ISSN 1000-1239 CN 11-1777/TP

计算机研究与发展 ›› 2018, Vol. 55 ›› Issue (9): 1946-1958.doi: 10.7544/issn1000-1239.2018.20180168

所属专题: 2018优青专题

• 综述 • 上一篇    下一篇

视觉问答技术研究

俞俊,汪亮,余宙   

  1. (杭州电子科技大学计算机学院 杭州 310018) (复杂系统建模与仿真教育部重点实验室(杭州电子科技大学) 杭州 310018) (yujun@hdu.edu.cn)
  • 出版日期: 2018-09-01
  • 基金资助: 
    国家自然科学基金优秀青年基金项目(61622205) This work was supported by the National Natural Science Foundation of China for Excellent Young Scientists (61622205).

Research on Visual Question Answering Techniques

Yu Jun, Wang Liang, Yu Zhou   

  1. (School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou 310018) (Key Laboratory of Complex System Modeling and Simulation (Hangzhou Dianzi University), Ministry of Education, Hangzhou 310018)
  • Online: 2018-09-01

摘要: 随着深度学习在计算机视觉、自然语言处理领域取得的长足进展,现有方法已经能准确理解视觉对象和自然语言的语义,并在此基础上开展跨媒体数据表达与交互研究.近年来,视觉问答(visual question answering, VQA)是跨媒体表达与交互方向上的研究热点问题.视觉问答旨在让计算机理解图像内容后根据自然语言输入的查询进行自动回答.围绕视觉问答问题,从概念、模型、数据集等方面对近年来的研究进展进行综述,同时探讨现有工作存在的不足;最后从方法论、应用和平台等多方面对视觉问答未来的研究方向进行了展望.

关键词: 视觉问答, 视觉推理, 视频问答, 深度学习, 知识网络

Abstract: With the significant advances of deep learning in computer vision and natural language processing, the existing methods are able to accurately understand the semantics of visual contents and natural languages, and carry out research on cross-media data representation and interaction. In recent years, visual question answering (VQA) has become a hot spot in cross-media expression and interaction area. The target of VQA is to learn a model to understand the visual content referred by a natural language question, and answer it automatically. This paper summarizes the research progresses in recent years on VQA from the aspects of concepts, models and datasets, and discusses the shortcomings of the current works. Finally, the possible future directions of VQA are discussed on methodology, applications and platforms.

Key words: visual question answering (VQA), visual reasoning, video question answering, deep learning, knowledge network

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