Following unprecedented success on the natural language tasks, Transformers have been
successfully applied to several computer vision problems, achieving state-of-the-art results …

Abstract Background and Objectives: Transformer is a model relying entirely on self-
attention which has a wide range of applications in the field of natural language processing …

Recent progress on Transformers and multi-layer perceptron (MLP) models provide new
network architectural designs for computer vision tasks. Although these models proved to be …

Diffusion model (DM) has achieved SOTA performance by modeling the image synthesis
process into a sequential application of a denoising network. However, different from image …

Transformer, one of the latest technological advances of deep learning, has gained
prevalence in natural language processing or computer vision. Since medical imaging bear …

Abstract Vision Transformers (ViTs), with the magnificent potential to unravel the information
contained within images, have evolved as one of the most contemporary and dominant …

In coded aperture snapshot spectral compressive imaging (CASSI) systems, hyperspectral
image (HSI) reconstruction methods are employed to recover the spatial-spectral signal from …

We propose a novel transformer model, capable of segmenting medical images of varying
modalities. Challenges posed by the fine-grained nature of medical image analysis mean …

By integrating certain optimization solvers with deep neural networks, deep unfolding
network (DUN) with good interpretability and high performance has attracted growing …

Transformers have demonstrated impressive expressiveness and transfer capability in
computer vision fields. Dense prediction is a fundamental problem in computer vision that is …