Modern day computer vision tasks requires efficient solution to problems such as image
recognition, natural language processing, object detection, object segmentation and …

Modern day computer vision tasks requires efficient solution to problems such as image
recognition, natural language processing, object detection, object segmentation and …

Accurate and reliable traffic forecasting for complicated transportation networks is of vital
importance to modern transportation management. The complicated spatial dependencies …

Deep Neural Networks (DNNs) have made significant improvements to reach the desired
accuracy to be employed in a wide variety of Machine Learning (ML) applications. Recently …

Capsule network is a promising model in computer vision. It has achieved excellent results
on simple datasets such as MNIST, but the performance deteriorates as data becomes …

Capsule network (CapsNet) is a novel type of network that can retain spatial information,
because each capsule can integrate more information than scalar-output features. However …

Abstract Classical Convolutional Neural Networks (CNNs) have been the benchmark for
most object classification and face recognition tasks despite their major shortcomings …

In the past few years, Capsule Networks (CapsNets) have taken the spotlight compared to
traditional convolutional neural networks (CNNs) for image classification. Unlike CNNs …

The capsule network (CapsNet) is a promising model in computer vision. It has achieved
excellent results on MNIST, but it is still slightly insufficient in real images. Deepening …

Capsule Networks (CapsNets) are able to hierarchically preserve the pose relationships
between multiple objects for image classification tasks. Other than achieving high accuracy …