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arXiv · 1707.06426

Semantic Segmentation with Reverse Attention

Qin Huang·Chunyang Xia·Chihao Wu·Siyang Li·Ye Wang·Yuhang Song·C. -C. Jay Kuo
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Abstract

Recent development in fully convolutional neural network enables efficient end-to-end learning of semantic segmentation. Traditionally, the convolutional classifiers are taught to learn the representative semantic features of labeled semantic objects. In this work, we propose a reverse attention network (RAN) architecture that trains the network to capture the opposite concept (i.e., what are not associated with a target class) as well. The RAN is a three-branch network that performs the direct, reverse and reverse-attention learning processes simultaneously. Extensive experiments are conducted to show the effectiveness of the RAN in semantic segmentation. Being built upon the DeepLabv2-LargeFOV, the RAN achieves the state-of-the-art mIoU score (48.1%) for the challenging PASCAL-Context dataset. Significant performance improvements are also observed for the PASCAL-VOC, Person-Part, NYUDv2 and ADE20K datasets.

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Qin Huang, Chunyang Xia, Chihao Wu, Siyang Li, Ye Wang, Yuhang Song, C. -C. Jay Kuo. 2017-07-20. Semantic Segmentation with Reverse Attention. https://arxiv.org/abs/1707.06426

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