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 …

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

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 …

According to Darwinian evolutionary theory, numerous species in the wild have developed
remarkable adaptive mechanisms, involving pattern rearrangement and environmental …

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 …

In recent years, salient object detection (SOD) has made great progress in natural scene
images (NSIs), but SOD of strip steel defect images (SDIs) in industrial scenes is still 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 a broad sense, camouflaged objects generally refer to objects that have a high degree of
similarity to the background. Therefore, camouflaged object segmentation (COS) is more …

The high intrinsic similarity between camouflaged objects and the background makes
camouflaged object detection (COD) more challenging than traditional salient object …