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<h4 style="text-align:center">MarineDet: Towards Open-Marine Object Detection</h4>
<p align="center" ,="" style="margin-bottom:12px;">
<a class="simple">Haixin Liang</a><sup>1 #</sup>
<a class="simple">Ziqiang Zheng</a><sup>1 # 📧</sup>
<a class="simple">Zeyu Ma</a><sup>2</sup>
<a href="https://scholar.google.com/citations?user=16iMMwwAAAAJ" class="simple">Sai-Kit Yeung</a><sup>1</sup>
</p>
<p align="center" style="margin-bottom:20px;">
<sup>1</sup>The Hong Kong University of Science and Technology
<span style="display:inline-block; width: 32px"></span>
<sup>2</sup>University of Electronic Science and Technology of China
<br>
<span style="display:inline-block; width: 32px"></span>
<sup>#</sup>co-first author
<span style="display:inline-block; width: 32px"></span>
<sup>📧</sup>corresponding author
<br>
</p>
<div class="publication-links">
<center>
<!-- PDF Link. https://arxiv.org/pdf/2310.01931#:~:text=Through%20an%20in%2Dair%2Dto,from%20ImageNet%20pre%2D%20trained%20weights. -->
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<span>Paper</span>
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<span>Dataset </span>
(comming soon)
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</div>
<div>
<img src="./index_files/marinedet_teaser.jpg" style="width:100%">
</div>
<p align="center">MarineDet dataset consisting of 821 marine-relative object categories to promote and measure open-marine object
detection performance </p>
<div id="teaser" class="container" style="width:100%; margin:0; padding:0">
<h5>Abstract</h5>
<p align="justify">
Marine object detection has gained prominence
in marine research, driven by the pressing need to unravel
oceanic mysteries and enhance our understanding of invaluable
marine ecosystems. There is a profound requirement to efficiently
and accurately identify and localize diverse and unseen marine
entities within underwater imagery. The open-marine object
detection (OMOD for short) is required to detect diverse
and unseen marine objects, performing categorization and
localization simultaneously. To achieve OMOD, we present
MarineDet. We formulate a joint visual-text semantic space
through pre-training and then perform marine-specific training
to achieve in-air-to-marine knowledge transfer. Considering
there is no specific dataset designed for OMOD, we construct
a MarineDet dataset consisting of 821 marine-relative object
categories to promote and measure OMOD performance. The
experimental results demonstrate the superior performance of
MarineDet over existing generalist and specialist object detection
algorithms. To the best of our knowledge, we are the first to
present OMOD, which holds a more valuable and practical
setting for marine ecosystem monitoring and management. Our
research not only pushes the boundaries of marine understanding
but also offers a standard pipeline for OMOD.
<br>
<br>
</p>
</div>
<div id="teaser" class="container" style="width:100%; margin:0; padding:0">
<h5>Video</h5>
<center>
<iframe width="560" height="315" src="./index_files/AQU5Tni13oQ.html" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen=""></iframe>
<!-- <iframe width="560" height="315" src="./MarineDet_files/m44ZO_s4IGo.html" title="MarineDet: A 360∘ 24/7 Real-Time Streaming Camera System for Underwater Applications" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share" allowfullscreen=""></iframe> -->
</center>
</div>
<br>
<div id="teaser" class="container" style="width:100%; margin:0; padding:0">
<h5>The framework overview</h5>
We aim to develop algorithms that can accurately detect, localize, and categorize objects within images, even
when faced with previously unseen object categories. Let <span
class="math inline"><em>O</em></span> represent the set of all possible
object categories, where <span
class="math inline">|<em>O</em> | → ∞</span>, indicating an open-ended
vocabulary. We detect the presence of objects within the image by
generating bounding boxes. We then assign category labels <span
class="math inline"><em>C</em> = {<em>c</em><sub><em>i</em></sub> }</span>
to the detected objects based on the similarity between the semantic
textual embedding of <span
class="math inline"><em>c</em><sub><em>i</em></sub></span> and the
regional features identified by the bounding box, wshere <span
class="math inline"><em>c</em><sub><em>i</em></sub></span> corresponds
to the category label.
<div>
<br>
<img src="./index_files/framework-min-1.png" style="width:100%">
</div>
<center>
<div class="caption">
<p align="justify">
There are two main procedures: 1) pre-training for joint visual-text semantic space construction
and 2) marine-specific training.
</p>
</div>
<br>
</center>
</div>
<div id="teaser" class="container" style="width:100%; margin:0; padding:0">
<h5>Qualitative Results</h5>
<center>
<img src="./index_files/qualitative comparison-1.png" style="width:100%">
<div class="caption">
<p align="justify">
The qualitative comparison between different algorithms. The left part of the dashed line represents the results of seen “Classes” while the right part
shows the results of unseen “Classes”.
</p>
</div>
<br>
</center>
</div>
<div class="section">
<h5>Citation</h5>
<pre style="margin:0"> <code>@article{haixin2023marinedet,
title={MarineDet: Towards Open-Marine Object Detection},
author={Haixin, Liang and Ziqiang, Zheng and Zeyu, Ma and Yeung, Sai-Kit},
journal={arXiv preprint arXiv:2310.01931},
year={2023}
}
</code>
</pre>
</div>
<!-- -->
<br>
<div class="section">
<h5>Acknowledgements</h5>
<p>
<!-- This paper was partially supported by an internal grant from HKUST (R9429). -->
</p>
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<h5>Acknowledgements</h5>
<p align="justify">
This research project is supported by The Innovation and Technology Fund (ITF) and The Marine Conservation Enhancement Fund (MCEF) Hong Kong.
</p>
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{ font-weight: bold; font-size: 14px; text-align: center;}.jvectormap-legend-cnt { position: absolute;}.jvectormap-legend-cnt-h { bottom: 0; right: 0;}.jvectormap-legend-cnt-v { top: 0; right: 0;}.jvectormap-legend { background: black; color: white; border-radius: 3px;}.jvectormap-legend-cnt-h .jvectormap-legend { float: left; margin: 0 10px 10px 0; padding: 3px 3px 1px 3px;}.jvectormap-legend-cnt-h .jvectormap-legend .jvectormap-legend-tick { float: left;}.jvectormap-legend-cnt-v .jvectormap-legend { margin: 10px 10px 0 0; padding: 3px;}.jvectormap-legend-cnt-h .jvectormap-legend-tick { width: 40px;}.jvectormap-legend-cnt-h .jvectormap-legend-tick-sample { height: 15px;}.jvectormap-legend-cnt-v .jvectormap-legend-tick-sample { height: 20px; width: 20px; display: inline-block; vertical-align: middle;}.jvectormap-legend-tick-text { font-size: 12px;}.jvectormap-legend-cnt-h .jvectormap-legend-tick-text { text-align: center;}.jvectormap-legend-cnt-v .jvectormap-legend-tick-text { display: inline-block; vertical-align: middle; line-height: 20px; padding-left: 3px;}</style><style type="text/css">a#clustrmaps-widget-v2, #clustrmaps-widget-v2 { display: block; font-size: 11px; line-height: 13px; margin: 0 auto; padding: 0; position: relative; text-align: center; color: transparent; min-height: 139px; text-decoration: none; /* text-shadow: 1px 1px 0 #01324f; */ border: 0 none;}.clustrmaps-map { position: relative;}#clustrmaps-widget-v2 > .clustrmaps-map-container { background-position: 0 0; background-repeat: no-repeat; position: relative;}#clustrmaps-widget-v2 > .clustrmaps-map-container > .clustrmaps-date { text-align: center; width: 100%; z-index: 10;}#clustrmaps-widget-v2 > .clustrmaps-map-container > .clustrmaps-logo { position: absolute; background-image: url("//clustrmaps.com/assets/clustrmaps/img/logo4-small.png"); bottom: 0px; background-repeat: no-repeat; background-position: center center; z-index: 1; width: 100px; height: 31px; left: 0px;}#clustrmaps-widget-v2 > .clustrmaps-map-container > .clustrmaps-connection { background-position: center center; background-repeat: no-repeat; bottom: 0; color: rgba(255, 255, 255, 0.5); padding: 4px 4px; position: absolute; right: 0; z-index: 1; font-variant: small-caps;}#clustrmaps-widget-v2 > .clustrmaps-bottom-text { letter-spacing: 0px; background: #FFFFFF; color: #000000; text-shadow: none;}#clustrmaps-widget-v2 > .clustrmaps-cursor-click { background-position: center center; background-repeat: no-repeat; display: block; height: 29px; position: absolute; right: 0; top: 56px; width: 30px;}#clustrmaps-widget-v2 > .clustrmaps-map-container > .clustrmaps-connection.clustrmaps-failed { color: rgba(255, 0, 0, 0.8);}/*#clustrmaps-widget-v2 > .clustrmaps-map-container { background-image: url("//clustrmaps.com/images/map_v2_loading.png");}*/#clustrmaps-widget-v2 > .clustrmaps-cursor-click { background-image: url("//clustrmaps.com/assets/clustrmaps/img/cursor_click.png");}#clustrmaps-widget-v2 > .clustrmaps-bottom-text.variation { display: none;}/* CONTROL *//*#clustrmaps-widget-v2.clustrmaps-map-control > .clustrmaps-map-container { background-image: url("//clustrmaps.com/images/map_v2-control.png");}*/#clustrmaps-widget-v2.clustrmaps-map-control > .clustrmaps-bottom-text { -webkit-border-radius: 3px; -moz-border-radius: 3px; border-radius: 3px; border: 1px solid #999; background: #F24D58; background: -moz-linear-gradient(top, #FF636D 50%, #DD2929 50%); background: -webkit-gradient(linear, left top, left bottom, color-stop(50%,#f83737), color-stop(50%,#f83737)); background: linear-gradient(to bottom, #FF636D 50%,#DD2929 51%); filter: progid:DXImageTransform.Microsoft.gradient( startColorstr="#FF636D", endColorstr="#f83737",GradientType=0 ); display: block; margin: 2px auto 0; padding: 3px 14px; color: #FFFFFF; text-shadow: 1px 1px 0px #5B0000; font-weight: 600;}#clustrmaps-widget-v2.clustrmaps-map-control > .clustrmaps-bottom-text:hover { border: 1px solid #888; background: #a3f5a2; background: -moz-linear-gradient(top, #ed8b92 50%, #D76666 50%); background: -webkit-gradient(linear, left top, left bottom, color-stop(50%,#ed8b92), color-stop(50%,#D76666)); background: linear-gradient(to bottom, #ed8b92 50%,#D76666 51%); filter: progid:DXImageTransform.Microsoft.gradient( startColorstr="#ed8b92", endColorstr="#D76666",GradientType=0 );}#clustrmaps-widget-v2.clustrmaps-map-control > .clustrmaps-bottom-text.variation { display: none;}.clstm { text-transform: capitalize !important; position: relative;}#clustrmaps-widget-v2.clustrmaps-map-variation > .clustrmaps-bottom-text { -webkit-border-radius: 3px; -moz-border-radius: 3px; border-radius: 3px; border: 1px solid #999; background: #F24D58; background: -moz-linear-gradient(top, #FF636D 50%, #DD2929 50%); background: -webkit-gradient(linear, left top, left bottom, color-stop(50%,#f83737), color-stop(50%,#f83737)); background: linear-gradient(to bottom, #FF636D 50%,#DD2929 51%); filter: progid:DXImageTransform.Microsoft.gradient( startColorstr="#FF636D", endColorstr="#f83737",GradientType=0 ); display: block; margin: 2px auto 0; padding: 3px 14px; color: #FFFFFF; text-shadow: 1px 1px 0px #5B0000; font-weight: 600;}#clustrmaps-widget-v2.clustrmaps-map-variation > .clustrmaps-bottom-text { display: none;}#clustrmaps-widget-v2.clustrmaps-map-variation > .clustrmaps-bottom-text.variation { display: block;}#clustrmaps-widget-v2.clustrmaps-map-variation > .clustrmaps-bottom-text:hover { border: 1px solid #888; background: #a3f5a2; background: -moz-linear-gradient(top, #ed8b92 50%, #D76666 50%); background: -webkit-gradient(linear, left top, left bottom, color-stop(50%,#ed8b92), color-stop(50%,#D76666)); background: linear-gradient(to bottom, #ed8b92 50%,#D76666 51%); filter: progid:DXImageTransform.Microsoft.gradient( startColorstr="#ed8b92", endColorstr="#D76666",GradientType=0 );}.clustrmaps-visitors, .clustrmaps-date, .clustrmaps-bottom-text { font-family: Arial, Helvetica, sans-serif; text-align: center; font-weight: normal;}.clustrmaps-bottom-text { font-size: 12px;}</style>
</div> -->
</div>
<script type="text/javascript" src="./index_files/jquery.min.js.download"></script>
<script type="text/javascript" src="./index_files/footable.min.js.download"></script>
<script type="text/javascript">
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<!-- End Document
–––––––––––––––––––––––––––––––––––––––––––––––––– -->
<div class="jvectormap-tip"></div><grammarly-desktop-integration data-grammarly-shadow-root="true"><template shadowrootmode="open"><style>
div.grammarly-desktop-integration {
position: absolute;
width: 1px;
height: 1px;
padding: 0;
margin: -1px;
overflow: hidden;
clip: rect(0, 0, 0, 0);
white-space: nowrap;
border: 0;
-moz-user-select: none;
-webkit-user-select: none;
-ms-user-select:none;
user-select:none;
}
div.grammarly-desktop-integration:before {
content: attr(data-content);
}
</style><div aria-label="grammarly-integration" role="group" tabindex="-1" class="grammarly-desktop-integration" data-content="{"mode":"full","isActive":true,"isUserDisabled":false}"></div></template></grammarly-desktop-integration></body></html>