跨媒体分析与推理：研究进展与发展方向

彭宇新; 朱文武; 赵耀; 徐常胜; 黄庆明; 卢汉清; 郑庆华; 黄铁军; 高文

doi:10.1631/FITEE.1601787

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跨媒体分析与推理：研究进展与发展方向

人工智能2.0专辑 | Updated：2022-05-19

- 跨媒体分析与推理：研究进展与发展方向
- Cross-media analysis and reasoning: advances and directions
- 信息与电子工程前沿（英文） 2017年18卷第1期页码：44-57
- Affiliations：
  
  1Institute of Computer Science and Technology, Peking University, Beijing 100871, China
  2Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China
  3Institute of Information Science, Beijing Jiaotong University, Beijing 100044, China
  4National Laboratory of Pattern Recognition, Institute of Automation, Chinese Academy of Sciences, Beijing 100190, China
  5Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China
  6Department of Computer Science and Technology, Xi'an Jiaotong University, Xi'an 710049, China
  7School of Electronics Engineering and Computer Science, Peking University, Beijing 100871, China
- Author bio：
  
  E-mail:pengyuxin@pku.edu.cn
  E-mail:wwzhu@tsinghua.edu.cn
- Funds：
  
  Project supported by the National Natural Science Foundation of China (Nos. 61371128, U1611461, 61425025, and 61532005)
- DOI：10.1631/FITEE.1601787
  中图分类号： TP391
- 收稿：2016-12-07，
  
  录用：2016-12-30，
  
  纸质出版：2017-01
- Accepted：
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彭宇新, 朱文武, 赵耀, 等. 跨媒体分析与推理：研究进展与发展方向[J]. 信息与电子工程前沿（英文）, 2017,18(1):44-57.

Yu-xin PENG, Wen-wu ZHU, Yao ZHAO, et al. Cross-media analysis and reasoning: advances and directions[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(1): 44-57.
彭宇新, 朱文武, 赵耀, 等. 跨媒体分析与推理：研究进展与发展方向[J]. 信息与电子工程前沿（英文）, 2017,18(1):44-57. DOI： 10.1631/FITEE.1601787.

Yu-xin PENG, Wen-wu ZHU, Yao ZHAO, et al. Cross-media analysis and reasoning: advances and directions[J]. Frontiers of Information Technology & Electronic Engineering, 2017, 18(1): 44-57. DOI： 10.1631/FITEE.1601787.

摘要

跨媒体分析与推理是计算机科学的热点问题，也是人工智能中一个具有广阔前景的研究方向。目前，尚未有文献对跨媒体分析与推理的现有方法进行归纳总结并给出它的研究进展、挑战及发展方向。为解决这些问题，本文从七个方面进行综述：（1）跨媒体统一表征理论与模型；（2）跨媒体关联理解与深度挖掘；（3）跨媒体知识图谱构建与学习方法；（4）跨媒体知识演化与推理；（5）跨媒体描述与生成；（6）跨媒体智能引擎；（7）跨媒体智能应用。本文的目标是给出跨媒体分析与推理的方法、进展以及发展方向，吸引更多人关注该领域的最新进展，通过探讨面临的挑战和研究方向，为研究者提供重要参考。

Abstract

Cross-media analysis and reasoning is an active research area in computer science

and a promising direction for artificial intelligence. However

to the best of our knowledge

no existing work has summarized the state-of-the-art methods for cross-media analysis and reasoning or presented advances

challenges

and future directions for the field. To address these issues

we provide an overview as follows: (1) theory and model for cross-media uniform representation; (2) cross-media correlation understanding and deep mining; (3) cross-media knowledge graph construction and learning methodologies; (4) cross-media knowledge evolution and reasoning; (5) cross-media description and generation; (6) cross-media intelligent engines; and (7) cross-media intelligent applications. By presenting approaches

advances

and future directions in cross-media analysis and reasoning

our goal is not only to draw more attention to the state-of-the-art advances in the field

but also to provide technical insights by discussing the challenges and research directions in these areas.

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