  Weakly supervised semantic segmentation (WSSS), aiming to train segmentation
models solely using image-level labels, has received significant attention.
Existing approaches mainly concentrate on creating high-quality pseudo labels
by utilizing existing images and their corresponding 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 GPT-Prompt Controlled
Diffusion (GPCD) for data augmentation. This approach enhances the current
labeled datasets by augmenting with a variety of images, achieved through
controlled diffusion guided by GPT prompts. In this process, the existing
images and image-level labels provide the necessary control information, where
GPT is employed to enrich the prompts, leading to the generation of diverse
backgrounds. Moreover, we integrate data source information as tokens into the
Vision Transformer (ViT) framework. These tokens are specifically designed to
improve the ability of downstream WSSS framework to recognize the origins of
augmented images. Our proposed GPCD 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.
