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<!DOCTYPE html>
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<head>
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<meta property="og:image" content="https://nchappa.github.io/assets/img/publication_preview/hattflow.png" />
<title>HAtt-Flow: Hierarchical Attention-Flow Mechanism for Group Activity Scene Graph Generation in Videos</title>
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<h1 class="title is-1 publication-title">HAtt-Flow: Hierarchical Attention-Flow Mechanism for Group Activity Scene Graph Generation in Videos</h1>
<div class="is-size-5 publication-authors">
<!-- Paper authors -->
<div class="is-size-5 publication-authors">
<span class="author-block"> <a href="https://uark-cviu.github.io/" target="_blank">https://uark-cviu.github.io/</a></span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"> <a href="https://nchappa.github.io/" target="_blank">Naga VS Chappa</a>, </span>
<span class="author-block"> <a href="https://pha-nguyen.github.io/" target="_blank">Pha Nguyen</a>, </span>
<span class="author-block"> Page Daniel Dobbs, </span>
<span class="author-block"> <a href="https://scholar.google.com/citations?user=JPAl8-gAAAAJ" target="_blank">Khoa Luu</a></span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block">University of Arkansas </span>
</div>
<div class="column has-text-centered">
<h2 class="title">Sensors Journal</h2>
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<span class="link-block"> <a href="https://arxiv.org/abs/2312.07740" target="_blank" class="external-link button is-normal is-rounded"> <span class="icon"> <i class="fas fa-file-pdf"></i> </span> <span>Paper</span> </a> </span>
<span class="link-block"> <a href="#annotations" target="_self" class="external-link button is-normal is-rounded"> <span class="icon"> <i class="fas fa-download"></i> </span> <span>Dataset</span> </a> </span>
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type="video/mp4"> -->
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<h2 class="subtitle has-text-centered">
Teaser video coming soon.
</h2>
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</section>
<!-- End teaser video -->
<!-- Paper abstract -->
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<h2 class="title is-3">Abstract</h2>
<div class="content has-text-justified">
<p>
Group-activity scene graph (GASG) generation is a challenging task in computer vision, aiming to anticipate
and describe relationships between subjects and objects in video sequences. Traditional video scene graph
generation (VidSGG) methods focus on retrospective
analysis, limiting their predictive capabilities. To enrich the scene-understanding capabilities,
we introduced a GASG dataset extending the JRDB dataset with nuanced annotations involving
appearance, interaction, position, relationship, and situation attributes. This work also introduces an
innovative approach, a Hierarchical Attention–flow (HAtt-Flow) mechanism, rooted in flow network
theory to enhance GASG performance. Flow–attention incorporates flow conservation principles,
fostering competition for sources and allocation for sinks, effectively preventing the generation of
trivial attention. Our proposed approach offers a unique perspective on attention mechanisms, where
conventional “values” and “keys” are transformed into sources and sinks, respectively, creating a
novel framework for attention-based models. Through extensive experiments, we demonstrate the
effectiveness of our Hatt-Flow model and the superiority of our proposed flow–attention mechanism.
This work represents a significant advancement in predictive video scene understanding, providing
valuable insights and techniques for applications that require real-time relationship prediction in
video data.
</p>
</div>
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</section>
<!-- End paper abstract -->
<section class="section hero is-small" id="annotations">
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<h2 class="title is-3 has-text-centered">Annotations</h2>
<div class="column">
<h2 class="title is-5"> v1.0: </h2>
<div class="content"><a href="" target="_blank">[Train annotations]</a>, <a href="" target="_blank">[Val annotations]</a></div>
<h2 class="title is-5"> Notes: </h2>
<div class="content">
<ul>
<li>Annotations coming soon.</li>
</ul>
</div>
<h2 class="title is-5"> Licensing: </h2>
<div class="content"> License coming soon. </div>
</div>
</div>
</div>
</div>
</section>
<!--BibTex citation -->
<section class="section" id="BibTeX">
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<h2 class="title">BibTeX</h2>
<pre><code><p>@article{chappa2024hatt, title = {HAtt-Flow: Hierarchical Attention-Flow Mechanism for Group Activity Scene Graph Generation in Videos},
author = {Naga VS Chappa and Pha Nguyen and Ngan Le and Page Daniel Dobbs and Khoa Luu},
journal = {Sensors},
year = 2024
}</p></code></pre>
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</section>
<!--End BibTex citation -->
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