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View Decomposition and Adversarial for Semantic Segmentation
He Guan; Zhaoxiang Zhang
2018-06
Conference NameThe 15th Pacific Rim International Conference on Artificial Intelligence
Conference DateAugust, 28-31, 2018
Conference PlaceNanjing
AbstractThe adversarial training strategy has been effectively validated because it maintains high-level contextual consistency. However, limited to the weak capability of a simple discriminator, it is irresponsible and unreasonable to identify one from the sample source at a time. We introduce a novel discriminator module called Multi-View Decomposition which transforms the discriminator role from general teacher to specific adversary. The proposed module separates single sample into a series of class inter-independent streams and extracts corresponding features from current mask. The key insight in the MVD module is that the final source decision can be aggregated from all available views rather than a harsh critic. Our experimental results demonstrate that the proposed module can improve performance on PASCAL VOC 2012 and PASCAL Context dataset further.
KeywordView Decomposition Adversarial Semantic Segmentation
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Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/21599
Collection类脑智能研究中心
Affiliation1.University of Chinese Academy of Sciences
2.Research Center for Brain-inspired Intelligence, CASIA
3.CAS Center for Excellence in Brain Science and Intelligence Technology
4.National Laboratory of Pattern Recognition, CASIA
Recommended Citation
GB/T 7714
He Guan,Zhaoxiang Zhang. View Decomposition and Adversarial for Semantic Segmentation[C],2018.
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