Segment objects with limited labels by collaboration of output and representation spaces with CSS
Segment objects with limited labels by collaboration of output and representation spaces with CSS
Space Engage: Collaborative Space Supervision for Contrastive-based Semi-Supervised Semantic Segmentation
arXiv paper abstract https://arxiv.org/abs/2307.09755
arXiv PDF paper https://arxiv.org/pdf/2307.09755.pdf
Semi-Supervised Semantic Segmentation (S4) aims to train a segmentation model with limited labeled images and a substantial volume of unlabeled images.
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.
However, previous contrastive-based S4 methods merely rely on the supervision from the model's output (logits) in logit space during unlabeled training.
In contrast, ... utilize the outputs in both logit space and representation space to obtain supervision in a collaborative way.
The supervision from two spaces plays two roles: 1) reduces the risk of over-fitting to incorrect semantic information in logits with the help of representations; 2) enhances the knowledge exchange between the two spaces.
... demonstrate the competitive performance of ... method compared with state-of-the-art methods.
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