Weakly-supervised diffusion models (DM) 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 for pixel-level
labels. Leveraging the unguided forward process as a reference, we identify
suitable hyperparameters, i.e., noise scale and threshold, for each input
image. We aggregate anomaly maps from each step in the 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.

无需像素级标签，利用弱监督扩散模型进行异常分割，提升信号强度并优于最新弱监督方法。

AnoFPDM: 前向扩散模型用于脑部 MRI 的异常分割

AnoFPDM: Anomaly Segmentation with Forward Process of Diffusion Models  for Brain MRI

Text segmentation tasks have a very wide range of application values, such as
image editing, style transfer, watermark removal, etc.However, existing public
datasets are of poor quality of pixel-level labels that have been shown to be
notoriously costly to acquire, both in terms of money and time. At the same
time, when pretraining is performed on synthetic datasets, the data
distribution of the synthetic datasets is far from the data distribution in the
real scene. These all pose a huge challenge to the current pixel-level text
segmentation algorithms.To alleviate the above problems, we propose a
self-supervised scene text segmentation algorithm with layered decoupling of
representations derived from the object-centric manner to segment images into
texts and background. In our method, we propose two novel designs which include
Region Query Module and Representation Consistency Constraints adapting to the
unique properties of text as complements to Auto Encoder, which improves the
network's sensitivity to texts.For this unique design, we treat the
polygon-level masks predicted by the text localization model as extra input
information, and neither utilize any pixel-level mask annotations for training
stage nor pretrain on synthetic datasets.Extensive experiments show the
effectiveness of the method proposed. On several public scene text datasets,
our method outperforms the state-of-the-art unsupervised segmentation
algorithms.

通过自我监督场景文本分割算法，基于对象为中心的分割方式将图像分割为文本和背景层，改进网络对文本的敏感性，以解决像素级标签和合成数据集的问题，取得了比现有算法更好的效果。