Vision transformers have gained significant attention and achieved state-of-the-art
performance in various computer vision tasks, including image classification, instance …

Abstract The advent of Vision Transformers (ViTs) marks a substantial paradigm shift in the
realm of computer vision. ViTs capture the global information of images through self …

Vision transformers (ViTs) are usually considered to be less light-weight than convolutional
neural networks (CNNs) due to the lack of inductive bias. Recent works thus resort to …

Vision transformers have shown excellent performance in computer vision tasks. As the
computation cost of their self-attention mechanism is expensive, recent works tried to …

Vision transformers have been applied successfully for image recognition tasks. There have
been either multi-headed self-attention based (ViT\cite {dosovitskiy2020image}, DeIT,\cite …

Mixup-based augmentation has been found to be effective for generalizing models during
training, especially for Vision Transformers (ViTs) since they can easily overfit. However …

The recently proposed Visual image Transformers (ViT) with pure attention have achieved
promising performance on image recognition tasks, such as image classification. However …

Vision Transformers (ViTs) have emerged as powerful models in the field of computer vision,
delivering superior performance across various vision tasks. However, the high …

Vision transformer has demonstrated promising performance on challenging computer
vision tasks. However, directly training the vision transformers may yield unstable and sub …

Transformers have become one of the dominant architectures in deep learning, particularly
as a powerful alternative to convolutional neural networks (CNNs) in computer vision …