Seminars

Abstract: The seminar will introduce my artistic practice and research, which explores how ubiquitous technologies can be used to reveal their own material, sensory, and image-making capacities. Through works made with scanners, robotic vacuums, window-cleaning robots, computer mice, printers, smartphone sensors, and thermal paper, I investigate how devices record, respond to, and interact with the … Read more

Abstract: This talk will present an experimental study on the behaviour, scalability, and generative quality of Denoising Diffusion Probabilistic Models (DDPMs) for image synthesis across datasets of increasing complexity.   Three case studies are analysed and compared based on FID (Fréchet Inception Distance) evaluation metric and the performance of downstream classifiers to discriminate the synthetically generated … Read more

Abstract: Neural networks (NNs) excel across a wide range of machine learning tasks due to their flexibility and scalability, but they also pose challenges in privacy, interpretability, robustness, and efficiency—limitations that can be especially critical for large models trained on sensitive data. To tackle these issues, we propose the use of tensor network models. In … Read more

Abstract: I will start by introducing DTLR, our general approach for recognizing text lines, whether printed (OCR) or handwritten (HTR), using Latin, Chinese, or ciphered characters. Most HTR methods have focused on autoregressive decoding, which predicts characters one at a time. In contrast, DTLR processes the entire line at once. Our method shows strong results … Read more

Abstract: We live in a world where all interactions with the environment necessitate a 3D understanding of our surroundings. While humans excel at reasoning about 3D structures from both multi-view and single-view images, replicating this capability in computers remains challenging due to the need to combine mathematically proven geometric knowledge with end-to-end learned priors. In … Read more

Abstract: This presentation provides a short overview of the research conducted by the computer vision research group in Amsterdam (UvA – University of Amsterdam) featuring various research projects and several commercially applied use cases.

Abstract: This presentation is divided into two interconnected parts, exploring the applications and inner workings of Vision-Language contrastive models, with a focus on CLIP-like architectures. In the first part, we examine the application of these models to semantic segmentation tasks. We begin with a brief overview of our previous work in semantic segmentation and multi-branched … Read more

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