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 …

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 …

Camouflaged object detection (COD) aims to address the tough issue of identifying
camouflaged objects visually blended into the surrounding backgrounds. COD is a …

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 …

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 …

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 …

How to identify and segment camouflaged objects from the background is challenging.
Inspired by the multi-head self-attention in Transformers, we present a simple masked …

We present the first comprehensive video polyp segmentation (VPS) study in the deep
learning era. Over the years, developments in VPS are not moving forward with ease due to …

Camouflaged object detection (COD) aims to detect out-of-attention regions in an image.
Current binary segmentation solutions fail to tackle COD easily, since COD is more …