Weakly-supervised diffusion models (DMs) in anomaly segmentation, leveraging image-level labels, have attracted significant attention for their superior performance compared to unsupervised methods. It eliminates the need for pixel-level labels in training, offering a more cost-effective alternative to supervised methods. However, existing methods are not fully weakly-supervised because they heavily rely on costly pixel-level labels for hyperparameter tuning in inference. To tackle this challenge, we introduce Anomaly Segmentation with Forward Process of Diffusion Models (AnoFPDM), a fully weakly-supervised framework that operates without the need of pixel-level labels. Leveraging the unguided forward process as a reference for the guided forward process, we select hyperparameters such as the noise scale, the threshold for segmentation and the guidance strength. We aggregate anomaly maps from guided forward process, enhancing the signal strength of anomalous regions. Remarkably, our proposed method outperforms recent state-of-the-art weakly-supervised approaches, even without utilizing pixel-level labels.

翻译：在异常分割任务中，利用图像级标签的弱监督扩散模型因其性能显著优于无监督方法而受到广泛关注。该方法无需在训练中使用像素级标签，为监督方法提供了一种更具成本效益的替代方案。然而，现有方法并非完全弱监督，因为它们在推理阶段严重依赖昂贵的像素级标签进行超参数调优。为应对这一挑战，我们提出了基于扩散模型前向过程的异常分割方法（AnoFPDM），这是一个完全弱监督的框架，无需依赖像素级标签即可运行。我们利用无引导的前向过程作为有引导前向过程的参考，从而选择噪声尺度、分割阈值和引导强度等超参数。我们聚合来自有引导前向过程的异常图，以增强异常区域的信号强度。值得注意的是，即使未使用像素级标签，我们提出的方法也优于近期最先进的弱监督方法。