Dual context prior and refined prediction for semantic segmentation

Recently, the focus of semantic segmentation research has shifted to the aggregation of context prior and refined boundary. A typical network adopts context aggregation modules to extract rich semantic features. It also utilizes top-down connection and skips connections for refining boundary details. But it still remains disadvantage, an obvious fact is that the problem of false segmentation occurs as the object has very different textures. The fusion of weak semantic and low-level features leads to context prior degradation. To tackle the issue, we propose a simple yet effective network, which integrates dual context prior and spatial propagation-dubbed DSPNet. It extends two mainstreams of current segmentation researches: (1) Designing a dual context prior module, which pays attention to context prior again with a shortcut connection. (2) The network can inherently learn semantic aware affinity values for each pixel and refine the segmentation. We will present detailed comparisons, which perform on PASCAL VOC 2012 and Cityscapes. The result demonstrates the validation of our approach.