Research

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