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.

中文翻译：

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基于互补注意力和生成对抗网络的社交网络跨媒体搜索方法

社交网络的迅猛发展给人们的生活带来了极大的便利。积累了大量的跨媒体大数据，如文本、图像、视频数据。跨媒体搜索可以促进信息的快速查询，从而使用户可以获得对社交网络有用的内容。然而，跨媒体数据在社交网络中存在语义鸿沟和稀疏性，这给跨媒体搜索带来了挑战。为了缓解语义差距和稀疏性，我们提出了一种基于互补注意和生成对抗网络（CAGS）的跨媒体搜索方法。为了获得高质量的特征表示，我们构建了一个包含图像的焦点和非焦点特征的互补注意力机制，以实现社交网络中跨媒体数据的一致关联。通过设计跨媒体对抗学习过程，我们可以获得跨媒体数据的通用语义表示，进一步缓解社交网络的语义差距和稀疏性问题。最后，我们进行相似度计算以实现准确的跨媒体搜索。我们利用两个标准的跨媒体数据集构建了四个搜索任务，以验证所提出的 CAGS 的搜索性能。