webinar by Prof. Xiaoming Liu on “Biometric Recognition in the Era of AI
Generated Content (AIGC)”. Detail on the webinar are given below:
Title: Biometric Recognition in the Era of AI Generated Content (AIGC)
Speaker: Prof. Xiaoming Liu, Michigan State University, USA
When: 24 April 2024, at 10am EST (4pm CEST, 7am PST, 10pm Beijing Time)
Where: Online (Zoom)
Registration: (free, but required):
https://us06web.zoom.us/webinar/register/WN_S_9bjNaOTMGVvD55MYAcVg
*** Talk Summary ***
In recent years we have witnessed impressive progress on AIGC
(Artificial Intelligence Generated Content). AIGC has many applications
in our society, as well as benefits diverse computer vision tasks. In
the context of biometric recognition, we believe that the AIGC era calls
for innovation on both data generation and how to leverage the generated
data. In this talk, I will present a number of efforts that showcases
these innovations, including: 1) how to bridge the gap between the
training data distribution and test data distribution; 2) how to
generate a complete synthetic database to train face recognition models;
3) how to estimate the 3D body shape from an image of clothed human
body; and 4) how to manipulate a human body image by changing its body
pose, clothing style, background, and identity. In the end, we will
briefly overview other research efforts in the Computer Vision Lab at
Michigan State University.
*** About the Speaker ***
Dr. Xiaoming Liu is the MSU Foundation Professor, and Anil and Nandita
Jain Endowed Professor at the Department of Computer Science and
Engineering of Michigan State University (MSU). He is also a visiting
scientist at Google Research. He received Ph.D. degree from Carnegie
Mellon University in 2004. Before joining MSU in 2012, he was a research
scientist at General Electric Global Research. He works on computer
vision, machine learning, and biometrics, especially on face related
analysis and 3D vision. Since 2012, he helps to develop a strong
computer vision area in MSU, who is ranked top 15 in US according to
csrankings.org. He is an Associate Editor of IEEE Transactions on
Pattern Analysis and Machine Intelligence. He has authored more than 200
publications and has filed 35 patents. His work has been cited over
25000 times with an H-index of 76. He is a fellow of IEEE and IAPR.
For more information, visit:
https://ieee-biometrics.org/index.php/activities/webinars
AIIA/AIDA/TEMA Summer School: “CVML Programming Short Course and Workshop on Deep Learning, Computer Vision and Big Data Analytics 2024”, 27-29 August 2024, Thessaloniki, Greece.
April 25th, 2024 
Daniela Lopez de Luise Dear AI/CS/ECE student/scientist/engineer/enthusiast,
the Artificial Intelligence and Information Analysis (AIIA) Lab of Aristotle University of Thessaloniki (AUTH) in cooperation with (TEMA) R&D project, the International AI Doctoral Academy (AIDA), is excited to invite you to register and attend the upcoming “CVML Programming Short Course and Workshop on Deep Learning, Computer Vision and Big Data Analytics 2024” which will take place in Thessaloniki, Greece from August 27th to 29th 2024.
This Summer School offers a three-day short course that delivers an in-depth exploration of programming tools and techniques for addressing a variety of computer vision and deep learning challenges. The course focuses on the fundamentals of deep learning and its applications in Natural Disaster Management. Here's a glimpse of what the course entails:
- Deep neural networks – Convolutional NNs
- 2D Object Tracking in Embedded Systems
- Real Time Object Detection.
- Real-Time Image Segmentation.
- Natural Language Processing.
- Explainability in Computer Vision applications.
Additionally, hands-on programming workshops will be conducted on each topic, providing participants with practical experience and skills enhancement.
Details
Host Institution: Aristotle University of Thessaloniki
27, 28, 29 August 2024, 08:30- 16:30 EEST (UTC + 3 hours)
On-site Participation: KEDEA Building, AUTH, Thessaloniki, Greece
General Registration
Early registration (till 15/07/2024)
Students/Scientists, Engineers from other scientific disciplines having the necessary mathematical background are also welcomed to register.
Special Registration for AIDA Students*
On top of the above registration, also enroll on this course using the “ENROLL ON THIS COURSE” button on the AIDA course page, so that this course is included on your AIDA Certificate of Course Attendance upon successful completion of the program.
*AIDA Students are PhD students/candidates or Postdoc researchers belonging to any AIDA member.
Upon successful completion of the course, participants will receive a certificate of attendance issued by Aristotle University of Thessaloniki.
For more details, please visit: https://icarus.csd.auth.gr/cvml-programming-short-course-and-workshop-on-deep-learning-computer-vision-and-big-data-analytics-2024/
This summer school is technically sponsored by TEMA , AI.BIG Cluster, AIDA, AI4Media R&D projects.
School Organizer: 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
Workshop on Recent Advances in Biometrics and its Applications | 2024 47th International Conference on Telecommunications and Signal Processing
April 25th, 2024 Daniela Lopez de Luise Call for papers – Workshop on “Recent Advances in Biometrics and its Applications”, in the 47th International Conference on Telecommunications and Signal Processing (TSP)
July 10-12, 2024 (Virtual Conference, https://tsp.vutbr.cz/ )
https://tsp.vutbr.cz/8th-workshop-biometrics/
Deadline : May 6, 2024
University of Udine, Italy Months Research Fellow Position – Object Tracking in First-Person and Third-Person Videos
April 23rd, 2024 Daniela Lopez de Luise
CFP – Big Visual Data Analytics (BVDA) Workshop at ICIP, 27-30 October 2024, Abu Dhabi, UAE
April 23rd, 2024 Daniela Lopez de Luise 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 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 9, 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.