Improving Residual Block for Semantic Image Segmentation | |
Liu, Fei1,2; Liu, Jing2; Fu, Jun1,2; Lu, Hanqing2 | |
2018-09 | |
会议日期 | 2018-9 |
会议地点 | 中国西安 |
英文摘要 | Currently, most state-of-the-art semantic segmentation methods employ residual network as base network. Residual network is composed of residual blocks. In this paper, we present an improved residual block called pyramid residual block to explicitly exploit context information and enhance useful features. In contrast to the standard residual block, the proposed pyramid residual block contains two newly added components: pyramid pooling module and attention mechanism. The former aggregates different-region-based context information. And the latter is able to adaptively re-calibrate feature responses through element-wise multiplication operation, thus enhancing useful features and suppressing less useful ones. Our proposed pyramid residual block demonstrates outstanding performance in PASCAL VOC 2012 segmentation datasets, and improve the segmentation accuracy by a large margin over the standard residual block. |
会议录出版者 | IEEE |
语种 | 英语 |
内容类型 | 会议论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/48670] |
专题 | 自动化研究所_模式识别国家重点实验室_图像与视频分析团队 |
通讯作者 | Liu, Jing |
作者单位 | 1.University of Chinese Academy of Sciences 2.National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences |
推荐引用方式 GB/T 7714 | Liu, Fei,Liu, Jing,Fu, Jun,et al. Improving Residual Block for Semantic Image Segmentation[C]. 见:. 中国西安. 2018-9. |
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