Knowledge Commons of Institute of Automation,CAS
A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection | |
Wang, Jiangong1,2; Shen, Tianyu3; Tian, Yonglin1; Wang, Yutong1; Gou, Chao4; Wang, Xiao1; Yao, Fei5; Sun, Changyin6 | |
发表期刊 | IEEE TRANSACTIONS ON INTELLIGENT VEHICLES |
ISSN | 2379-8858 |
2022-09-01 | |
卷号 | 7期号:3页码:441-455 |
摘要 | Large-scale synthetic traffic image datasets have been widely used to make compensate for the insufficient data in real world. However, the mismatch in domain distribution between synthetic datasets and real datasets hinders the application of the synthetic dataset in the actual vision system of intelligent vehicles. In this paper, we propose a novel synthetic-to-real domain adaptation method to settle the mismatch domain distribution from two aspects, i.e., data level and knowledge level. On the data level, a Style-Content Discriminated Data Recombination (SCD-DR) module is proposed, which decouples the style from content and recombines style and content from different domains to generate a hybrid domain as a transition between synthetic and real domains. On the knowledge level, a novel Iterative Cross-Domain Knowledge Transferring (ICD-KT) module including source knowledge learning, knowledge transferring and knowledge refining is designed, which achieves not only effective domain-invariant feature extraction, but also transfers the knowledge from labeled synthetic images to unlabeled actual images. Comprehensive experiments on public virtual and real dataset pairs demonstrate the effectiveness of our proposed synthetic-to-real domain adaptation approach in object detection of traffic scenes. |
其他摘要 |
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关键词 | Object detection Feature extraction Data models Training Knowledge engineering Detectors Computational modeling Computer vision Unsupervised Domain Adaptation Teacher-student learning Traffic object detection |
DOI | 10.1109/TIV.2022.3197818 |
关键词[WOS] | INTELLIGENT VEHICLES ; TRACKING ; VISION ; NETWORKS ; SYSTEMS ; IMAGES |
收录类别 | SCI |
语种 | 英语 |
资助项目 | Key-Area Research and Development Program of Guangdong Province[2020B090921003] ; National Natural Science Foundation of China[U1811463] ; Key Research and Development Program 2020 of Guangzhou[202007050002] ; Shenzhen Science and Technology Program[RCBS20200714114920272] |
项目资助者 | Key-Area Research and Development Program of Guangdong Province ; National Natural Science Foundation of China ; Key Research and Development Program 2020 of Guangzhou ; Shenzhen Science and Technology Program |
WOS研究方向 | Computer Science ; Engineering ; Transportation |
WOS类目 | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic ; Transportation Science & Technology |
WOS记录号 | WOS:000873905600008 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
七大方向——子方向分类 | 机器学习 |
国重实验室规划方向分类 | 视觉信息处理 |
是否有论文关联数据集需要存交 | 是 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/50545 |
专题 | 多模态人工智能系统全国重点实验室_平行智能技术与系统团队 |
通讯作者 | Wang, Xiao |
作者单位 | 1.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China 3.Beijing Normal Univ, Sch Artificial Intelligence, Beijing 100875, Peoples R China 4.Sun Yat Sen Univ, Sch Intelligent Syst Engn, Shenzhen Campus, Shenzhen 518107, Peoples R China 5.North Automat Controltechnol Inst, Taiyuan 030006, Peoples R China 6.Anhui Univ, Sch Artificial Intelligence, Hefei 230039, Peoples R China |
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 | Wang, Jiangong,Shen, Tianyu,Tian, Yonglin,et al. A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection[J]. IEEE TRANSACTIONS ON INTELLIGENT VEHICLES,2022,7(3):441-455. |
APA | Wang, Jiangong.,Shen, Tianyu.,Tian, Yonglin.,Wang, Yutong.,Gou, Chao.,...&Sun, Changyin.(2022).A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection.IEEE TRANSACTIONS ON INTELLIGENT VEHICLES,7(3),441-455. |
MLA | Wang, Jiangong,et al."A Parallel Teacher for Synthetic-to-Real Domain Adaptation of Traffic Object Detection".IEEE TRANSACTIONS ON INTELLIGENT VEHICLES 7.3(2022):441-455. |
条目包含的文件 | ||||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
A_Parallel_Teacher_f(2602KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 |
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