Our guest speaker is Yuki Asano from the University of Amsterdam.

You are all cordially invited to the CVG Seminar on the 9th of December at 2:30 p.m. CET

• via Zoom (passcode is 303207).

Abstract

It is a 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 and how this can be leveraged to unsupervisedly segment objects in images [1]. Finally, as augmentations are crucial for all of the self-supervised learning, we will analyze these in more detail in a recent preprint [2]. Here, we show that it is possible to extrapolate to semantic classes such as those of ImageNet using just a single datum as visual input when combined with strong augmentations.

[1] Self-Supervised Learning of Object Parts for Semantic Segmentation [arxiv]

[2] Extrapolating from a Single Image to a Thousand Classes using Distillation [arxiv]

Bio

Yuki Asano is an assistant professor for computer vision and machine learning at the Qualcomm-UvA lab at the University of Amsterdam, where he works with Cees Snoek, Max Welling and Efstratios Gavves. His current research interests are multi-modal and self-supervised learning and ethics in computer vision. Prior to his current appointment, he finished his PhD at the Visual Geometry Group (VGG) at the University of Oxford working with Andrea Vedaldi and Christian Rupprecht. During his time as a PhD student, he also interned at Facebook AI Research and worked at TransferWise. Prior to the PhD, he studied physics at the University of Munich (LMU) and Economics in Hagen as well as a MSc in Mathematical Modelling and Scientific Computing at the Mathematical Institute in Oxford.