Aggregating Region Context Information for Semantic Segmentation

Recently, lots of works try to capture contextual information to benefit semantic segmentation problems. However, most approaches adopt the uniform method to obtain context information, which means each pixel gets its context from the same region. We argue that for each pixel, contextual information aggregated from the region it belongs to can benefit the dense prediction, while those from other irrelevant regions possibly mislead the prediction. In this work, we propose a Region Context Module (RCM) that aggregates context for each pixel only from its object region. Furthermore, we design a Region Context Network (RCNet) embedded in the ASPP Module and Region Context Module. We conduct experiments on three datasets: Cityscapes, Vaihingen and Potsdam datasets. Extensive quantitative and qualitative evaluations demonstrate our model achieves favorable performance against state-of-the-art approaches.