The" Roaring 20s" of visual recognition began with the introduction of Vision Transformers
(ViTs), which quickly superseded ConvNets as the state-of-the-art image classification …

Vision Transformers have been the most popular network architecture in visual recognition
recently due to the strong ability of encode global information. However, its high …

Driven by improved architectures and better representation learning frameworks, the field of
visual recognition has enjoyed rapid modernization and performance boost in the early …

The past year has witnessed the rapid development of applying the Transformer module to
vision problems. While some researchers have demonstrated that Transformer-based …

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 attracted much attention from computer vision researchers as they
are not restricted to the spatial inductive bias of ConvNets. However, although Transformer …

Abstract Deep Convolutional Neural Networks (CNNs) have long been the architecture of
choice for computer vision tasks. Recently, Transformer-based architectures like Vision …

Recently, Vision Transformers (ViTs) have been broadly explored in visual recognition. With
low efficiency in encoding fine-level features, the performance of ViTs is still inferior to 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 …

Transformers have offered a new methodology of designing neural networks for visual
recognition. Compared to convolutional networks, Transformers enjoy the ability of referring …