CASIA OpenIR  > 模式识别国家重点实验室  > 机器人视觉
Semantic Segmentation with Modified Deep Residual Networks
Chen, Xinze1,2; Chen, Guangliang1,2; Cai, Yinghao1; Wen, Dayong1; Li, Heping1
2016-10
Conference NameChinese Conference on Pattern Recognition(CCPR)
Source PublicationProceedings of Chinese Conference on Pattern Recognition
Conference DateNovember, 2016
Conference PlaceCheng Du, China
AbstractA novel semantic segmentation method is proposed, which consists of the following three parts: (I) First, a simple yet effective data augmentation method is introduced without any extra GPU memory cost during training. (II) Second, a deeper residual network is constructed through three effective techniques: dilated convolution, LSTM network and multi-scale prediction. (III) Third, an online hard pixels mining is adopted to improve the segmentation performance. We combine these three parts to train an end-to-end network and achieve a new state-ofthe-art segmentation accuracy of 79.3% on PASCAL VOC 2012 test set at the time of submission.
KeywordSemantic Segmentation Data Augmentation Residual Networks Lstm Multi-scale Prediction
Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/14458
Collection模式识别国家重点实验室_机器人视觉
Corresponding AuthorLi, Heping
Affiliation1.中国科学院自动化研究所
2.中国科学院大学
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Chen, Xinze,Chen, Guangliang,Cai, Yinghao,et al. Semantic Segmentation with Modified Deep Residual Networks[C],2016.
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