FreeMask: Synthetic Images with Dense Annotations Make Stronger
  Segmentation Models
Lihe Yang Xiaogang Xu Bingyi Kang Yinghuan Shi Hengshuang Zhao
Semantic segmentation has witnessed tremendous progress due to the proposal
of various advanced network architectures. However, they are extremely hungry
for delicate annotations to train, and the acquisition is laborious and
unaffordable. Therefore, we present FreeMask in this work, which resorts to
synthetic images from generative models to ease the burden of both data
collection and annotation procedures. Concretely, we first synthesize abundant
training images conditioned on the semantic masks provided by realistic
datasets. This yields extra well-aligned image-mask training pairs for semantic
segmentation models. We surprisingly observe that, solely trained with
synthetic images, we already achieve comparable performance with real ones
(e.g., 48.3 vs. 48.5 mIoU on ADE20K, and 49.3 vs. 50.5 on COCO-Stuff). Then, we
investigate the role of synthetic images by joint training with real images, or
pre-training for real images. Meantime, we design a robust filtering principle
to suppress incorrectly synthesized regions. In addition, we propose to
inequally treat different semantic masks to prioritize those harder ones and
sample more corresponding synthetic images for them. As a result, either
jointly trained or pre-trained with our filtered and re-sampled synthesized
images, segmentation models can be greatly enhanced, e.g., from 48.7 to 52.0 on
ADE20K. Code is available at https://github.com/LiheYoung/FreeMask.