TACoS achieves over 96% of fully supervised segmentation performance on 2D material flakes using less than 0.6% annotated pixels via a unified framework of consistency learning, tree energy regularization, and asymmetric contrastive learning.
Bootstrapping Semantic Segmentation with Regional Contrast
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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 high-quality semantic segmentation models, requiring only 5 examples of each semantic class. Code is available at https://github.com/lorenmt/reco.
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TACoS: Weakly Supervised Learning of Two-Dimensional Materials from Scribble Annotations to Precise Segmentation
TACoS achieves over 96% of fully supervised segmentation performance on 2D material flakes using less than 0.6% annotated pixels via a unified framework of consistency learning, tree energy regularization, and asymmetric contrastive learning.