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 …

Recent camouflaged object detection (COD) attempts to segment objects visually blended
into their surroundings, which is extremely complex and difficult in real-world scenarios …

Abstract The Segment Anything Model (SAM) a prompt-driven foundational model has
demonstrated remarkable performance in natural image segmentation. However its …

Deep learning for unsupervised image segmentation remains challenging due to the
absence of human labels. The common idea is to train a segmentation head, with the …

Camouflaged object detection (COD) aims to identify the objects that seamlessly blend into
the surrounding backgrounds. Due to the intrinsic similarity between the camouflaged …

The purpose of camouflaged object detection (COD) focuses on detecting objects that
seamlessly blend into their surroundings. Camouflaged objects pose a substantial …

Detecting building changes with multi-temporal remote sensing (RS) imagery at a very high
resolution can help us understand urbanization and human activities, making informed …

Video camouflaged object detection aims to identify objects that are visually concealed
within the surroundings in a video. Most of the existing methods fall into analyzing the …

The key that hinders the performance improvement of current camouflaged object detection
(COD) models is the lack of discriminability of features at fine granularity. We solve this …

Concealed object detection (COD) in cluttered scenes is significant for various image
processing applications. However, due to that concealed objects are always similar to their …