In agricultural image analysis, optimal model performance is keenly pursued for better
fulfilling visual recognition tasks (eg, image classification, segmentation, object detection …

Transformers have recently gained significant attention in the computer vision community.
However, the lack of scalability of self-attention mechanisms with respect to image size has …

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 …

In this paper, we present Uformer, an effective and efficient Transformer-based architecture
for image restoration, in which we build a hierarchical encoder-decoder network using the …

Despite the tantalizing success in a broad of vision tasks, transformers have not yet
demonstrated on-par ability as ConvNets in high-resolution image generative modeling. In …

Generative modeling and representation learning are two key tasks in computer vision.
However, these models are typically trained independently, which ignores the potential for …

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

Diffusion models with their powerful expressivity and high sample quality have achieved
State-Of-The-Art (SOTA) performance in the generative domain. The pioneering Vision …

Real-world imagery does not always exhibit good visibility and clean content, but often
suffers from various kinds of degradations (eg, noise, blur, rain drops, fog, color distortion …

Transformers have made remarkable progress towards modeling long-range dependencies
within the medical image analysis domain. However, current transformer-based models …