Over the past decade, domain adaptation has become a widely studied branch of transfer
learning which aims to improve performance on target domains by leveraging knowledge …

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 …

Salient object detection (SOD) and camouflaged object detection (COD) are related yet
distinct binary map** tasks. These tasks involve multiple modalities sharing …

Abstract Concealed Object Detection (COD) aims to identify objects visually embedded in
their background. Existing COD datasets and methods predominantly focus on animals or …

Camouflaged Object Detection (COD) is a challenging task in computer vision due to the
high similarity between camouflaged objects and their surroundings. Existing COD methods …

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 …

Abstract Camouflage Object Detection (COD) involves the challenge of isolating a target
object from a visually similar background, presenting a formidable challenge for learning …

In recent years, excellent RGB-D salient object detection performance has been achieved.
However, existing detection methods generally require a large number of model parameters …

In this article, we provide a comprehensive study of a new task called collaborative
camouflaged object detection (CoCOD), which aims to simultaneously detect camouflaged …

We consider the problem of referring camouflaged object detection (Ref-COD), a new task
that aims to segment specified camouflaged objects based on a small set of referring images …