One of the main challenges in machine vision relates to the problem of obtaining robust
representation of visual features that remain unaffected by geometric transformations. This …

In this paper we challenge the common assumption that convolutional layers in modern
CNNs are translation invariant. We show that CNNs can and will exploit the absolute spatial …

Modern neural networks use building blocks such as convolutions that are equivariant to
arbitrary 2 D translations. However, these vanilla blocks are not equivariant to arbitrary 3 D …

Omnidirectional cameras offer great benefits over classical cameras wherever a wide field of
view is essential, such as in virtual reality applications or in autonomous robots …

In many machine learning tasks it is desirable that a model's prediction transforms in an
equivariant way under transformations of its input. Convolutional neural networks (CNNs) …

Abstract Deep Neural Networks (DNNs) have been demonstrated to perform exceptionally
well on most recognition tasks such as image classification and segmentation. However …

We introduce a self-supervised learning method that focuses on beneficial properties of
representation and their abilities in generalizing to real-world tasks. The method …

In many computer vision tasks, we expect a particular behavior of the output with respect to
rotations of the input image. If this relationship is explicitly encoded, instead of treated as any …

We describe generalizations of the universal approximation theorem for neural networks to
maps invariant or equivariant with respect to linear representations of groups. Our goal is to …

There is a high demand of 3D data for 360 panoramic images and videos, pushed by the
growing availability on the market of specialized hardware for both capturing (eg …