? Objectness Region Enhancement Networks for Scene Parsing
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Journal of Computer Science and Technology 2017, Vol. 32 Issue (4) :683-700    DOI: 10.1007/s11390-017-1751-x
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Objectness Region Enhancement Networks for Scene Parsing
Xin-Yu Ou1,2,3, Member, CCF, IEEE, Ping Li1,*, He-Fei Ling1, Member, CCF, ACM, IEEE, Si Liu2, Member, CCF, ACM, IEEE, Tian-Jiang Wang1, Member, CCF, ACM, IEEE, Dan Li1
1 School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan 430074, China;
2 Institute of Information Engineering, Chinese Academy of Sciences, Beijing 100091, China;
3 Cadres Online Learning Institute of Yunnan Province, Yunnan Open University, Kunming 650223, China

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Abstract Semantic segmentation has recently witnessed rapid progress, but existing methods only focus on identifying objects or instances. In this work, we aim to address the task of semantic understanding of scenes with deep learning. Different from many existing methods, our method focuses on putting forward some techniques to improve the existing algorithms, rather than to propose a whole new framework. Objectness enhancement is the first effective technique. It exploits the detection module to produce object region proposals with category probability, and these regions are used to weight the parsing feature map directly. "Extra background" category, as a specific category, is often attached to the category space for improving parsing result in semantic and instance segmentation tasks. In scene parsing tasks, extra background category is still beneficial to improve the model in training. However, some pixels may be assigned into this nonexistent category in inference. Black-hole filling technique is proposed to avoid the incorrect classification. For verifying these two techniques, we integrate them into a parsing framework for generating parsing result. We call this unified framework as Objectness Enhancement Network (OENet). Compared with previous work, our proposed OENet system effectively improves the performance over the original model on SceneParse150 scene parsing dataset, reaching 38.4 mIoU (mean intersectionover-union) and 77.9% accuracy in the validation set without assembling multiple models. Its effectiveness is also verified on the Cityscapes dataset.
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Keywordsobjectness region enhancement   black-hole filling   scene parsing   instance enhancement   objectness region proposal     
Received 2016-12-20;
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This work was supported by the Joint Funds of the National Natural Science Foundation of China under Grant No. U1536203, the National Natural Science Foundation of China under Grant Nos. 61572493, 61572214, and 61502185, the Major Scientific and Technological Innovation Project of Hubei Province of China under Grant No. 2015AAA013, the Open Project Program of the National Laboratory of Pattern Recognition of China under Grant No. 201600035, the Key Program of the Natural Science Foundation of the Open University of China under Grant No. G16F3702Z, and the Young Scientists Fund of the Natural Science Foundation of the Open University of China under Grant No. G16F2505Q.

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Xin-Yu Ou, Ping Li, He-Fei Ling, Si Liu, Tian-Jiang Wang, Dan Li.Objectness Region Enhancement Networks for Scene Parsing[J]  Journal of Computer Science and Technology, 2017,V32(4): 683-700
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