A long time ago in the machine learning literature, the idea of incorporating a mechanism
inspired by the human visual system into neural networks was introduced. This idea is …

The rapid development of deep learning has made a great progress in image segmentation,
one of the fundamental tasks of computer vision. However, the current segmentation …

Image dehazing is a representative low-level vision task that estimates latent haze-free
images from hazy images. In recent years, convolutional neural network-based methods …

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 …

As a de facto solution, the vanilla Vision Transformers (ViTs) are encouraged to model long-
range dependencies between arbitrary image patches while the global attended receptive …

Dense computer vision tasks such as object detection and segmentation require effective
multi-scale feature representation for detecting or classifying objects or regions with varying …

While some studies have proven that Swin Transformer (Swin) with window self-attention
(WSA) is suitable for single image super-resolution (SR), the plain WSA ignores the broad …

Abstract Vision Transformers (ViTs) have emerged with superior performance on computer
vision tasks compared to convolutional neural network (CNN)-based models. However, ViTs …

Abstract Recently, Vision Transformer (ViT) has continuously established new milestones in
the computer vision field, while the high computation and memory cost makes its …

Recently, Transformer-based image restoration networks have achieved promising
improvements over convolutional neural networks due to parameter-independent global …