  The advance of generative models for images has inspired various training
techniques for image recognition utilizing synthetic images. In semantic
segmentation, one promising approach is extracting pseudo-masks from attention
maps in text-to-image diffusion models, which enables
real-image-and-annotation-free training. However, the pioneering training
method using the diffusion-synthetic images and pseudo-masks, i.e., DiffuMask
has limitations in terms of mask quality, scalability, and ranges of applicable
domains. To overcome these limitations, this work introduces three techniques
for diffusion-synthetic semantic segmentation training. First,
reliability-aware robust training, originally used in weakly supervised
learning, helps segmentation with insufficient synthetic mask quality. %Second,
large-scale pretraining of whole segmentation models, not only backbones, on
synthetic ImageNet-1k-class images with pixel-labels benefits downstream
segmentation tasks. Second, we introduce prompt augmentation, data augmentation
to the prompt text set to scale up and diversify training images with a limited
text resources. Finally, LoRA-based adaptation of Stable Diffusion enables the
transfer to a distant domain, e.g., auto-driving images. Experiments in PASCAL
VOC, ImageNet-S, and Cityscapes show that our method effectively closes gap
between real and synthetic training in semantic segmentation.
