Big Visual Data Analytics (BVDA) Workshop Call for Papers


Big Visual Data Analytics (BVDA) Workshop at ICIP 2024

IEEE International Conference on Image Processing, 
27-30 October 2024, Abu Dhabi, UAE

We invite researchers and practitioners working on various aspects of
big visual data analytics to submit their work to the Big Visual Data
Analytics (BVDA) Workshop, organized in conjunction with the IEEE
International Conference on Image Processing (ICIP) 2024. 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.

The BVDA Workshop 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 fully and
semi-automated visual data analysis workflows at an unprecedented
scale. 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.

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.

Topics of interest include (non-exhaustively) the following ones:

    Scalable algorithms and architectures for big visual data
    processing and analysis.

    High-performance computing, distributed and parallel processing,
    efficient data storage and retrieval for big visual data analysis.

    Deep learning architectures for large-scale visual content
    understanding, search & retrieval: Convolutional Neural Networks
    (CNNs), Transformers, Self-Supervised Learning, etc.

    Big visual data summarization.

    Decentralized/distributed DNN architectures for big visual data

    Cloud/edge computing architectures for big visual data analysis.

    Multimodal big visual data analysis.

    Large Vision-Language Models/Foundation Models.

    Fast generative models for visual data: Synthesizing realistic
    images/videos, data augmentation, in-painting and manipulation.

    Fast Interpretability and eXplainability (XAI) of visual analytics
    models: Understanding and communicating model decisions, trust and
    bias in AI systems.

    Privacy-preserving analytics in the context of big visual data:
    Secure data processing, differential privacy, federated learning.

    Visual analytics for real-time applications: Efficient analysis of
    visual streaming data, edge/fog computing.

    Visual analytics for specialized domains: Remote sensing, natural
    disaster management, medical imaging, social media analysis, etc.

    Ethical considerations in big visual data analytics: Data
    ownership, fairness, accountability, societal impact.

The regular ICIP paper template/style must be used for submission. All
accepted contributions will be published in IEEE Xplore. The paper
submission deadline is April 25, 2024.

For further details and submission instructions visit:


Prof. Ioannis Pitas: Chair of the International AI Doctoral Academy (AIDA), Director of the Artificial Intelligence and Information analysis (AIIA) Lab,
Aristotle University of Thessaloniki, Greece.

Prof. Massimo Villari: University of Messina, Italy.

Dr. Ioannis Mademlis: Postdoctoral researcher at the Harokopio University of Athens.