Camouflaged Object Segmentation

Camouflaged Object Segmentation (COS) was introduced by Le et al. in 2017, aiming to identify objects that are highly similar in texture to their surrounding environment. This task overcomes the challenge of high intrinsic similarity in traditional object segmentation by distinguishing subtle differences between the target and the background, making it valuable for computer vision applications.