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

Although no specific domain knowledge is considered in the design, plain vision
transformers have shown excellent performance in visual recognition tasks. However, little …

In the pursuit of achieving ever-increasing accuracy, large and complex neural networks are
usually developed. Such models demand high computational resources and therefore …

Brain tumor semantic segmentation is a critical medical image processing work, which aids
clinicians in diagnosing patients and determining the extent of lesions. Convolutional neural …

Transformer has shown excellent performance in remote sensing field with long-range
modeling capabilities. Remote sensing video (RSV) moving object detection and tracking …

Abstract Convolutional Neural Networks (CNNs) have dominated computer vision for years,
due to its ability in capturing locality and translation invariance. Recently, many vision …

Abstract Although Vision transformers (ViTs) have recently dominated many vision tasks,
deploying ViT models on resource-limited devices remains a challenging problem. To …

Image restoration aims to reconstruct a high-quality image from its corrupted version, playing
essential roles in many scenarios. Recent years have witnessed a paradigm shift in image …

The quadratic computational complexity to the number of tokens limits the practical
applications of Vision Transformers (ViTs). Several works propose to prune redundant …

In this paper, we show the surprisingly good properties of plain vision transformers for body
pose estimation from various aspects, namely simplicity in model structure, scalability in …