Seminars

Abstract: In this talk we will present recent works that generally aim at improving the generalization of visual representations on both classification and retrieval tasks. We will start from a recent work on supervised pre-training and will present an approach that aims to improve the transferability of encoders learned in a supervised manner, while retaining … Read more

Abstract: Deep learning applications have been thriving over the last decade in many different domains, including image processing and recognition. The driver for the vibrant development of deep learning have been the availability of abundant data. This talk reviews the main results of our research activities carried out over the last few years. During this … Read more

Abstract: With the advancement of Transformers, especially as far as computer vision is concerned, we are starting to apply end-to-end neural networks, without OCR or other pre-/postprocessing techniques, to the challenges of document understanding and information extraction. I will present developments in this area and discuss potential problems from both theoretical (handling longer sequences) and … Read more

Abstract: In the last decade, many areas of computer vision have progressed to a level supporting reliable, and sometimes impressive, applications. I wil talk about two such domains, fine-grained recognition and visual retrieval. In the fine-grained recognition, I’ll discuss the issue of prior probability shift, classifier calibration and the choice of loss functions driven by … Read more

Abstract: Continual Learning (CL) is a paradigm where an agent learns over time from a stream of data. In this talk, we will discuss how to exploit pretrained models in CL. First, we will talk about “continual pretraining”, a scenario where a large pretrained model is updated over time. The results show that continual pretraining … Read more