Abstract Knowledge extraction through machine learning techniques has been successfully
applied in a large number of application domains. However, apart from the required …

Transformer has shown excellent performance in remote sensing field with long-range
modeling capabilities. Remote sensing video (RSV) moving object detection and tracking …

The ability to learn richer network representations generally boosts the performance of deep
learning models. To improve representation-learning in convolutional neural networks, we …

Drones, or general UAVs, equipped with cameras have been fast deployed with a wide
range of applications, including agriculture, aerial photography, and surveillance …

Abstract Spiking Neural Networks (SNNs) have gained huge attention as a potential energy-
efficient alternative to conventional Artificial Neural Networks (ANNs) due to their inherent …

Vision transformers (ViTs) inherited the success of NLP but their structures have not been
sufficiently investigated and optimized for visual tasks. One of the simplest solutions is to …

Automation of neural architecture design has been a coveted alternative to human experts.
Various search methods have been proposed aiming to find the optimal architecture in the …

Neural Architecture Search (NAS) has been widely adopted to design accurate and efficient
image classification models. However, applying NAS to a new computer vision task still …

Designing feasible and effective architectures under diverse computational budgets,
incurred by different applications/devices, is essential for deploying deep models in real …

In today's digital age, Convolutional Neural Networks (CNNs), a subset of Deep Learning
(DL), are widely used for various computer vision tasks such as image classification, object …