计算机科学与探索 ›› 2023, Vol. 17 ›› Issue (1): 53-73.DOI: 10.3778/j.issn.1673-9418.2206020
刘春磊,陈天恩,王聪,姜舒文,陈栋
出版日期:
2023-01-01
发布日期:
2023-01-01
LIU Chunlei, CHEN Tian‘en2,3, WANG Cong, JIANG Shuwen, CHEN Dong
Online:
2023-01-01
Published:
2023-01-01
摘要: 目标检测是计算机视觉方向的热点领域,其通常需要大量的标注图像用于模型训练,这将花费大量的人力和物力来实现。同时,由于真实世界中的数据存在固有的长尾分布,大部分对象的样本数量都比较稀少,比如众多非常见疾病等,很难获得大量的标注图像。小样本目标检测只需要提供少量的标注信息,就能够检测出感兴趣的对象,对小样本目标检测方法做了详细综述。首先回顾了通用目标检测的发展及其存在的问题,从而引出小样本目标检测的概念,对同小样本目标检测相关的其他任务做了区分阐述。之后介绍了现有小样本目标检测基于迁移学习和基于元学习的两种经典范式。根据不同方法的改进策略,将小样本目标检测分为基于注意力机制、图卷积神经网络、度量学习和数据增强四种类型,对这些方法中使用到的公开数据集和评估指标进行了说明,对比分析了不同方法的优缺点、适用场景以及在不同数据集上的性能表现。最后讨论了小样本目标检测的实际应用领域和未来的研究趋势。
刘春磊, 陈天恩, 王聪, 姜舒文, 陈栋. 小样本目标检测研究综述[J]. 计算机科学与探索, 2023, 17(1): 53-73.
LIU Chunlei, CHEN Tian‘en, WANG Cong, JIANG Shuwen, CHEN Dong. Survey of Few-Shot Object Detection[J]. Journal of Frontiers of Computer Science and Technology, 2023, 17(1): 53-73.
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