Knowledge Commons of Institute of Automation,CAS
Densely Connected Deconvolutional Network For Semantic Segmentation | |
Fu, Jun; Liu, Jing; Wang, Yuhang; Lu, Hanqing | |
2017 | |
会议名称 | IEEE International Conference on Image Processing |
会议日期 | 2017.9.17-9.20 |
会议地点 | Beijing,China |
产权排序 | 1 |
摘要 | 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 |
收录类别 | EI |
语种 | 英语 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/20117 |
专题 | 紫东太初大模型研究中心_图像与视频分析 模式识别国家重点实验室 |
作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Fu, Jun,Liu, Jing,Wang, Yuhang,et al. Densely Connected Deconvolutional Network For Semantic Segmentation[C],2017. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
付君_DENSELY CONNECTED(724KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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