Bootstrapping Semantic Segmentation with Regional Contrast
- URL: http://arxiv.org/abs/2104.04465v2
- Date: Mon, 12 Apr 2021 00:53:22 GMT
- Title: Bootstrapping Semantic Segmentation with Regional Contrast
- Authors: Shikun Liu, Shuaifeng Zhi, Edward Johns, Andrew J. Davison
- Abstract summary: ReCo is a contrastive learning framework designed at a regional level to assist learning in semantic segmentation.
We achieve 50% mIoU in the CityScapes dataset, whilst requiring only 20 labelled images, improving by 10% relative to the previous state-of-the-art.
- Score: 27.494579304204226
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We present ReCo, a contrastive learning framework designed at a regional
level to assist learning in semantic segmentation. ReCo performs
semi-supervised or supervised 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 in both semi-supervised and supervised
semantic segmentation methods, achieving smoother segmentation boundaries and
faster convergence. The strongest effect is in semi-supervised learning with
very few labels. With ReCo, we achieve 50% mIoU in the CityScapes dataset,
whilst requiring only 20 labelled images, improving by 10% relative to the
previous state-of-the-art. Code is available at
https://github.com/lorenmt/reco.
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