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 analysis.
• 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 May 13, 2024.
 
For further details and submission instructions visit: https://icarus.csd.auth.gr/cfp-bvda-icip24-workshop/
 
 
Organizers
 
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.