MBNet: A Multi-Resolution Branch Network for Semantic Segmentation Of Ultra-High Resolution Images

Abstract

Semantic segmentation of ultra-high resolution images is more challenging than ordinary images since high-resolution images need to be cropped into patches in training due to GPU memory limitation. To solve this problem, we design a multibranch structure to deal with multi-resolution inputs, called Multi-resolution Branch Network (MBNet). MBNet takes patches of various instead of only one resolution as inputs, so it can make the extracted features pure and different from each other so as to cover the complex scenes with tremendous variation. Moreover, to make full use of the multi-branch structure, we design a zoom module. Zoom module abandons the previous L2-norm before feature concatenation but combines different features according to the learned attention, which fully releases the advantages of multi-resolution. Results on two benchmark datasets show that our method improves significantly over the previous state-of-the-art methods.