CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
Densely Connected Deconvolutional Network For Semantic Segmentation
Fu, Jun; Liu, Jing; Wang, Yuhang; Lu, Hanqing
2017
Conference NameIEEE International Conference on Image Processing
Conference Date2017.9.17-9.20
Conference PlaceBeijing,China
Contribution Rank1
Abstract

Recent progress in semantic segmentation has been driven by improving the spatial resolution under Fully Convolutional Networks(FCNs). To address this problem, we propose a Densely Connected Deconvolutional Network (DCDN) for semantic segmentation. In DCDN, multiple shallow deconvolutional networks, which are called as DCDN units, are stacked one by one to make the structure deeper and guarantee the fine recovery of localization information, meanwhile, the inter-unit and intra-unit dense connections are designed to make the network easy to train since the connections improve the flow of information and gradients throughout the network. Besides, the intermediate supervisions are applied to each DCDN unit to ensure the fast convergence. Extensive experiments on two urban scene datasets, i.e.,CamVid and GATECH, demonstrate that the proposed model achieves better performance than some state-of-the-art methods without using any post-processing, pretrained model, nor temporal information, whilst requiring less parameters

Indexed ByEI
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/20117
Collection模式识别国家重点实验室_图像与视频分析
模式识别国家重点实验室
Affiliation中国科学院自动化研究所
Recommended Citation
GB/T 7714
Fu, Jun,Liu, Jing,Wang, Yuhang,et al. Densely Connected Deconvolutional Network For Semantic Segmentation[C],2017.
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