SEMI-SUPERVISED SEMANTIC SEGMENTATION WITH STRUCTURED OUTPUT SPACE ADAPTION

Semi-supervised semantic segmentation methods rely on dense pixel-level classification with limited data and can thus be developed to adapt source ground truth labels to a target domain. In this paper, we creatively propose a method for semi-supervised semantic segmentation. The key innovation is our adversarial learning method for space adaptation in context, which can be regarded as a structured output that contains spatial similarities between unlabeled data and labeled data. Furthermore, we introduce two learning strategies, one that can selectively capture intra-category and inter-category context dependencies, resulting in robust feature representations. While the other explicitly concatenates the shape information of objects as a separate processing branch to produce sharper predicted boundaries of objects. Experimental results on two well-known benchmark datasets show that our method achieves better performance compared to the previous competitive models.