Since its inception in 2015, the YOLO (You Only Look Once) variant of object detectors has
rapidly grown, with the latest release of YOLO-v8 in January 2023. YOLO variants are …

Deep supervised learning algorithms typically require a large volume of labeled data to
achieve satisfactory performance. However, the process of collecting and labeling such data …

In this work, we introduce Dual Attention Vision Transformers (DaViT), a simple yet effective
vision transformer architecture that is able to capture global context while maintaining …

Very recently, a variety of vision transformer architectures for dense prediction tasks have
been proposed and they show that the design of spatial attention is critical to their success in …

We study how to introduce locality mechanisms into vision transformers. The transformer
network originates from machine translation and is particularly good at modelling long-range …

Astounding results from Transformer models on natural language tasks have intrigued the
vision community to study their application to computer vision problems. Among their salient …

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 …

The recent explosive interest on transformers has suggested their potential to become
powerful``universal" models for computer vision tasks, such as classification, detection, and …

Recently, pure transformer-based models have shown great potentials for vision tasks such
as image classification and detection. However, the design of transformer networks is …

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 …