Multi Receptive Field Network for Semantic Segmentation

• URL: http://arxiv.org/abs/2011.08577v2
• Date: Wed, 7 Sep 2022 14:01:42 GMT
• Title: Multi Receptive Field Network for Semantic Segmentation
• Authors: Jianlong Yuan, Zelu Deng, Shu Wang, Zhenbo Luo
• Abstract summary: We propose a new Multi-Receptive Field Module (MRFM) for semantic segmentation. We also design an edge-aware loss which is effective in distinguishing the boundaries of object/stuff. Specifically, we achieve a mean IoU of 83.0 on the Cityscapes dataset and 88.4 mean IoU on the Pascal VOC2012 dataset.
• Score: 8.06045579589765
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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.

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