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.

翻译：伪装目标检测（COD）是计算机视觉领域的一项具有挑战性的任务，原因是伪装目标与其周围环境高度相似。现有COD方法主要采用语义分割，但该方法常因过度自信而产生错误预测。本文提出一种新范式，将COD视为利用扩散模型的条件掩码生成任务。我们的方法名为CamoDiffusion，通过扩散模型的去噪过程迭代减少掩码中的噪声。由于扩散过程的随机采样特性，模型能从掩码分布中采样多种可能的预测结果，从而避免过度自信的点估计问题。此外，我们开发了专门的学习策略，包括一种用于生成稳健预测的创新集成方法，以及针对COD任务设计的用于高效训练的定制前向扩散方法。在三个COD数据集上的大量实验表明，我们的模型相比现有最先进方法具有更优性能，尤其是在最具挑战性的COD10K数据集上，我们的方法在MAE指标上达到了0.019。