CE-OST: Contour Emphasis for One-Stage Transformer-based Camouflage Instance Segmentation

Thanh-Danh Nguyen, Duc-Tuan Luu, Vinh-Tiep Nguyen, Thanh Duc Ngo

Abstract: Understanding camouflage images at instance level is such a challenging task in computer vision. Since the camouflage instances have their colors and textures similar to the background, the key to distinguish them in the images should rely on their contours. The contours seperate the instance from the background, thus recognizing these contours should break their camouflage mechanism. To this end, we address the problem of camouflage instance segmentation via the Contour Emphasis approach. We improve the ability of the segmentation models by enhancing the contours of the camouflaged instances. We propose the CE-OST framework which employs the well-known architecture of Transformer-based models in a one-stage manner to boost the performance of camouflaged instance segmentation. The extensive experiments prove our contributions over the state-of-the-art baselines on different benchmarks, i.e. CAMO++, COD10K and NC4K.