Through the success of artificial neural networks (ANNs) in different domains, intense
research has been recently centered on changing the networks architecture to optimize the …

In humans, Attention is a core property of all perceptual and cognitive operations. Given our
limited ability to process competing sources, attention mechanisms select, modulate, and …

Modern robotic systems are often equipped with a direct three-dimensional (3-D) data
acquisition device, eg, LiDAR, which provides a rich 3-D point cloud representation of the …

In recent years, forest-fire monitoring methods represented by deep learning have been
developed rapidly. The use of drone technology and optimization of existing models to …

Hyperspectral Remote Rensing Image (HRSI) classification based on Convolution Neural
Network (CNN) has become one of the hot topics in the field of remote sensing. However …

Abstract We introduce Spatial Group Convolution (SGC) for accelerating the computation of
3D dense prediction tasks. SGC is orthogonal to group convolution, which works on spatial …

Recognising in what type of environment one is located is an important perception task. For
instance, for a robot operating indoors it is helpful to be aware whether it is in a kitchen, a …

The use of deep learning for fault diagnosis is already a common approach. However,
integrating discriminative information of fault types and scales into deep learning models for …

We present a novel class of convolutional neural networks (CNNs) for set functions, ie, data
indexed with the powerset of a finite set. The convolutions are derived as linear, shift …

Convolutional neural networks (CNNs) are a powerful tool for pattern recognition and
computer vision, but they do not scale well to higher-dimensional inputs, because of the …