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<p class="MsoNormal"><span
style="font-family:"Aptos",sans-serif"><o:p> </o:p></span></p>
<p class="MsoNormal"><o:p> </o:p></p>
<p class="elementtoproof" style="text-align:center"
align="center"><b><span style="color:black">CALL FOR PAPERS</span></b><o:p></o:p></p>
<p style="text-align:center" align="center"><b><i><span
style="font-family:"Times New Roman",serif;color:black"> </span></i></b><o:p></o:p></p>
<p style="text-align:center" align="center"><b><i><span
style="font-size:14.0pt;font-family:"Times New Roman",serif;color:black">Big
Visual Data Analytics (BVDA) Workshop</span></i></b><b><span
style="font-family:"Times New Roman",serif;color:black"> at </span></b><b><span
style="font-size:14.0pt;font-family:"Times New Roman",serif;color:black">ICIP
2024</span></b><o:p></o:p></p>
<p style="text-align:center" align="center"><b><span
style="font-family:"Times New Roman",serif;color:black"> </span></b><o:p></o:p></p>
<p style="text-align:center" align="center"><b><span
style="font-family:"Times New Roman",serif;color:black">IEEE
International Conference on Image Processing, 27-30
October 2024, Abu Dhabi, UAE</span></b><o:p></o:p></p>
<p><b><span
style="font-family:"Times New Roman",serif;color:black"> </span></b><o:p></o:p></p>
<p style="margin-top:6.0pt;text-align:justify"><span
style="font-family:"Times New Roman",serif;color:black">We
invite researchers and practitioners working on various
aspects of <b>big visual data analytics</b> to submit
their work to the <b>Big Visual Data Analytics (BVDA)
Workshop</b>, organized in conjunction with the<b> IEEE
International Conference on Image Processing (ICIP)
2024. </b>The ever-increasing visual data availability
leads to repositories or streams characterized by big data
volumes, velocity (acquisition and processing speed),
variety (e.g., RGB or RGB-D or hyperspectral images) and
complexity (e.g., video data and point clouds). Their
processing necessitates novel and advanced visual analysis
methods, in order to unlock their potential across diverse
domains.</span><o:p></o:p></p>
<p style="margin-top:6.0pt;text-align:justify"><span
style="font-family:"Times New Roman",serif;color:black">The <b>BVDA
Workshop</b> aims to explore this rapidly evolving field
encompassing cutting-edge methods, emerging applications,
and significant challenges in extracting meaning and value
from large-scale visual datasets. From high-throughput
biomedical imaging and autonomous driving sensors to
satellite imagery and social media platforms, visual data
has permeated nearly every aspect of our lives. Analyzing
this data effectively requires efficient tools that go
beyond traditional methods, leveraging advancements in
machine learning, computer vision and data science.
Exciting new developments in these fields are already
paving the way for <b>fully and semi-automated visual
data analysis workflows at an unprecedented scale.</b>
This workshop will provide a platform for researchers and
practitioners to discuss recent breakthroughs and
challenges in big visual data analytics, explore novel
applications across diverse domains (e.g., environment
monitoring, natural disaster management, robotics, urban
planning, healthcare, etc.), as well as for fostering
interdisciplinary collaborations between computer vision,
data science, machine learning, and domain experts. Its
ultimate goal is to help identify promising research
directions and pave the way for future innovations.</span><o:p></o:p></p>
<p style="margin-top:6.0pt;text-align:justify"><span
style="font-family:"Times New Roman",serif;color:black">The
BVDA Workshop delves deeper into specific aspects of big
visual data, complementing the broader ICIP themes. Thus
it can generate new research interest and collaborations
within the main conference community, while attracting
researchers and practitioners specifically interested in
big visual data analytics. Its interdisciplinary nature,
its focus on cutting-edge areas (e.g., large
Vision-Language Models, distributed deep neural
architectures, fast generative models, etc.) and its
synergies with neighboring fields (e.g.,
privacy-preserving analytics, real-time visual analytics,
ethical considerations, etc.) broaden the discussion.</span><o:p></o:p></p>
<p style="text-align:justify"><b><span
style="font-family:"Times New Roman",serif;color:black"> </span></b><o:p></o:p></p>
<p style="text-align:justify"><b><span
style="font-family:"Times New Roman",serif;color:black">Topics
of interest</span></b><span
style="font-family:"Times New Roman",serif;color:black"> include
(non-exhaustively) the following ones:</span><o:p></o:p></p>
<p class="MsoNormal"
style="margin-left:72.0pt;text-align:justify;text-indent:-18.0pt;mso-list:l0 level1 lfo2"><!--[if !supportLists]--><span
style="font-size:10.0pt;font-family:Symbol;color:black"><span
style="mso-list:Ignore">·<span
style="font:7.0pt "Times New Roman"">
</span></span></span><!--[endif]--><span
style="color:black">Scalable algorithms and architectures
for big visual data processing and analysis.<o:p></o:p></span></p>
<p class="MsoNormal"
style="margin-left:72.0pt;text-align:justify;text-indent:-18.0pt;mso-list:l0 level1 lfo2"><!--[if !supportLists]--><span
style="font-size:10.0pt;font-family:Symbol;color:black"><span
style="mso-list:Ignore">·<span
style="font:7.0pt "Times New Roman"">
</span></span></span><!--[endif]--><span
style="color:black">High-performance computing,
distributed and parallel processing, efficient data
storage and retrieval for big visual data analysis.<o:p></o:p></span></p>
<p class="MsoNormal"
style="margin-left:72.0pt;text-align:justify;text-indent:-18.0pt;mso-list:l0 level1 lfo2"><!--[if !supportLists]--><span
style="font-size:10.0pt;font-family:Symbol;color:black"><span
style="mso-list:Ignore">·<span
style="font:7.0pt "Times New Roman"">
</span></span></span><!--[endif]--><span
style="color:black">Deep learning architectures for
large-scale visual content understanding, search &
retrieval: Convolutional Neural Networks (CNNs),
Transformers, Self-Supervised Learning, etc.<o:p></o:p></span></p>
<p class="MsoNormal"
style="margin-left:72.0pt;text-align:justify;text-indent:-18.0pt;mso-list:l0 level1 lfo2"><!--[if !supportLists]--><span
style="font-size:10.0pt;font-family:Symbol;color:black"><span
style="mso-list:Ignore">·<span
style="font:7.0pt "Times New Roman"">
</span></span></span><!--[endif]--><span
style="color:black">Big visual data summarization.<o:p></o:p></span></p>
<p class="MsoNormal"
style="margin-left:72.0pt;text-align:justify;text-indent:-18.0pt;mso-list:l0 level1 lfo2"><!--[if !supportLists]--><span
style="font-size:10.0pt;font-family:Symbol;color:black"><span
style="mso-list:Ignore">·<span
style="font:7.0pt "Times New Roman"">
</span></span></span><!--[endif]--><span
style="color:black">Decentralized/distributed DNN
architectures for big visual data analysis.<o:p></o:p></span></p>
<p class="MsoNormal"
style="margin-left:72.0pt;text-align:justify;text-indent:-18.0pt;mso-list:l0 level1 lfo2"><!--[if !supportLists]--><span
style="font-size:10.0pt;font-family:Symbol;color:black"><span
style="mso-list:Ignore">·<span
style="font:7.0pt "Times New Roman"">
</span></span></span><!--[endif]--><span
style="color:black">Cloud/edge computing architectures for
big visual data analysis.<o:p></o:p></span></p>
<p class="MsoNormal"
style="margin-left:72.0pt;text-align:justify;text-indent:-18.0pt;mso-list:l0 level1 lfo2"><!--[if !supportLists]--><span
style="font-size:10.0pt;font-family:Symbol;color:black"><span
style="mso-list:Ignore">·<span
style="font:7.0pt "Times New Roman"">
</span></span></span><!--[endif]--><span
style="color:black">Multimodal big visual data analysis.<o:p></o:p></span></p>
<p class="MsoNormal"
style="margin-left:72.0pt;text-align:justify;text-indent:-18.0pt;mso-list:l0 level1 lfo2"><!--[if !supportLists]--><span
style="font-size:10.0pt;font-family:Symbol;color:black"><span
style="mso-list:Ignore">·<span
style="font:7.0pt "Times New Roman"">
</span></span></span><!--[endif]--><span
style="color:black">Large Vision-Language
Models/Foundation Models.<o:p></o:p></span></p>
<p class="MsoNormal"
style="margin-left:72.0pt;text-align:justify;text-indent:-18.0pt;mso-list:l0 level1 lfo2"><!--[if !supportLists]--><span
style="font-size:10.0pt;font-family:Symbol;color:black"><span
style="mso-list:Ignore">·<span
style="font:7.0pt "Times New Roman"">
</span></span></span><!--[endif]--><span
style="color:black">Fast generative models for visual
data: Synthesizing realistic images/videos, data
augmentation, in-painting and manipulation.<o:p></o:p></span></p>
<p class="MsoNormal"
style="margin-left:72.0pt;text-align:justify;text-indent:-18.0pt;mso-list:l0 level1 lfo2"><!--[if !supportLists]--><span
style="font-size:10.0pt;font-family:Symbol;color:black"><span
style="mso-list:Ignore">·<span
style="font:7.0pt "Times New Roman"">
</span></span></span><!--[endif]--><span
style="color:black">Fast Interpretability and
eXplainability (XAI) of visual analytics models:
Understanding and communicating model decisions, trust and
bias in AI systems.<o:p></o:p></span></p>
<p class="MsoNormal"
style="margin-left:72.0pt;text-align:justify;text-indent:-18.0pt;mso-list:l0 level1 lfo2"><!--[if !supportLists]--><span
style="font-size:10.0pt;font-family:Symbol;color:black"><span
style="mso-list:Ignore">·<span
style="font:7.0pt "Times New Roman"">
</span></span></span><!--[endif]--><span
style="color:black">Privacy-preserving analytics in the
context of big visual data: Secure data processing,
differential privacy, federated learning.<o:p></o:p></span></p>
<p class="MsoNormal"
style="margin-left:72.0pt;text-align:justify;text-indent:-18.0pt;mso-list:l0 level1 lfo2"><!--[if !supportLists]--><span
style="font-size:10.0pt;font-family:Symbol;color:black"><span
style="mso-list:Ignore">·<span
style="font:7.0pt "Times New Roman"">
</span></span></span><!--[endif]--><span
style="color:black">Visual analytics for real-time
applications: Efficient analysis of visual streaming data,
edge/fog computing.<o:p></o:p></span></p>
<p class="MsoNormal"
style="margin-left:72.0pt;text-align:justify;text-indent:-18.0pt;mso-list:l0 level1 lfo2"><!--[if !supportLists]--><span
style="font-size:10.0pt;font-family:Symbol;color:black"><span
style="mso-list:Ignore">·<span
style="font:7.0pt "Times New Roman"">
</span></span></span><!--[endif]--><span
style="color:black">Visual analytics for specialized
domains: Remote sensing, natural disaster management,
medical imaging, social media analysis, etc.<o:p></o:p></span></p>
<p class="MsoNormal"
style="margin-left:72.0pt;text-align:justify;text-indent:-18.0pt;mso-list:l0 level1 lfo2"><!--[if !supportLists]--><span
style="font-size:10.0pt;font-family:Symbol;color:black"><span
style="mso-list:Ignore">·<span
style="font:7.0pt "Times New Roman"">
</span></span></span><!--[endif]--><span
style="color:black">Ethical considerations in big visual
data analytics: Data ownership, fairness, accountability,
societal impact.<o:p></o:p></span></p>
<p style="text-align:justify"><b><span
style="font-family:"Times New Roman",serif;color:black"> </span></b><o:p></o:p></p>
<p style="text-align:justify"><span
style="font-family:"Times New Roman",serif;color:black">The
regular ICIP paper template/style must be used for
submission. All accepted contributions will be <b>published
in IEEE Xplore</b>. The paper submission deadline is <b>May
9, 2024</b>.</span><o:p></o:p></p>
<p style="text-align:justify"><b><span
style="font-family:"Times New Roman",serif;color:black"> </span></b><o:p></o:p></p>
<p class="elementtoproof" style="text-align:justify"><b><span
style="color:black">For further details and submission
instructions visit: </span></b><span
style="font-size:11.0pt;font-family:"Calibri",sans-serif;color:black"><a
href="https://icarus.csd.auth.gr/cfp-bvda-icip24-workshop/"
moz-do-not-send="true" class="moz-txt-link-freetext">https://icarus.csd.auth.gr/cfp-bvda-icip24-workshop/</a></span><o:p></o:p></p>
<p class="elementtoproof" style="text-align:justify"><o:p> </o:p></p>
<p class="elementtoproof" style="text-align:justify"><o:p> </o:p></p>
<p class="elementtoproof" style="text-align:justify"><span
style="color:black">Organizers</span><o:p></o:p></p>
<p class="elementtoproof" style="text-align:justify"><o:p> </o:p></p>
<p class="elementtoproof" style="text-align:justify"><span
style="color:black">Prof. Ioannis Pitas: Chair of the
International AI Doctoral Academy (<a
href="https://www.i-aida.org/"
title="https://www.i-aida.org/" moz-do-not-send="true">AIDA</a>),
Director of the Artificial Intelligence and Information
analysis (<a href="https://aiia.csd.auth.gr/"
title="https://aiia.csd.auth.gr/" moz-do-not-send="true">AIIA</a>)
Lab,</span><o:p></o:p></p>
<p class="elementtoproof" style="background:white"><span
style="color:black">Aristotle University of Thessaloniki,
Greece.</span><o:p></o:p></p>
<p class="elementtoproof" style="background:white"><o:p> </o:p></p>
<p class="elementtoproof" style="background:white"><span
style="color:black">Prof. Massimo Villari: University of
Messina, Italy.</span><o:p></o:p></p>
<p class="elementtoproof" style="background:white"><o:p> </o:p></p>
<p class="elementtoproof" style="background:white"><span
style="color:black">Dr. Ioannis Mademlis: Postdoctoral
researcher at the Harokopio University of Athens.</span><o:p></o:p></p>
<p class="MsoNormal"><span
style="font-family:"Aptos",sans-serif"><o:p> </o:p></span></p>
</div>
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