SegRCDB: Semantic Segmentation via Formula-Driven Supervised Learning
Risa Shinoda Ryo Hayamizu Kodai Nakashima Nakamasa Inoue Rio Yokota Hirokatsu Kataoka
Pre-training is a strong strategy for enhancing visual models to efficiently
train them with a limited number of labeled images. In semantic segmentation,
creating annotation masks requires an intensive amount of labor and time, and
therefore, a large-scale pre-training dataset with semantic labels is quite
difficult to construct. Moreover, what matters in semantic segmentation
pre-training has not been fully investigated. In this paper, we propose the
Segmentation Radial Contour DataBase (SegRCDB), which for the first time
applies formula-driven supervised learning for semantic segmentation. SegRCDB
enables pre-training for semantic segmentation without real images or any
manual semantic labels. SegRCDB is based on insights about what is important in
pre-training for semantic segmentation and allows efficient pre-training.
Pre-training with SegRCDB achieved higher mIoU than the pre-training with
COCO-Stuff for fine-tuning on ADE-20k and Cityscapes with the same number of
training images. SegRCDB has a high potential to contribute to semantic
segmentation pre-training and investigation by enabling the creation of large
datasets without manual annotation. The SegRCDB dataset will be released under
a license that allows research and commercial use. Code is available at:
https://github.com/dahlian00/SegRCDB