DID: Disentangling-Imprinting-Distilling for Continuous Low-Shot Detection | |
Chen, Xianyu1,2; Wang, Yali1,2; Liu, Jianzhuang3; Qiao, Yu1,2 | |
刊名 | IEEE TRANSACTIONS ON IMAGE PROCESSING |
2020 | |
卷号 | 29页码:7765-7778 |
关键词 | Object detection low-shot learning continuous learning deep learning transfer learning |
ISSN号 | 1057-7149 |
DOI | 10.1109/TIP.2020.3006397 |
通讯作者 | Qiao, Yu(yu.qiao@siat.ac.cn) |
英文摘要 | Practical applications often face a challenging continuous low-shot detection scenario, where a target detection task only has a few annotated training images, and a number of such new tasks come in sequence. To address this challenge, we propose a generic detection scheme via Disentangling-Imprinting-Distilling (DID). DID can leverage delicate transfer insights into the main development flow of deep learning, i.e., architecture design (Disentangling), model initialization (Imprinting), and training methodology (Distilling). This allows DID to be a simple but effective solution for continuous low-shot detection. In addition, DID can integrate the supervision from different detection tasks into a progressive learning procedure. As a result, one can efficiently adapt the previous detector for a new low-shot task, while maintaining the learned detection knowledge in the history. Finally, we evaluate our DID on a number of challenging settings in continuous/incremental low-shot detection. All the results demonstrate that our DID outperforms the recent state-of-the-art approaches. The code and models are available at https://github.com/chenxy99/DID. |
资助项目 | Science and Technology Service Network Initiative of Chinese Academy of Sciences[KFJ-STS-QYZX-092] ; National Natural Science Foundation of China[61876176] ; National Natural Science Foundation of China[U1713208] ; Shenzhen Basic Research Program[JCYJ20170818164704758] ; Shenzhen Basic Research Program[CXB201104220032A] ; Shenzhen Institute of Artificial Intelligence and Robotics for Society |
WOS研究方向 | Computer Science ; Engineering |
语种 | 英语 |
出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
WOS记录号 | WOS:000549387700006 |
资助机构 | Science and Technology Service Network Initiative of Chinese Academy of Sciences ; National Natural Science Foundation of China ; Shenzhen Basic Research Program ; Shenzhen Institute of Artificial Intelligence and Robotics for Society |
内容类型 | 期刊论文 |
源URL | [http://ir.ia.ac.cn/handle/173211/40177] |
专题 | 自动化研究所_智能制造技术与系统研究中心 |
通讯作者 | Qiao, Yu |
作者单位 | 1.Chinese Acad Sci, Shenzhen Inst Adv Technol, Shenzhen Key Lab Comp Vis & Pattern Recognit, Shenzhen 518055, Peoples R China 2.Shenzhen Inst Artificial Intelligence & Robot Soc, SIAT Branch, Shenzhen 518055, Peoples R China 3.Huawei Technol Co Ltd, Noahs Ark Lab, Shenzhen 518129, Peoples R China |
推荐引用方式 GB/T 7714 | Chen, Xianyu,Wang, Yali,Liu, Jianzhuang,et al. DID: Disentangling-Imprinting-Distilling for Continuous Low-Shot Detection[J]. IEEE TRANSACTIONS ON IMAGE PROCESSING,2020,29:7765-7778. |
APA | Chen, Xianyu,Wang, Yali,Liu, Jianzhuang,&Qiao, Yu.(2020).DID: Disentangling-Imprinting-Distilling for Continuous Low-Shot Detection.IEEE TRANSACTIONS ON IMAGE PROCESSING,29,7765-7778. |
MLA | Chen, Xianyu,et al."DID: Disentangling-Imprinting-Distilling for Continuous Low-Shot Detection".IEEE TRANSACTIONS ON IMAGE PROCESSING 29(2020):7765-7778. |
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