CamoDiffusion: Camouflaged Object Detection via Conditional Diffusion
  Models
Zhongxi Chen Ke Sun Xianming Lin Rongrong Ji
Camouflaged Object Detection (COD) is a challenging task in computer vision
due to the high similarity between camouflaged objects and their surroundings.
Existing COD methods primarily employ semantic segmentation, which suffers from
overconfident incorrect predictions. In this paper, we propose a new paradigm
that treats COD as a conditional mask-generation task leveraging diffusion
models. Our method, dubbed CamoDiffusion, employs the denoising process of
diffusion models to iteratively reduce the noise of the mask. Due to the
stochastic sampling process of diffusion, our model is capable of sampling
multiple possible predictions from the mask distribution, avoiding the problem
of overconfident point estimation. Moreover, we develop specialized learning
strategies that include an innovative ensemble approach for generating robust
predictions and tailored forward diffusion methods for efficient training,
specifically for the COD task. Extensive experiments on three COD datasets
attest the superior performance of our model compared to existing
state-of-the-art methods, particularly on the most challenging COD10K dataset,
where our approach achieves 0.019 in terms of MAE.