Transformers have dominated the field of natural language processing and have recently
made an impact in the area of computer vision. In the field of medical image analysis …

Camouflaged object detection (COD) is an emerging visual detection task, which aims to
locate and distinguish the disguised target in complex backgrounds by imitating the human …

The recently proposed camouflaged object detection (COD) attempts to segment objects that
are visually blended into their surroundings, which is extremely complex and difficult in real …

Although convolutional neural networks (CNNs) have achieved great success in computer
vision, this work investigates a simpler, convolution-free backbone network useful for many …

Most polyp segmentation methods use CNNs as their backbone, leading to two key issues
when exchanging information between the encoder and decoder: 1) taking into account the …

More than 90% of colorectal cancer is gradually transformed from colorectal polyps. In
clinical practice, precise polyp segmentation provides important information in the early …

Appearance and motion are two important sources of information in video object
segmentation (VOS). Previous methods mainly focus on using simplex solutions, lowering …

Camouflaged object detection (COD) aims to identify the objects that conceal themselves in
natural scenes. Accurate COD suffers from a number of challenges associated with low …

Abstract Camouflaged Object Detection (COD) aims to detect objects with similar patterns
(eg, texture, intensity, colour, etc) to their surroundings, and recently has attracted growing …

This paper introduces deep gradient network (DGNet), a novel deep framework that exploits
object gradient supervision for camouflaged object detection (COD). It decouples the task …