Instance-specific and Model-adaptive Supervision for Semi-supervised Semantic Segmentation

Understand

Recently, semi-supervised semantic segmentation has achieved promising performance with a small fraction of labeled data.

• However, most existing studies treat all unlabeled data equally and barely consider the differences and training difficulties among unlabeled instances.
• Differentiating unlabeled instances can promote instance-specific supervision to adapt to the model's evolution dynamically.
• In this paper, we emphasize the cruciality of instance differences and propose an instance-specific and model-adaptive supervision for semi-supervised semantic segmentation, named iMAS.