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<channel>
<title>Yuanjun's Recent Activities</title>
<link>http://yjxiong.me/feeds.xml</link>
<description>This is a list of Yuanjun's recent academic activities.</description>

<!-- BCT -->
<item>
    <title>Backward Compatible Representation</title>
    <link>http://www.wider-challenge.org/</link>
    <guid>http://www.wider-challenge.org/</guid>
    <pubDate>Tuesday, 15 Nov 2020 00:00:00 GMT</pubDate>
    <description>
        <![CDATA[
           I am thrilled to be co-organizing the WIDER Face and Pedestrain Challenge held on the CVPR2018. The challenge website <a href=http://www.wider-challenge.org/>can found here</a>.
           The challenge centers around the problem of precise localization of human faces and bodies, and accurate association of identities. This year the challenge is sponsored by SenseTime and Amazon. The winning prize will be appealing. The challenge comprises of three tracks:
           
           <a href=https://competitions.codalab.org/competitions/19053>WIDER Face</a>, aims at soliciting new approaches to advance the state-of- the-art in face detection.
           <a href=https://competitions.codalab.org/competitions/19068>WIDER Pedestrian</a>, has the goal of gathering effective and efficient approaches to address the problem of pedestrian detection in unconstrained environments.
           <a href=https://competitions.codalab.org/competitions/19055>WIDER Person Search<a/>, presents an exciting challenge of searching persons across 192 movies.
           
           Welcome to participate in the challenge!
        ]]>
    </description>
</item>

<!-- WIDER Challenge -->
<item>
    <title>The WIDER Face and Pedestrain Challenge on CVPR2018</title>
    <link>http://www.wider-challenge.org/</link>
    <guid>http://www.wider-challenge.org/</guid>
    <pubDate>Tuesday, 15 May 2018 00:00:00 GMT</pubDate>
    <description>
        <![CDATA[
           I am thrilled to be co-organizing the WIDER Face and Pedestrain Challenge held on the CVPR2018. The challenge website <a href=http://www.wider-challenge.org/>can found here</a>.
           The challenge centers around the problem of precise localization of human faces and bodies, and accurate association of identities. This year the challenge is sponsored by SenseTime and Amazon. The winning prize will be appealing. The challenge comprises of three tracks:
           
           <a href=https://competitions.codalab.org/competitions/19053>WIDER Face</a>, aims at soliciting new approaches to advance the state-of- the-art in face detection.
           <a href=https://competitions.codalab.org/competitions/19068>WIDER Pedestrian</a>, has the goal of gathering effective and efficient approaches to address the problem of pedestrian detection in unconstrained environments.
           <a href=https://competitions.codalab.org/competitions/19055>WIDER Person Search<a/>, presents an exciting challenge of searching persons across 192 movies.
           
           Welcome to participate in the challenge!
        ]]>
    </description>
</item>

<!-- Lemniscate  release -->
<item>
    <title>Arxiv preprint, code, and models of project Lemniscate are released.</title>
    <link>https://github.com/zhirongw/lemniscate.pytorch</link>
    <guid>https://github.com/zhirongw/lemniscate.pytorch</guid>
    <pubDate>Sunday, 6 May 2018 00:00:00 GMT</pubDate>
    <description>
        <![CDATA[
            Our paper "Unsupervised Feature Learning via Non-parametric Instance-level Discrimination"" is
            recently accepted to CVPR 2018.
            Now we have released the paper's preprint on Arxiv, together with the code and trained models.
            We decided to name the project as Lemniscate. In mathmatics, lemniscate refers to the symbol that stands for infinity.
            The name came from our hope that the unsupervised leawrning scheme introduced in this work could be used on the infinite amount of visaul data available on the Internet. One day we will make use of these data and build strong computer vision models.
            
            <br><br>
            Download the paper at
            <a href=https://arxiv.org/abs/1805.01978>[Arxiv Preprint]</a>.
            <br>
            Checkout the code and models this
            <a href=https://github.com/zhirongw/lemniscate.pytorch>[Github link]</a>.
        ]]>
    </description>
</item>

<!-- ST-GCN  release -->
<item>
<title>Arxiv preprint, code, and models of ST-GCN (AAAI 2018) are released.</title>
<link>https://github.com/yysijie/st-gcn</link>
<guid>https://github.com/yysijie/st-gcn</guid>
<pubDate>Tuesday, 23 Jan 2018 00:00:00 GMT</pubDate>
<description>
<![CDATA[
Our paper "Spatial Temporal Graph Convolutional Networks for Skeleton-Based Action Recognition" (ST-GCN) is
recently accepted to AAAI 2018.
Now we have released the paper's preprint on Arxiv, together with the code and trained models of ST-GCN.
ST-GCN is an elegant model to tackle the problem of skeleton-based action recognition.
It achieves superior recognition performance with a simple yet effective architecture.
We believe this model will also see wide applications in many other problems.

<br><br>
Download the paper at
<a href=https://arxiv.org/abs/1801.07455>[Arxiv Preprint]</a>.
<br>
Checkout the code and models this
<a href=https://github.com/yysijie/st-gcn>[Github link]</a>.
]]>
</description>
</item>

<!-- SSN code release -->
<item>
<title>Code and models of SSN (ICCV 2017) are released.</title>
<link>https://github.com/yjxiong/action-detection</link>
<guid>https://github.com/yjxiong/action-detection</guid>
<pubDate>Wednesday, 20 Sep 2017 00:00:00 GMT</pubDate>
<description>
<![CDATA[ 
After months of works, we are now releasing the code and models of the SSN frameworks, implemented in PyTorch. The paper describing SSN has been accepted to ICCV 2017. We used SSN in this year's ActivityNet challenge and got the 2nd place with single models in temporal action deteciton track.

As well as the standard models with ImageNet pretraining, we released the models with Kinetics pretraining, which boosts the detection performance by a good margin.

Big congrats to Yue Zhao for his first paper to present in ICCV!

<br><br>
Check out the code and models this <a href=https://github.com/yjxiong/action-detection>[Github link]</a>. 
]]>
</description>
</item>

<!-- Kinetics -->
<item>
<title>Kinetics pretrained TSN model weights.</title>
<link>http://yjxiong.me/others/kinetics_action/</link>
<guid>http://yjxiong.me/others/kinetics_action/</guid>
<pubDate>Tuesday, 15 Aug 2017 00:00:00 GMT</pubDate>
<description>
<![CDATA[ 
<a href="https://deepmind.com/research/open-source/open-source-datasets/kinetics/">Kinetics Human Action Dataset</a> is a large-scale human action recogntion dataset comprising over 300,000 videos. We are releasing the TSN model weights pretrained on this dataset. Extensive transfer learning experiments confirmed that TSN models pretrained on Kinetics leads to significant performance boost compared with sole ImageNet pretraining. The model weights with CNN architectures BNInception and InceptionV3, as well as transfer learning results can be found on the website below.

<br><br>
<a href=http://yjxiong.me/others/kinetics_action>[TSN on Kinetics Website]</a>. 
]]>
</description>
</item>

<!-- TSN Pytorch -->
<item>
<title>The PyTorch implementation of TSN is released.</title>
<link>https://github.com/yjxiong/tsn-pytorch</link>
<guid>https://github.com/yjxiong/tsn-pytorch</guid>
<pubDate>Tuesday, 15 Aug 2017 00:00:00 GMT</pubDate>
<description>
<![CDATA[ 
Welcome to the modern era of deep learning framework! To facilitate research efforts and keep up with the evolution of DL frameworks, we reimplemented TSN under PyTorch. 
<br>
The reimplementation process is quite smooth thanks to the design of PyTorch. The coding takes less than one day. I have tested the classification performance on UCF101 and Kinetics to confirm that it matches the original Caffe implementation of TSN. So, happy experimenting!

<br><br>
<a href=https://github.com/yjxiong/tsn-pytorch>[Github link]</a>. 
]]>
</description>
</item>

<!-- SSN ICCV17 -->
<item>
<title>The SSN temporal action detection paper is accepted to ICCV2017</title>
<link>http://yjxiong.me/others/ssn/</link>
<guid>http://yjxiong.me/others/ssn/</guid>
<pubDate>Sunday, 30 Jul 2017 00:00:00 GMT</pubDate>
<description>
<![CDATA[

The paper titled "Temporal Action Detection with Structured Segment Networks" (SSN) is accepted for publication at ICCV 2017. This paper describes our latest approach towards temporal action detection. 

Our runner-up entry in ActivityNet 2017 features is an exact realization of SSN. 
<br>
<br>The code and models are released to foster future research efforts. 
<br>
See you in Venice!
]]>

</description>

</item>

<!-- CVPR17 -->
<item>
<title>Our UntrimmedNets paper is presented at CVPR2017</title>
<link>http://openaccess.thecvf.com/content_cvpr_2017/html/Wang_UntrimmedNets_for_Weakly_CVPR_2017_paper.html</link>
<guid>http://openaccess.thecvf.com/content_cvpr_2017/html/Wang_UntrimmedNets_for_Weakly_CVPR_2017_paper.html</guid>
<pubDate>Tuesday, 30 Jul 2017 00:00:00 GMT</pubDate>
<description>
<![CDATA[ 
The paper titled "UntrimmedNets for Weakly Supervised Action Recognition and Detection" is successfully presented at CVPR2017. This paper describes our latest approach towards weakly supervised action modeling, where only video level categorical labels are available for untrimmed web videos. 

<br><br>
The code and models are released <a href=https://github.com/wanglimin/UntrimmedNet>here</a>. 
]]>
</description>
</item>

<!-- First -->
<item>
<title>RSS feed is online</title>
<link>http://yjxiong.me</link>
<guid>http://yjxiong.me</guid>
<pubDate>Tuesday, 30 Jul 2017 00:00:00 GMT</pubDate>
<description>
I have made a RSS feed to reflect my activities. Subscribe if you are interested!
</description>
</item>
</channel>
</rss>
