Graph Neural Networks (GNNs) have gained momentum in graph representation learning
and boosted the state of the art in a variety of areas, such as data mining (eg, social network …

This paper presents a new vision Transformer, called Swin Transformer, that capably serves
as a general-purpose backbone for computer vision. Challenges in adapting Transformer …

Transformer, first applied to the field of natural language processing, is a type of deep neural
network mainly based on the self-attention mechanism. Thanks to its strong representation …

Abstract Pretraining Vision Transformers (ViTs) has achieved great success in visual
recognition. A following scenario is to adapt a ViT to various image and video recognition …

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 …

Abstract Within Convolutional Neural Network (CNN), the convolution operations are good
at extracting local features but experience difficulty to capture global representations. Within …

The complex nature of combining localization and classification in object detection has
resulted in the flourished development of methods. Previous works tried to improve the …

Recently, vision-language pre-training shows great potential in open-vocabulary object
detection, where detectors trained on base classes are devised for detecting new classes …

DETR is a recently proposed Transformer-based method which views object detection as a
set prediction problem and achieves state-of-the-art performance but demands extra-long …

Transformer, first applied to the field of natural language processing, is a type of deep neural
network mainly based on the self-attention mechanism. Thanks to its strong representation …