Method Details

Details for method 'MRFM'

name	MRFM
challenge	pixel-level semantic labeling
details	Semantic segmentation is one of the key tasks in comput- er 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 is- sues: 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 pixel- s close to or at the boundaries of object/stuff are hard to classify due to the intrinsic weakness of convolutional net- works. 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 Mul- ti Receptive Field Network achieves new state-of-the-art re- sults 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.
publication	Multi Receptive Field Network for Semantic Segmentation Jianlong Yuan, Zelu Deng, Shu Wang, Zhenbo Luo WACV2020 https://github.com/jianlong-yuan/MRFM
project page / code
used Cityscapes data	fine annotations, coarse annotations
used external data
runtime	n/a
subsampling	no
submission date	April, 2019
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