>Tue, 01 Jan 2013 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/jay-et-al-scia-13/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Editorial of the Special issue: Machine Learning in Motion Analysis: New Advances</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/pie-et-al-ivc-13/</link>
      <pubDate>Tue, 01 Jan 2013 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/pie-et-al-ivc-13/</guid>
      <description></description>
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    <item>
      <title>Efficient Hand Pose Estimation from a Single Depth Image</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/xu-che-iccv-13/</link>
      <pubDate>Tue, 01 Jan 2013 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/xu-che-iccv-13/</guid>
      <description></description>
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    <item>
      <title>Exploiting Syntactic, Semantic, and Lexical Regularities in Language Modeling via Directed Markov Random Fields</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/wan-et-al-ci-13/</link>
      <pubDate>Tue, 01 Jan 2013 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/wan-et-al-ci-13/</guid>
      <description></description>
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      <title>Finding Distinctive Shape Features for Hematoma Classification in Brain CT Images</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/gon-et-al-ictai-13/</link>
      <pubDate>Tue, 01 Jan 2013 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/gon-et-al-ictai-13/</guid>
      <description></description>
    </item>
    
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      <title>Riemannian Similarity Learning</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/che-icml-13/</link>
      <pubDate>Tue, 01 Jan 2013 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/che-icml-13/</guid>
      <description></description>
    </item>
    
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      <title>Subgrouping of Primary Angle-Closure Suspects Based on Anterior Segment Optical Coherence Tomography Parameters</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/non-et-al-optho-13/</link>
      <pubDate>Tue, 01 Jan 2013 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/non-et-al-optho-13/</guid>
      <description></description>
    </item>
    
    <item>
      <title>A Bag-of-Words Model for Cellular Image Segmentation</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/che-et-al-springerbkchapt-12/</link>
      <pubDate>Sun, 01 Jan 2012 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/che-et-al-springerbkchapt-12/</guid>
      <description></description>
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      <title>Integrating Local Action Elements for Action Analysis</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/thi-et-al-cviu-12/</link>
      <pubDate>Sun, 01 Jan 2012 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/thi-et-al-cviu-12/</guid>
      <description></description>
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      <title>Structured learning of local features for human action classification and localization</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/thi-et-al-ivc-12/</link>
      <pubDate>Sun, 01 Jan 2012 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/thi-et-al-ivc-12/</guid>
      <description></description>
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      <title>Discriminative Cellular Segmentation for Microscopic Images</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/che-et-al-miccai-11/</link>
      <pubDate>Sat, 01 Jan 2011 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/che-et-al-miccai-11/</guid>
      <description></description>
    </item>
    
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      <title>Discriminative Human Action Segmentation and Recognition using SMMs</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/shi-et-al-ijcv-11/</link>
      <pubDate>Sat, 01 Jan 2011 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/shi-et-al-ijcv-11/</guid>
      <description></description>
    </item>
    
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      <title>Elastic Sequence Correlation for Human Action Analysis</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/wan-che-wan-tip-11/</link>
      <pubDate>Sat, 01 Jan 2011 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/wan-che-wan-tip-11/</guid>
      <description></description>
    </item>
    
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      <title>Foreground Segmentation of Live Videos using Locally Competing 1SVMs</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/gon-che-cvpr-11/</link>
      <pubDate>Sat, 01 Jan 2011 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/gon-che-cvpr-11/</guid>
      <description></description>
    </item>
    
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      <title>Incorporating Estimated Motion in Real-time Background Subtraction</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/gon-che-icip-11/</link>
      <pubDate>Sat, 01 Jan 2011 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/gon-che-icip-11/</guid>
      <description></description>
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      <title>Real-time Discriminative Background Subtraction</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/che-et-al-tip-11/</link>
      <pubDate>Sat, 01 Jan 2011 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/che-et-al-tip-11/</guid>
      <description></description>
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      <title>Efficient Learning to Label Images</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/jia-che-liu-wan-icpr-10/</link>
      <pubDate>Fri, 01 Jan 2010 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/jia-che-liu-wan-icpr-10/</guid>
      <description></description>
    </item>
    
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      <title>Human Action Recognition and Localization in Video using Structured Learning of Local Space-Time Features</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/thi-che-zha-wan-sat-avss-10/</link>
      <pubDate>Fri, 01 Jan 2010 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/thi-che-zha-wan-sat-avss-10/</guid>
      <description></description>
    </item>
    
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      <title>Human Action Recognition from Boosted Pose Estimation</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/wan-che-thi-zha-dicta-10/</link>
      <pubDate>Fri, 01 Jan 2010 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/wan-che-thi-zha-dicta-10/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Implicit Motion-Shape Model: A generic approach for action matching</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/thi-che-zha-wan-icpr-10/</link>
      <pubDate>Fri, 01 Jan 2010 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/thi-che-zha-wan-icpr-10/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Learning Graph Matching</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/cae-et-al-tpami-09/</link>
      <pubDate>Mon, 01 Jun 2009 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/cae-et-al-tpami-09/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Human Body Articulation for Action Recognition in Video Sequences</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/thi-et-al-avss-09/</link>
      <pubDate>Thu, 01 Jan 2009 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/thi-et-al-avss-09/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Inference of the Structural Credit Risk Model using MLE</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/li-che-sch-cife-09/</link>
      <pubDate>Thu, 01 Jan 2009 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/li-che-sch-cife-09/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Learning-based multiview video coding</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/bao-et-al-pcs-09/</link>
      <pubDate>Thu, 01 Jan 2009 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/bao-et-al-pcs-09/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Machine Learning for Human Motion Analysis: Theory and Practice</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/igi-book-09/</link>
      <pubDate>Thu, 01 Jan 2009 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/igi-book-09/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Realtime Background Subtraction from Dynamic Scenes</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/che-gon-iccv-09/</link>
      <pubDate>Thu, 01 Jan 2009 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/che-gon-iccv-09/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Spatial-Temporal Modeling of Interactive Image Interpretation</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/zho-che-bis-sv-09/</link>
      <pubDate>Thu, 01 Jan 2009 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/zho-che-bis-sv-09/</guid>
      <description></description>
    </item>
    
    <item>
      <title>User-Driven Lossy Compression for Images and Video</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/bre-et-al-ivcnz-09/</link>
      <pubDate>Thu, 01 Jan 2009 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/bre-et-al-ivcnz-09/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Consistent image analogies using semi-supervised learning</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/che-vis-zha-cvpr-08/</link>
      <pubDate>Tue, 01 Jan 2008 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/che-vis-zha-cvpr-08/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Discriminative Human Action Segmentation and Recognition using. Semi-Markov Model</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/shi-wan-che-smo-cvpr-08/</link>
      <pubDate>Tue, 01 Jan 2008 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/shi-wan-che-smo-cvpr-08/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Prediction and Change Detection In Sequential Data for Interactive Applications</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/zho-che-bis-aaai-08/</link>
      <pubDate>Tue, 01 Jan 2008 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/zho-che-bis-aaai-08/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Bayesian stereo matching</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/che-cae-cviu-07/</link>
      <pubDate>Mon, 01 Jan 2007 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/che-cae-cviu-07/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Influence of Human Inputs on Semi-automatic Image Interpretation</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/zho-che-cae-bis-hcii-07/</link>
      <pubDate>Mon, 01 Jan 2007 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/zho-che-cae-bis-hcii-07/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Learning Graph Matching</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/cae-che-le-smo-iccv-07/</link>
      <pubDate>Mon, 01 Jan 2007 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/cae-che-le-smo-iccv-07/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Learning to compress images and videos</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/che-vis-cvpr-07/</link>
      <pubDate>Mon, 01 Jan 2007 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/che-vis-cvpr-07/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Online Learning with Novelty Detection in Human-guided Road Tracking</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/zho-che-bis-tgrs-07/</link>
      <pubDate>Mon, 01 Jan 2007 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/zho-che-bis-tgrs-07/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Stochastic Analysis of Lexical and Semantic for Enhanced Structural Language Model</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/wan-wan-che-gre-sch-icgi-07/</link>
      <pubDate>Mon, 01 Jan 2007 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/wan-wan-che-gre-sch-icgi-07/</guid>
      <description></description>
    </item>
    
    <item>
      <title>A Novel Learning Approach for Semi-automatic Road Tracking</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/zho-che-bis-prrs-06/</link>
      <pubDate>Sun, 01 Jan 2006 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/zho-che-bis-prrs-06/</guid>
      <description></description>
    </item>
    
    <item>
      <title>An Online Discriminative Approach to Background Subtraction</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/che-wan-sch-cae-vis-avss-06/</link>
      <pubDate>Sun, 01 Jan 2006 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/che-wan-sch-cae-vis-avss-06/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Component Optimization for Image Understanding: a Bayesian Approach</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/che-cae-tpami-06/</link>
      <pubDate>Sun, 01 Jan 2006 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/che-cae-tpami-06/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Bayesian Image Understanding: From Images to Virtual Forests</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/che-cae-fan-ijcra-05/</link>
      <pubDate>Sat, 01 Jan 2005 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/che-cae-fan-ijcra-05/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Exploiting syntactic, semantic and lexical regularities in language modeling via directed Markov random fields</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/wan-wan-gre-sch-che-icml-05/</link>
      <pubDate>Sat, 01 Jan 2005 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/wan-wan-gre-sch-che-icml-05/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Variational Bayesian image modelling</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/che-jia-sch-wan-icml-05/</link>
      <pubDate>Sat, 01 Jan 2005 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/che-jia-sch-wan-icml-05/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Bayesian Stereo Matching</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/che-cae-gmbv-04/</link>
      <pubDate>Thu, 01 Jan 2004 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/che-cae-gmbv-04/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Forestry Scene Geometry Estimation via Statistical Learning</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/che-cae-lcvpr-04/</link>
      <pubDate>Thu, 01 Jan 2004 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/che-cae-lcvpr-04/</guid>
      <description></description>
    </item>
    
    <item>
      <title>A Bayesian Approach to Image Understanding: From Images to Virtual Forests</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/che-cae-vi-03-b/</link>
      <pubDate>Wed, 01 Jan 2003 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/che-cae-vi-03-b/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Doubly-MRF Stereo Matching</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/che-cae-icassp-03/</link>
      <pubDate>Wed, 01 Jan 2003 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/che-cae-icassp-03/</guid>
      <description></description>
    </item>
    
    <item>
      <title>Unsupervised Image Segmentation: A Bayesian Approach</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/che-cae-vi-03-a/</link>
      <pubDate>Wed, 01 Jan 2003 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/che-cae-vi-03-a/</guid>
      <description></description>
    </item>
    
    <item>
      <title>A Trainable Hierarchical Hidden Markov Tree Model for color image Annotation</title>
      <link>https://vision-and-learning-lab-ualberta.github.io/publication/che-cae-icpr-02/</link>
      <pubDate>Tue, 01 Jan 2002 00:00:00 +0000</pubDate>
      <guid>https://vision-and-learning-lab-ualberta.github.io/publication/che-cae-icpr-02/</guid>
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