Knowledge Commons of Institute of Automation,CAS
Attention-guided Unified Network for Panoptic Segmentation | |
Li, Yanwei1,2; Chen, Xinze3; Zhu, Zheng1,2; Xie, Lingxi4,5; Huang, Guan3; Du, Dalong3; Wang, Xingang1 | |
2019 | |
会议名称 | IEEE Conference on Computer Vision and Pattern Recognition (CVPR) |
会议日期 | 2019.6.16-2019.6.20 |
会议地点 | 美国长滩 |
出版者 | IEEE |
摘要 | This paper studies panoptic segmentation, a recently proposed task which segments foreground (FG) objects at the instance level as well as background (BG) contents at the semantic level. Existing methods mostly dealt with these two problems separately, but in this paper, we reveal the underlying relationship between them, in particular, FG objects provide complementary cues to assist BG understanding. Our approach, named the Attention-guided Unified Network (AUNet), is a unified framework with two branches for FG and BG segmentation simultaneously. Two sources of attentions are added to the BG branch, namely, RPN and FG segmentation mask to provide object-level and pixellevel attentions, respectively. Our approach is generalized to different backbones with consistent accuracy gain in both FG and BG segmentation, and also sets new state-of-thearts both in the MS-COCO (46.5% PQ) and Cityscapes (59.0% PQ) benchmarks. |
收录类别 | EI |
语种 | 英语 |
七大方向——子方向分类 | 图像视频处理与分析 |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/39169 |
专题 | 中科院工业视觉智能装备工程实验室_精密感知与控制 |
通讯作者 | Wang, Xingang |
作者单位 | 1.Institute of Automation, Chinese Academy of Sciences 2.University of Chinese Academy of Sciences 3.Horizon Robotics 4.Johns Hopkins University 5.Noah’s Ark Lab, Huawei Inc |
第一作者单位 | 中国科学院自动化研究所 |
通讯作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Li, Yanwei,Chen, Xinze,Zhu, Zheng,et al. Attention-guided Unified Network for Panoptic Segmentation[C]:IEEE,2019. |
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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 | ||
Li_Attention-Guided_(3076KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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