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<!DOCTYPE html>
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content="USV: Towards Understanding the User-generated Short-form Videos">
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<title>USV: Towards Understanding the User-generated Short-form Videos</title>
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<h1 class="title is-1 publication-title">USV: Towards Understanding the User-generated Short-form Videos</h1>
<div class="is-size-5 publication-authors">
<span>Technical Report 2022</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="mailto:chenghaoyue98@gmail.com" target="_blank">Haoyue Cheng</a>*<sup>1</sup>,</span>
<span class="author-block">
<a href="mailto:xusu@sensetime.com" target="_blank">Su Xu</a>*<sup>2</sup>,</span>
<span class="author-block">
<a href="mailto:liwei.jin97@gmail.com" target="_blank">Liwei Jin</a>*<sup>1</sup>,</span>
<span class="author-block">
Wayne Wu<sup>2</sup>,</span>
<span class="author-block">
Limin Wang<sup>1</sup>,</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block" style='margin-right:0.2em; display:inline-block;'><sup>1</sup>Nanjing University</span>
<span class="author-block" style='margin-right:0.2em; display:inline-block;'><sup>2</sup>SenseTime Research</span>
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</div>
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</div>
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</section>
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<h2 class="title is-2">Abstract</h2>
<div class="content has-text-justified">
<p>
Several large-scale video datasets have been published these years and have advanced the area of video understanding. However, the newly emerged user-generated short-form videos have rarely been studied. This paper presents USV, the User-generated Short-form Video dataset for high-level semantic video understanding. The dataset contains around 245K videos collected from UGC platforms by label queries without extra manual verification and trimming. Although video understanding has achieved plausible improvement these years, most works focus on instance-level recognition, which is not sufficient for learning the representation of the high-level semantic information of videos. Therefore, we further establish two tasks: topic recognition and video-text retrieval on USV. We propose two unified and effective baseline methods called Multi-Modality Fusion Network (MMF-Net) and Video-Text Contrastive Learning (VTCL) to tackle the topic recognition task and video-text retrieval respectively, and carry out comprehensive benchmarks to facilitate future researches.
</p>
</div>
</div>
</div>
<!--/ Abstract. -->
</div>
</section>
<!-- Dataset Info -->
<section>
<div class="container is-max-desktop">
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<h2 class="title is-3 has-text-centered">Dataset Overview</h2>
<h1 class="title is-4 has-text-centered">Part of the label topology</h1>
<div>
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<img class="cover" id="mag" src="./static/images/tsne_2.png" style="width:70%; display: block; margin: auto">
</div>
</div>
<br>
<br>
<h2 class="title is-4 has-text-centered">Dataset Distribution</h2>
<div class="columns-mid is-vcentered">
<!-- <div>-->
<!-- <div>Statistics</div>-->
<div class="column is-4 has-text-centered">
<!-- <div class="interpolation-image">-->
<img src='./static/images/fig_stat_2.png' class="interpolation-image"
alt="Interpolate start reference image." style="width:90%; display: block; margin: auto"/>
</div>
<div class="column is-7 has-text-centered">
<!-- <div class="interpolation-image">-->
<img src='./static/images/part_of_speech_2.png' class="interpolation-image"
alt="Interpolate start reference image." style="width:90%; display: block; margin: auto"/>
</div>
</div>
</div>
</section>
<br>
<br>
<br>
<br>
<!-- Model Architecture -->
<section>
<div class="container is-max-desktop">
<h2 class="title is-3 has-text-centered">Model Architecture</h2>
<div>
<div class="image is-1">
<img src='./static/images/pipeline3.png' class="interpolation-image"
alt="Interpolate start reference image." style="width:70%; display: block; margin: auto"/>
</div>
</div>
</div>
</section>
<br>
<br>
<br>
<br>
<!-- Class Analysis -->
<section>
<div class="container is-max-desktop">
<h2 class="title is-3 has-text-centered">Class Analysis</h2>
<h1 class="title is-4 has-text-centered">Top-10 easiest and hardest classes</h1>
<div>
<div class="image is-1">
<img src='./static/images/class_analyze.png' class="interpolation-image"
alt="Interpolate start reference image." style="width:70%; display: block; margin: auto"/>
</div>
</div>
</div>
</section>
<br>
<br>
<br>
<br>
<!-- Model Zoo Info -->
<section class="section">
<div class="container is-max-desktop">
<div class="columns is-centered has-text-centered">
<div class="column is-12">
<h2 class="title is-3 "> Data Feature</h2>
<table style="margin-left:auto;margin-right:auto;">
<tr>
<th>Type</th>
<th>Model</th>
<th>Pretrained Data</th>
</tr>
<tr>
<td>
<a href=""
target="_blank"> Vision
</a>
</td>
<td>
<a href="https://github.com/open-mmlab/mmaction2"
target="_blank"> TSN-ResNet50 </a>
</td>
<td>ImageNet-USV</td>
</tr>
<tr>
<td>
<a href=""
target="_blank"> Text
</a>
</td>
<td>
<a href="https://github.com/google-research/bert/blob/master/multilingual.md"
target="_blank"> multilingual BERT </a>
</td>
<td>104 languages</td>
</tr>
<tr>
<td>
<a href=""
target="_blank"> Audio
</a>
</td>
<td>Log-Mel Spectrogram</td>
<td>-</td>
</tr>
</table>
</div>
</div>
</div>
</section>
<section class="section" id="Citaton">
<div class="container is-max-desktop content">
<h2 class="title">BibTeX</h2>
<pre><code>
<!-- @article{fu2022styleganhuman,-->
<!-- title={StyleGAN-Human: A Data-Centric Odyssey of Human Generation},-->
<!-- author={Fu, Jianglin and Li, Shikai and Jiang, Yuming and Lin, Kwan-Yee and Qian, Chen and Loy, Chen-Change and Wu, Wayne and Liu, Ziwei },-->
<!-- journal = {arXiv preprint},-->
<!-- volume = {arXiv:2204.11823},-->
<!-- year = {2022}}-->
</code></pre>
</div>
</section>
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