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Incremental few-shot object detection via knowledge transfer | |
Feng, Hangtao1,2![]() ![]() ![]() ![]() | |
发表期刊 | PATTERN RECOGNITION LETTERS
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ISSN | 0167-8655 |
2022-04-01 | |
卷号 | 156页码:67-73 |
摘要 | As a challenging problem in machine learning, incremental few-shot object detection (iFSD) [1] aims to incrementally detect novel classes with few examples, while keeping the previous knowledge without revisiting base classes. Here, we propose two models based on the observation that when new memories come, new connections will be created between memory cells in the brain [2] . The first one, which is called the multi-class head (MCH) model, simulates how humans add new memory-connections that every time novel classes come, a classification branch is added to predict novel classification. And the second model, called the bi-path multi-class head (BPMCH) model, adds a new backbone, which is initialized with the weight of the base class backbone, to transfer more knowledge of the base class to the novel class. Considering accuracy and speed, we choose the Fully Convolutional One-Stage Object Detection (FCOS) [3] + Adaptive Training Sample Selection (ATSS) [4] detector as our baseline. Our models are first trained on the base classes with abundant examples and then finetuned on novel classes with few examples, which not only maintain the knowledge learned from the base class but also transfer the knowledge to the novel class. Extensive experiments show that our models outperform the state-of-theart model ONCE [1] on the COCO [5] and PASCAL VOC [6] by a large margin. (c) 2022 Elsevier B.V. All rights reserved. |
关键词 | Machine learning Convolutional neural networks Transfer learning Incremental few-shot object detection |
DOI | 10.1016/j.patrec.2022.01.024 |
收录类别 | SCI |
语种 | 英语 |
资助项目 | National Key Research and Development Plan of China[2020AAA0108902] ; Strategic Priority Research Program of Chinese Academy of Science[XDB32050100] ; NSFC[61627808] ; Dongguan Core Technology Research Frontier Project, China[2019622101001] |
项目资助者 | National Key Research and Development Plan of China ; Strategic Priority Research Program of Chinese Academy of Science ; NSFC ; Dongguan Core Technology Research Frontier Project, China |
WOS研究方向 | Computer Science |
WOS类目 | Computer Science, Artificial Intelligence |
WOS记录号 | WOS:000789226600010 |
出版者 | ELSEVIER |
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 | 视觉信息处理 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/49378 |
专题 | 多模态人工智能系统全国重点实验室_机器人理论与应用 多模态人工智能系统全国重点实验室 |
通讯作者 | Liu, Zhiyong |
作者单位 | 1.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing, Peoples R China 2.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing, Peoples R China 3.Chinese Acad Sci China, Ctr Excellence Brain Sci & Intelligence Technol, Beijing, 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 | Feng, Hangtao,Zhang, Lu,Yang, Xu,et al. Incremental few-shot object detection via knowledge transfer[J]. PATTERN RECOGNITION LETTERS,2022,156:67-73. |
APA | Feng, Hangtao,Zhang, Lu,Yang, Xu,&Liu, Zhiyong.(2022).Incremental few-shot object detection via knowledge transfer.PATTERN RECOGNITION LETTERS,156,67-73. |
MLA | Feng, Hangtao,et al."Incremental few-shot object detection via knowledge transfer".PATTERN RECOGNITION LETTERS 156(2022):67-73. |
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