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

Transformers have been recently adapted for large scale image classification, achieving
high scores shaking up the long supremacy of convolutional neural networks. However the …

Vision transformers have been successfully applied to image recognition tasks due to their
ability to capture long-range dependencies within an image. However, there are still gaps in …

Built on top of self-attention mechanisms, vision transformers have demonstrated
remarkable performance on a variety of vision tasks recently. While achieving excellent …

The recently developed vision transformer (ViT) has achieved promising results on image
classification compared to convolutional neural networks. Inspired by this, in this paper, we …

This paper provides a strong baseline for vision transformers on the ImageNet classification
task. While recent vision transformers have demonstrated promising results in ImageNet …

Motivated by the success of Transformers in natural language processing (NLP) tasks, there
exist some attempts (eg, ViT and DeiT) to apply Transformers to the vision domain. However …

While the Transformer architecture has become the de-facto standard for natural language
processing tasks, its applications to computer vision remain limited. In vision, attention is …

Vision Transformers (ViTs) have shown competitive accuracy in image classification tasks
compared with CNNs. Yet, they generally require much more data for model pre-training …

Transformers, which are popular for language modeling, have been explored for solving
vision tasks recently, eg, the Vision Transformer (ViT) for image classification. The ViT model …