  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.
