The remarkable success of deep learning in various domains relies on the availability of
large-scale annotated datasets. However, obtaining annotations is expensive and requires …

Machine learning has become an increasingly ubiquitous technology, as big data continues
to inform and influence everyday life and decision-making. Currently, in medicine and …

Abstract Iterative Closest Point (ICP) solves the rigid point cloud registration problem
iteratively in two steps:(1) make hard assignments of spatially closest point …

We present a novel clustering objective that learns a neural network classifier from scratch,
given only unlabelled data samples. The model discovers clusters that accurately match …

We present a simple and general framework for feature learning from point cloud. The key to
the success of CNNs is the convolution operator that is capable of leveraging spatially-local …

The cornerstone of neural algorithmic reasoning is the ability to solve algorithmic tasks,
especially in a way that generalises out of distribution. While recent years have seen a surge …

We introduce a self-supervised representation learning method based on the task of
temporal alignment between videos. The method trains a network using temporal cycle …

Permutations and matchings are core building blocks in a variety of latent variable models,
as they allow us to align, canonicalize, and sort data. Learning in such models is difficult …

This paper addresses the problem of cross-view image geo-localization, where the
geographic location of a ground-level street-view query image is estimated by matching it …

Self-supervised learning approaches leverage unlabeled samples to acquire generic
knowledge about different concepts, hence allowing for annotation-efficient downstream …