# Multi Receptive Field Network for Semantic Segmentation Source: [Multi Receptive Field Network for Semantic Segmentation]() Collection index: [llms.txt]() Data updated: 2026-01-04T03:23:11.542Z Authors: Jianlong Yuan, Zelu Deng, Shu Wang, Zhenbo Luo Published: 2020-11-17T11:52:23.000Z ## Abstract Semantic segmentation is one of the key tasks in computer vision, which is to assign a category label to each pixel in an image. Despite significant progress achieved recently, most existing methods still suffer from two challenging issues: 1) the size of objects and stuff in an image can be very diverse, demanding for incorporating multi-scale features into the fully convolutional networks (FCNs); 2) the pixels close to or at the boundaries of object/stuff are hard to classify due to the intrinsic weakness of convolutional networks. To address the first issue, we propose a new Multi-Receptive Field Module (MRFM), explicitly taking multi-scale features into account. For the second issue, we design an edge-aware loss which is effective in distinguishing the boundaries of object/stuff. With these two designs, our Multi Receptive Field Network achieves new state-of-the-art results on two widely-used semantic segmentation benchmark datasets. Specifically, we achieve a mean IoU of 83.0 on the Cityscapes dataset and 88.4 mean IoU on the Pascal VOC2012 dataset. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Semantic Segmentation on PASCAL VOC 2012 (test)](): Semantic segmentation; PASCAL VOC 2012 (test); mIoU: 88.4. - [Semantic Segmentation on Cityscapes (test)](): Semantic segmentation; Cityscapes (test); mIoU: 83. ## Original sources and implementation [Original paper]() [PDF]()