Research

Abstract: Deep Learning justifiably attracted the attention and interest of the scientific community and industry as well as of the wider society and even policy makers. However, the predominant architectures (from Convolutional Neural Networks to Transformers) are hyper-parametric models with weights/parameters being detached from the physical meaning of the object of modelling. They are, essentially, … Read more

Abstract: Semantic and goal-oriented visual navigation is one of the most prominent tasks performed by intelligent species in their daily lives. This task is defined as the ability we have to navigate through our environment, finding targets and enabling interaction with it. Navigation methods used in robotics can be divided into two main categories: geometry-based … Read more

Abstract:  In this talk, Yuki M. Asano will talk about pushing the limits of what can be learnt without using any human annotations. After a first overview of what self-supervised learning is, we will first dive into how clustering can be combined with representation learning using optimal transport ([1] @ ICLR’20), a paradigm still relevant … Read more

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