Journals & Publications

Title: Cross-Media Analysis and Reasoning: Advances and Directions

Authors: Peng, YX; Zhu, WW; Zhao, Y; Xu, CS; Huang, QM; Lu, HQ; Zheng, QH; Huang, TJ; Gao, W

Author Full Names: Peng, Yu-xin; Zhu, Wen-wu; Zhao, Yao; Xu, Chang-sheng; Huang, Qing-ming; Lu, Han-qing; Zheng, Qing-hua; Huang, Tie-jun; Gao, Wen

Source: FRONTIERS OF INFORMATION TECHNOLOGY & ELECTRONIC ENGINEERING, 18 (1):44-57; 10.1631/FITEE.1601787 JAN 2017

Language: English

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.

ISSN: 2095-9184

eISSN: 2095-9230

IDS Number: EL3TC

Unique ID: WOS:000394541200004

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