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Found 3 papers, 1 papers with code
PRCL: Probabilistic Representation Contrastive Learning for Semi-Supervised Semantic Segmentation
To address this issue, we propose a robust contrastive-based S4 framework, termed the Probabilistic Representation Contrastive Learning (PRCL) framework to enhance the robustness of the unsupervised training process.
Space Engage: Collaborative Space Supervision for Contrastive-based Semi-Supervised Semantic Segmentation
To improve the robustness of representations, powerful methods introduce a pixel-wise contrastive learning approach in latent space (i. e., representation space) that aggregates the representations to their prototypes in a fully supervised manner.
Boosting Semi-Supervised Semantic Segmentation with Probabilistic Representations
In prevalent pixel-wise contrastive learning solutions, the model maps pixels to deterministic representations and regularizes them in the latent space.
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