Multi Receptive Field Network for Semantic Segmentation
This paper offers strong specific gains for researchers and practitioners working on semantic segmentation by improving performance on challenging aspects of the task.
This paper addresses challenges in semantic segmentation, specifically diverse object sizes and difficult boundary pixels. The proposed Multi Receptive Field Network achieved state-of-the-art results, with a mean IoU of 83.0 on Cityscapes and 88.4 on Pascal VOC2012.
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.