CODdiff: Prior leading diffusion model for Camouflage Object Detection

The Camouflage Object Detection (COD) task poses a significant challenge in distinguishing subtle visual distinctions between camouflaged objects and their surroundings. Existing approaches treat COD as a pixel classification task, initiated from the original input and proceeding to the camouflaged objects’ masks through a coarse-to-fine process in multiple cascading stages. Nevertheless, accomplishing the intricate COD task within just a few stages often proves arduous, and the interoperability of each stage is hard to determine. Drawing inspiration from the success of the Diffusion Probabilistic Model (DPM) in image generation, this paper presents an innovative perspective that regards the COD task as a conditional denoising process. This process initiates with Gaussian noise and utilizes the original image as a condition to attain the final outcomes. To address this idea, we propose CODdiff, a distinctive methodology grounded in the diffusion model. CODdiff comprises two autonomous branches dedicated to acquiring diffusion embedding and semantic prior embedding, respectively. These two forms of embedding information are subsequently amalgamated through a mixing strategy at each reverse step. This amalgamation could effectively suppresses background noise while revealing the mask of camouflaged objects. Extensive experiments conducted across 3 widely utilized datasets attest to CODdiff's superior performance compared to 17 state-of-the-art approaches, illustrating the generalizability of the proposed model. In summary, CODdiff introduces a novel approach to dissect the COD task's complexity, transforming it into a repetitive Gaussian noise prediction task empowered by advanced computer vision techniques.