Weakly-supervised semantic segmentation (WSSS), which aims to train
segmentation models solely using image-level labels, has achieved significant
attention. Existing methods primarily focus on generating high-quality pseudo
labels using available images and their image-level labels. However, the
quality of pseudo labels degrades significantly when the size of available
dataset is limited. Thus, in this paper, we tackle this problem from a
different view by introducing a novel approach called Image Augmentation with
Controlled Diffusion (IACD). This framework effectively augments existing
labeled datasets by generating diverse images through controlled diffusion,
where the available images and image-level labels are served as the controlling
information. Moreover, we also propose a high-quality image selection strategy
to mitigate the potential noise introduced by the randomness of diffusion
models. In the experiments, our proposed IACD approach clearly surpasses
existing state-of-the-art methods. This effect is more obvious when the amount
of available data is small, demonstrating the effectiveness of our method.

通过引入一种名为受控扩散的新方法，该研究论文提出了一种解决图像级标签训练弱监督语义分割模型的问题的框架，它通过可控的扩散生成多样化的图像来增强现有的标记数据集，并提出了一种高质量的图像选择策略来减少扩散模型的随机性引入的潜在噪声。实验证明了该方法在有限的可用数据量下优于现有的最先进方法。