A Virtual-Real Interaction Approach to Object Instance Segmentation in Traffic Scenes
文献类型：期刊论文
| 作者 | Zhang, Hui3,4 ; Luo, Guiyang5; Tian, Yonglin1,4; Wang, Kunfeng4,6; He, Haibo2; Wang, Fei-Yue4
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| 刊名 | IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS
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| 出版日期 | 2021-02-01 |
| 卷号 | 22期号:2页码:863-875 |
| 关键词 | Image segmentation Annotations Computational modeling Automation Object detection Visualization Instance segmentation virtual-real interaction synthetic images distribution discrepancy autonomous vehicles |
| ISSN号 | 1524-9050 |
| DOI | 10.1109/TITS.2019.2961145 |
| 通讯作者 | Wang, Kunfeng(wangkf@mail.buct.edu.cn) |
| 英文摘要 | Object instance segmentation in traffic scenes is an important research topic. For training instance segmentation models, synthetic data can potentially complement real data, alleviating manual effort on annotating real images. However, the data distribution discrepancy between synthetic data and real data hampers the wide applications of synthetic data. In light of that, we propose a virtual-real interaction method for object instance segmentation. This method works over synthetic images with accurate annotations and real images without any labels. The virtual-real interaction guides the model to learn useful information from synthetic data while keeping consistent with real data. We first analyze the data distribution discrepancy from a probabilistic perspective, and divide it into image-level and instance-level discrepancies. Then, we design two components to align these discrepancies, i.e., global-level alignment and local-level alignment. Furthermore, a consistency alignment component is proposed to encourage the consistency between the global-level and the local-level alignment components. We evaluate the proposed approach on the real Cityscapes dataset by adapting from virtual SYNTHIA, Virtual KITTI, and VIPER datasets. The experimental results demonstrate that it achieves significantly better performance than state-of-the-art methods. |
| 资助项目 | National Key Research and Development Program of China[2018YFC1704400] ; National Natural Science Foundation of China[U1811463] |
| WOS研究方向 | Engineering ; Transportation |
| 语种 | 英语 |
| WOS记录号 | WOS:000615045000014 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Key Research and Development Program of China ; National Natural Science Foundation of China |
| 源URL | [http://ir.ia.ac.cn/handle/173211/43213]  |
| 专题 | 自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队 |
| 通讯作者 | Wang, Kunfeng |
| 作者单位 | 1.Univ Sci & Technol China, Dept Automat, Hefei 230027, Peoples R China 2.Univ Rhode Isl, Dept Elect Comp & Biomed Engn, Kingston, RI 02881 USA 3.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 5.Beijing Univ Posts & Telecommun, State Key Lab Networking & Switching Technol, Beijing 100876, Peoples R China 6.Beijing Univ Chem Technol, Coll Informat Sci & Technol, Beijing 100029, Peoples R China |
| 推荐引用方式 GB/T 7714 | Zhang, Hui,Luo, Guiyang,Tian, Yonglin,et al. A Virtual-Real Interaction Approach to Object Instance Segmentation in Traffic Scenes[J]. IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,2021,22(2):863-875. |
| APA | Zhang, Hui,Luo, Guiyang,Tian, Yonglin,Wang, Kunfeng,He, Haibo,&Wang, Fei-Yue.(2021).A Virtual-Real Interaction Approach to Object Instance Segmentation in Traffic Scenes.IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS,22(2),863-875. |
| MLA | Zhang, Hui,et al."A Virtual-Real Interaction Approach to Object Instance Segmentation in Traffic Scenes".IEEE TRANSACTIONS ON INTELLIGENT TRANSPORTATION SYSTEMS 22.2(2021):863-875. |
入库方式： OAI收割
来源：自动化研究所
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