Anomalies and outliers in visual data can significantly impact the performance and reliability of computer vision systems. As the applications of computer vision continue to grow across various domains, the need for robust and efficient anomaly detection techniques becomes paramount. This workshop aims to bring together researchers, industry experts, and practitioners to explore the latest advancements in automated anomaly detection methods specifically tailored for computer vision tasks. The primary objective of this workshop is to provide a platform for researchers to share and discuss their cutting-edge research in the field of anomaly detection in computer vision. We seek to foster collaboration, encourage knowledge exchange, and promote the development of novel approaches to tackle the challenges posed by anomalies in visual data. The expected outcome of this workshop is to foster a vibrant exchange of knowledge and ideas among researchers and practitioners in the field. Through insightful presentations, discussions, and interactive sessions, participants will gain a comprehensive understanding of cutting-edge AI-based methodologies for anomaly detection in computer vision. The workshop aims to encourage collaborations, inspire new research directions, and promote the development of innovative solutions to address real-world challenges in anomaly detection. As a result, attendees will leave the workshop equipped with valuable insights and practical tools that will contribute to advancements in anomaly detection techniques, ultimately leading to more robust and reliable computer vision systems across various domains.
We invite researchers and practitioners to submit their original research contributions to the 1st Workshop on Automated Spatial and Temporal Anomaly Detection (ASTAD), held as part of WACV 2024. This workshop aims to explore the latest advancements and novel approaches in anomaly detection using AI techniques within the domain of computer vision. Topics of Interest (but not limited to):
Submission Site: Microsoft CMT
Jan. 08, 2024 (Time below is in Hawaii local time)
The important dates are as follows:
Carnegie Mellon University
Dr. Akoglu is the Heinz College Dean’s Associate Professor at Carnegie Mellon University’s Heinz College of Information Systems and Public Policy. She also hold courtesy appointments at the Machine Learning Department (MLD) and the Computer Science Department (CSD) of School of Computer Science (SCS). At Heinz, she directs the Data Analytics Techniques Algorithms (DATA) Lab. Her research interests are broadly in data mining, graph mining, machine learning, and knowledge discovery, with specific focus on anOmaLiEs—identifying and characterizing ‘what stands out’ in large-scale, time-varying, multi-modal data sources through scalable computational methods.
Dr Ye Zhu is a senior lecturer of computer science at the School of Information Technology at Deakin University. He is also the Data to Intelligence (D2i) Research Centre HDR coordinator and Master of Data Science Course CPL Officer. He received a PhD degree in Artificial Intelligence with a Mollie Holman Medal for the best doctoral thesis of the year from Monash University in 2017. Dr Zhu joined Deakin University as a postdoc research fellow in complex system data analytics in July 2017 and then became a lecturer in Feb 2019. Dr Zhu is an IEEE senior member and ACM member.
Narges Armanfard is a Tenure-Track Assistant Professor of Electrical and Computer Engineering at McGill University, and Mila - Quebec AI Institute. She is also affiliated with McGill Centre for Intelligent Machines (CIM), McGill initiative in Computational Medicine (MiCM) and McGill Institute for Aerospace Engineering (MIAE). Before joining McGill, she was a postdoctoral researcher in the Intelligent Assistive Technology and Systems Lab at the University of Toronto and University Health Network. She obtained her PhD degree in Electrical and Computer Engineering from McMaster University.
Hadi received his Bachelor’s degree in Electrical Engineering from Sharif University of Technology, Iran. He started as an MSc student at the iSMART Lab. and fast-tracked to PhD. He has received Graduate Excellence Fellowship Award (GEF), McGill Engineering Doctoral Award (MEDA), GREAT Award, and AGE-WELL Award. He is currently doing research on multi-modal and multi-variate anomaly detection in time-series data.
Khanh received her Master’s degree at Gwangju Institute of Science and Technology (GIST), South Korea. She was a researcher at Seoul National University Hospital (SNUH), South Korea, before moving to Montreal for her PhD. She has received the prestigious McGill Engineering Doctoral Award (MEDA), GREAT Award and is nominated for the prestigious Vanier Scholarship. Her research focuses on time-series data anomaly detection using generative models and graphs.