Semantic Segmentation with Modified Deep Residual Networks
文献类型：会议论文
| 作者 | Chen, Xinze1,2 ; Chen, Guangliang1,2; Cai, Yinghao1; Wen, Dayong1; Li, Heping1
|
| 出版日期 | 2016-10 |
| 会议日期 | November, 2016 |
| 会议地点 | Cheng Du, China |
| 关键词 | Semantic Segmentation Data Augmentation Residual Networks Lstm Multi-scale Prediction |
| 英文摘要 | A 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. |
| 会议录 | Proceedings of Chinese Conference on Pattern Recognition
 |
| 语种 | 英语 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/14458]  |
| 专题 | 自动化研究所_模式识别国家重点实验室_机器人视觉团队 |
| 通讯作者 | Li, Heping |
| 作者单位 | 1.中国科学院自动化研究所 2.中国科学院大学 |
| 推荐引用方式 GB/T 7714 | Chen, Xinze,Chen, Guangliang,Cai, Yinghao,et al. Semantic Segmentation with Modified Deep Residual Networks[C]. 见:. Cheng Du, China. November, 2016. |
入库方式： OAI收割
来源：自动化研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。