Abstract A Vision Transformer (ViT) is a simple neural architecture amenable to serve
several computer vision tasks. It has limited built-in architectural priors, in contrast to more …

Recently, neural networks purely based on attention were shown to address image
understanding tasks such as image classification. These high-performing vision …

Most animal species on Earth are insects, and recent reports suggest that their abundance is
in drastic decline. Although these reports come from a wide range of insect taxa and regions …

After their initial success in natural language processing, transformer architectures have
rapidly gained traction in computer vision, providing state-of-the-art results for tasks such as …

Despite the large volume of face recognition datasets, there is a significant portion of
subjects, of which the samples are insufficient and thus under-represented. Ignoring such …

Real-world data is often unbalanced and long-tailed, but deep models struggle to recognize
rare classes in the presence of frequent classes. To address unbalanced data, most studies …

As the deep learning revolution marches on, self-supervised learning has garnered
increasing attention in recent years thanks to its remarkable representation learning ability …

In this study, we automate tree species classification and mapping using field-based training
data, high spatial resolution airborne hyperspectral imagery, and a convolutional neural …

Action recognition has seen a dramatic performance improvement in the last few years. Most
of the current state-of-the-art literature either aims at improving performance through …

As a key component of wind turbines (WTs), the blade conditions are related to the WT
normal operation and the WT blade inspection is a significant task. Most studies of WT blade …