Bootstrapping Semantic Segmentation with Regional Contrast
Abstract
We present ReCo, a contrastive learning framework designed at a regional level to assist learning in semantic segmentation. ReCo performs pixel-level contrastive learning on a sparse set of hard negative pixels, with minimal additional memory footprint. ReCo is easy to implement, being built on top of off-the-shelf segmentation networks, and consistently improves performance, achieving more accurate segmentation boundaries and faster convergence. The strongest effect is in semi-supervised learning with very few labels. With ReCo, we achieve high quality semantic segmentation model, requiring only 5 examples of each semantic class.
Cite
Text
Liu et al. "Bootstrapping Semantic Segmentation with Regional Contrast." International Conference on Learning Representations, 2022.Markdown
[Liu et al. "Bootstrapping Semantic Segmentation with Regional Contrast." International Conference on Learning Representations, 2022.](https://mlanthology.org/iclr/2022/liu2022iclr-bootstrapping/)BibTeX
@inproceedings{liu2022iclr-bootstrapping,
title = {{Bootstrapping Semantic Segmentation with Regional Contrast}},
author = {Liu, Shikun and Zhi, Shuaifeng and Johns, Edward and Davison, Andrew},
booktitle = {International Conference on Learning Representations},
year = {2022},
url = {https://mlanthology.org/iclr/2022/liu2022iclr-bootstrapping/}
}