| 冯耀功,于剑,桑基韬,杨朋波.基于知识的零样本视觉识别任务综述.软件学报,2021,32(2):0 |
| 基于知识的零样本视觉识别任务综述 |
| Zero-Shot Learning in Computer Vision Based on Knowledge: A Survey |
| 投稿时间：2020-07-03 修订日期：2020-08-11 |
| DOI：10.13328/j.cnki.jos.006146 |
| 中文关键词: 零样本学习 初级知识 抽象知识 外部知识 图像分类 |
| 英文关键词:zero-shot learning primary knowledge abstract knowledge external knowledge image classification |
| 基金项目:国家重点研发计划（2017YFC1703506）；国家自然科学基金（61632004，61832002，61672518）；中央高校基本科研业务费专项资金（2018JBZ006，2019JBZ110，2020YJS030） |
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| 中文摘要: |
| 零样本学习旨在通过运用已学到的已知类知识去认知未知类.近年来，“数据+知识驱动”已经成为当下的新潮流，而在计算机视觉领域内的零样本任务中，“知识”本身却缺乏统一明确的定义.本文针对这种情况，尝试从知识的角度出发，梳理了本领域内“知识”这一概念所覆盖的范畴，共划分为初级知识、抽象知识以及外部知识.基于前面对知识的定义和划分梳理了当前的零样本学习（主要是图像分类任务的模型）工作，分为基于初级知识的零样本模型、基于抽象知识的零样本模型以及引入外部知识的零样本模型.本文还对领域内存在的域偏移和枢纽点问题进行了阐述，并基于问题对现有工作进行了总结归纳.最后总结了目前常用的图像分类任务的数据集和知识库，图像分类实验评估标准以及代表性的模型实验结果；并对未来工作进行了展望. |
| 英文摘要: |
| Zero-shot learning aims to recognize the unseen classes by using the knowledge of the seen classes that has been learned. In recent years, ‘knowledge + data driven’ has become a new trend but lacking of unified definition of ‘knowledge’ in the current zero-shot tasks of computer vision. In this paper, we try to define the ‘knowledge’ and divided it in this field into three categories which are primary knowledge, abstract knowledge and external knowledge. In addition, based on the definition and classification of knowledge, we sorted out the current works on zero-shot learning (mainly in image classification task), which divided them into zero-shot models based on primary knowledge, zero-shot models based on abstract knowledge and zero-shot models based on external knowledge. We also introduce the problems which are domain shift and hubness in this field, and further summarizes existing works based on the problems. Finally, we summarized the datasets and knowledge bases that commonly used in image classification tasks, the evaluation criteria of image classification experiment and the experimental results of representative models. And the future works were summarized and prospected. |
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