Concealed scene understanding (CSU) is a hot computer vision topic aiming to perceive
objects exhibiting camouflage. The current boom in terms of techniques and applications …

Vision transformers have recently shown strong global context modeling capabilities in
camouflaged object detection. However, they suffer from two major limitations: less effective …

Accurate segmentation of polyps from colonoscopy images plays a critical role in the
diagnosis and cure of colorectal cancer. Although effectiveness has been achieved in the …

SAM is a segmentation model recently released by Meta AI Research and has been gaining
attention quickly due to its impressive performance in generic object segmentation …

Camouflaged object detection (COD) aims to detect/segment camouflaged objects
embedded in the environment, which has attracted increasing attention over the past …

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 …

Camouflaged object detection (COD) aims to search and identify disguised objects that are
hidden in their surrounding environment, thereby deceiving the human visual system. As an …

The goal of Camouflaged object detection (COD) is to detect objects that are visually
embedded in their surroundings. Existing COD methods only focus on detecting …

The aim of camouflaged object detection (COD) is to find objects that are hidden in their
surrounding environment. Due to the factors like low illumination, occlusion, small size and …

Preys in the wild evolve to be camouflaged to avoid being recognized by predators. In this
way, camouflage acts as a key defence mechanism across species that is critical to survival …