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 …

Learning to predict agent motions with relationship reasoning is important for many
applications. In motion prediction tasks, maintaining motion equivariance under Euclidean …

Recently, object detection in aerial images has gained much attention in computer vision.
Different from objects in natural images, aerial objects are often distributed with arbitrary …

The big empirical success of group equivariant networks has led in recent years to the
sprouting of a great variety of equivariant network architectures. A particular focus has …

Symmetries and equivariance are fundamental to the generalization of neural networks on
domains such as images, graphs, and point clouds. Existing work has primarily focused on a …

The translation equivariance of convolutional layers enables CNNs to generalize well on
image problems. While translation equivariance provides a powerful inductive bias for …

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 …

The principle of equivariance to symmetry transformations enables a theoretically grounded
approach to neural network architecture design. Equivariant networks have shown excellent …

We present a convolutional network that is equivariant to rigid body motions. The model
uses scalar-, vector-, and tensor fields over 3D Euclidean space to represent data, and …

Recently, the first foundation model developed specifically for image segmentation tasks
was developed, termed the" Segment Anything Model"(SAM). SAM can segment objects in …