Recurrent neural networks (RNNs) have been widely adopted in research areas concerned
with sequential data, such as text, audio, and video. However, RNNs consisting of sigma …

Identification of leaf disorder plays an important role in the economic prosperity of any
country. Many parts of a plant can be infected by a virus, fungal, bacteria, and other …

Few-shot semantic segmentation (FSS) has great potential for medical imaging applications.
Most of the existing FSS techniques require abundant annotated semantic classes for …

This article describes the design, implementation, and results of the latest installment of the
dermoscopic image analysis benchmark challenge. The goal is to support research and …

Recent advances in deep learning, especially deep convolutional neural networks (CNNs),
have led to significant improvement over previous semantic segmentation systems. Here we …

Vision transformer networks have shown superiority in many computer vision tasks. In this
paper, we take a step further by proposing a novel generative vision transformer with latent …

In this article, we describe the design and implementation of a publicly accessible
dermatology image analysis benchmark challenge. The goal of the challenge is to sup-port …

Salient object detection has recently witnessed substantial progress due to powerful
features extracted using deep convolutional neural networks (CNNs). However, existing …

Superpixels group perceptually similar pixels to create visually meaningful entities while
heavily reducing the number of primitives for subsequent processing steps. As of these …

Most existing bottom-up methods measure the foreground saliency of a pixel or region
based on its contrast within a local context or the entire image, whereas a few methods focus …