Abstract: Virtual reality (VR) is a rapidly expanding medium that presents both challenges and opportunities. As VR techniques and applications continue to advance, it becomes increasingly important to create immersive experiences that can fully exploit its potential. Understanding and predicting human visual behavior and user attention is an essential factor in achieving this goal. This … Read more
Abstract: Adversarial attacks exploit vulnerabilities in machine learning models by introducing subtle perturbations to input data, leading to incorrect predictions. Rigorous testing of machine learning models against these attacks is often impractical for modern deep learning systems. For these reasons, empirical methods, optimizing adversarial perturbations via gradient descent, are often used to provide robustness evaluations. … Read more
Abstract: Pathology is the medical specialty at the core of disease understanding, diagnosis and patient management, but suffers from subjective quantifications, shortage of pathologists and workload increases due to the rise of cancer incidence.Digital pathology (digitizing histology tissue sections into images) enables the use of AI in pathology image analysis, a field known as Computational … Read more
Abstract: Every day, we find ourselves immersed in the age of data. Every hour, vast amounts of media content flood social media and user-generated platforms. For instance, over 500 hours of video are uploaded to YouTube every minute, as of February 2020. Meanwhile, the metaverse is gaining popularity, boasting approximately 400 million monthly users with … Read more
Abstract: Continually learning from a stream of non-stationary data is difficult for deep neural networks. When these networks are trained on something new, they tend to quickly forget what was learned before. In recent years, considerable progress has been made towards overcoming such “catastrophic forgetting”, predominantly thanks to approaches that add replay or regularization terms … Read more
Abstract: Image quality assessment (IQA) methods have been long desired to enhance the performance ofcomputer vision related tasks, such as image restoration, image compression and image synthesis. However, the variation in image contents and complexity in image distortions are hindering IQA methods from being applied in practical usages. In this presentation, we explore to develop … Read more
Abstract: The Particle and Astrophysical Science Computing Center (PIC) is a multifaceted data centre located adjacent to the Computer Vision Center (CVC). In this presentation, we outline various projects within PIC, including its role as a high-throughput facility supporting experiments such as the Euclid satellite, Large Hadron Collider (LHC) experiments, magic telescopes, and the Cherenkov … Read more
Abstract: In this seminar, we will first cover irresponsible AI: (1) discrimination (e.g., facial recognition, justice); (2) phrenology (e.g., biometric-based predictions); (3) limitations (e.g., human incompetence, minimal adversarial AI), (4) indiscriminate use of computing resources (e.g., large language models) and (5) the impact of generative AI (disinformation, mental health and copyright issues). These examples do … Read more
Abstract: In this seminar, Dr. Silvana Silva will present her research during the past years within the field of neuroscience, which has focused on data modeling and analysis using brain images obtained with EEG, MEG, and fMRI. First, a network-theoretical analysis of MEG, where sensors are identified with nodes and the interaction between channel time-series … Read more
Abstract: While artificial intelligence is making ever-larger strides, we cannot neglect the potential for its impact on the way we nurture human intelligence. Education is without a doubt a primary candidate for a beneficial application of generative AI and machine learning. In this talk, Dr. Elitza Maneva will specifically concentrate on their application to the … Read more