BiDiCOS: Camouflaged object segmentation via bilateral diffusion model

Abstract

The task of camouflaged object segmentation (COS) is to extract objects with camouflage characteristics that are difficult to detect in an image. The current mainstream COS method mainly relies on the general object segmentation framework using the encoder-decoder structure to predict the objects. However, the current COS framework suffers from over-reliance on the labeled ground truth and the output results. To this end, we propose a bilateral diffusion model for camouflaged object segmentation (BiDiCOS). The main innovation of BiDiCOS is that we design a new bilateral diffusion paradigm, transforming the segmentation and depth estimation tasks into denoising tasks. At the same time, the depth estimation branch can provide semantic information to the segmentation branch. Specifically, we design the adaptive feature fusion module for two branches to provide the required features adaptively. We design the bilateral feature fusion module to fuse the depth information with the mask information to reduce the semantic and logical errors during segmentation. Finally, we develop the specialized training strategy and inference strategy for BiDiCOS, which provide a solid foundation for realizing rational training and accurate prediction. Comprehensive experiments on four challenging COS datasets attest to the superior performance of BiDiCOS compared to the existing 20 COS methods. Our BiDiCOS achieves state-of-the-art with different sizes of backbones, where the MAE is reduced by 4.3 % on average. We have also performed comparative experiments on depth estimation. The experiments demonstrate that BiDiCOS can achieve a win–win situation for both tasks. The code will be available at https://github.com/jiangxinhao2020/BiDiCOS.