MMS:Multi-Source Mutual Supervision Semantic Segmentation

How to use multi-source data for semantic segmentation is a hot topic. In this article, a new multi-source image data semantic segmentation based on multi-source mutual supervision (MMS) has been proposed. First, multi-source data from the same region are trained separately using identical segmentation networks for initialization; then, MMS performs mutual supervision training on these initialized networks, thus adaptively cooperating differences in information distribution between multiple sources of image data, and combines real label constraints output consistency across different networks. Experimental results demonstrate that the proposed MMS strategy can effectively coordinate information between multi-source data, improve segmentation accuracy, and is effective in different semantic segmentation networks and different types of data sets.