| 摘要: |
| 计算机视觉因其强大的学习能力,在各种真实场景中得到了广泛应用.随着数据库的发展,利用数据库中成熟的数据管理技术来处理视觉分析应用,已成为一种日益增长的研究趋势.图像、视频和文本等多模态数据的相互融合处理,也促进了视觉分析应用的多样性和准确性.近年来,因深度学习的兴起,支持深度学习的视觉分析应用开始受到广泛关注.然而,传统的数据库管理技术在深度学习场景下面临着复杂视觉分析语义难以表达、应用执行效率低等问题.因此,支持深度学习的视觉数据库管理系统得到了广泛关注.综述了目前视觉数据库管理系统的研究进展:首先,总结了视觉数据库管理系统在不同层面上面临的挑战,包括编程接口、查询优化、执行调度和数据存储;其次,分别探讨了上述4个层面上的相关技术;最后,对视觉数据库管理系统未来的研究方向进行了展望. |
| 关键词: 深度学习 视觉分析 数据库管理系统 |
| DOI：10.13328/j.cnki.jos.007075 |
| 分类号: |
| 基金项目:国家自然科学基金(62272168);上海市自然科学基金(23ZR1419900) |
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| Research Progress on Vision Database Management Systems Supporting Deep Learning |
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DING Guang-Yao1,2, XU Chen1,2, QIAN Wei-Ning1,2, ZHOU Ao-Ying1,2
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1.School of Data Science and Engineering, East China Normal University, Shanghai 200062, China;2.Shanghai Engineering Research Center on Big Data Management(East China Normal University), Shanghai 200062, China
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| Abstract: |
| Computer vision has been widely used in various real-world scenarios due to its powerful learning ability. With the development of databases, there is a growing trend in research to exploit mature data management techniques in databases for vision analytics applications. The integration and processing of multimodal data, including images, video and text, promotes diversity and improves accuracy in vision analytics applications. In recent years, due to the popularization of deep learning, there has been a growing interest in vision analytics applications that support deep learning. Nevertheless, traditional database management techniques in deep learning scenarios suffer from the issues such as lack of semantics for vision analytics and inefficiency in application execution. Hence, vision database management systems that support deep learning have been widely studied. This study reviews the progress of vision database management systems. First, this study summarizes the challenges faced by vision database management systems in different dimensions, including programming interface, query optimization, execution scheduling, and data storage. Second, this study discusses the technologies in each of these four dimensions. Finally, the study investigates the future research directions of vision database management systems. |
| Key words: deep learning vision analytics database management system |
