Cross‐media search method based on complementary attention and generative adversarial network for social networks. Issue 8 (3rd November 2021)

• Record Type: Journal Article
• Authors: Shi, Lei; Du, Junping; Cheng, Gang; Liu, Xia; Xiong, Zenggang; Luo, Jia
• Is Part Of: International journal of intelligent systems. Volume 37:Issue 8(2022)
• Page Start: 4393
• Page End: 4416
• Publication Date: 2021-11-03
• Subjects: complementary attention -- cross‐media search -- generative adversarial network -- social networks; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; 006.3
• DOI: 10.1002/int.22723
• Languages: English
• ISSNs: 0884-8173

Abstract

Abstract: The rapid development of the social network has brought great convenience to people's lives. A large amount of cross‐media big data, such as text, image, and video data, has been accumulated. A cross‐media search can facilitate a quick query of information so that users can obtain helpful content for social networks. However, cross‐media data suffer from semantic gaps and sparsity in social networks, which bring challenges to cross‐media searches. To alleviate the semantic gaps and sparsity, we propose a cross‐media search method based on complementary attention and generative adversarial networks (CAGS). To obtain high‐quality feature representations, we build a complementary attention mechanism containing the focused and unfocused features of images to realize the consistent association of cross‐media data in social networks. By designing the cross‐media adversarial learning process, we can obtain a common semantic representation of cross‐media data and further alleviate the semantic gap and sparsity issues for social networks. Finally, we perform a similarity calculation to realize an accurate cross‐media search. We construct four search tasks utilizing two standard cross‐media data sets to verify the search performance of the proposed CAGS.