Knowledge Commons of Institute of Automation,CAS
Deep domain adaptive object detection: a survey | |
Wanyi Li![]() ![]() ![]() | |
2020 | |
会议名称 | IEEE Symposium Series on Computational Intelligence (SSCI) |
会议日期 | 01-04 December 2020 |
会议地点 | Canberra, ACT, Australia |
会议录编者/会议主办者 | IEEE |
摘要 | Deep learning (DL) based object detection has achieved great progress. These methods typically assume that large amount of labeled training data is available, and training and test data are drawn from an identical distribution. However, the two assumptions are not always hold in practice. Deep domain adaptive object detection (DDAOD) has emerged as a new learning paradigm to address the above mentioned challenges. This paper aims to review the state-of-the-art progress on deep domain adaptive object detection approaches. Firstly, we introduce briefly the basic concepts of deep domain adaptation. Secondly, the deep domain adaptive detectors are classified into five categories and detailed descriptions of representative methods in each category are provided. Finally, insights for future research trend are presented. |
关键词 | Object detection Deep domain adaptation Adaptive object detection |
学科门类 | 工学::控制科学与工程 ; 工学::计算机科学与技术(可授工学、理学学位) |
DOI | 10.1109/SSCI47803.2020.9308604 |
URL | 查看原文 |
收录类别 | EI |
资助项目 | National Natural Science Foundation of China[91748131] ; National Natural Science Foundation of China[61771471] |
语种 | 英语 |
是否为代表性论文 | 否 |
七大方向——子方向分类 | 目标检测、跟踪与识别 |
国重实验室规划方向分类 | 实体人工智能系统感认知 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 会议论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/51584 |
专题 | 多模态人工智能系统全国重点实验室 多模态人工智能系统全国重点实验室_智能机器人系统研究 |
通讯作者 | Wanyi Li |
作者单位 | Institute of Automation, Chinese Academy of Sciences |
推荐引用方式 GB/T 7714 | Wanyi Li,Fuyu Li,Yongkang Luo,et al. Deep domain adaptive object detection: a survey[C]//IEEE,2020. |
条目包含的文件 | 下载所有文件 | |||||
文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
20_ssci_Deep Domain (129KB) | 会议论文 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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