Deep attribute-aware hashing for cross retrieval

该项目旨在通过开发一个深度属性感知框架，将异构特征嵌入到共享数据空间中，实现有效和高效的跨系统信息检索，使个人、行业和政府能够自由访问来自不同来源的不同媒体类型所携带的重要和相关信息。随着异构数据源的激增，迫切需要支持跨不同媒体类型和领域的搜索和检索。该项目开发的框架使用深度学习方法开发有意义的图像属性，以在将哈希函数附加到数据时积极弥合模态差距和领域差距。本课题将对多媒体检索的研究起到重要的推动作用，有利于解决异构多媒体数据在应用中的一系列相关问题。

This project aims to enable individuals, industries and governments, to freely access vital and linked information carried in different media types from different sources by developing a deep attribute-aware framework that embeds heterogeneous features into a shared data space to achieve effective and efficient cross system information retrieval. With the proliferation of heterogeneous data sources, there is an urgent need to enable search and retrieval across different media types and domains. The framework developed by the project uses deep learning methods to develop meaningful image attributes to positively bridge the modality gap and the domain gap when hash functions are affixed to data. This project will significantly advance the research of multimedia retrieval, and benefit a series of related research problems whenever heterogeneous multimedia data are involved in their applications.