An Overview of Cross-media Retrieval: Concepts, Methodologies, Benchmarks and Challenges
Yuxin Peng, Xin Huang, and Yunzhen Zhao

TL;DR
This paper provides a comprehensive overview of cross-media retrieval, discussing concepts, methodologies, benchmarks, and challenges, and introduces a new dataset to facilitate future research in this emerging field.
Contribution
It offers a detailed review of cross-media retrieval, introduces the first multi-media dataset XMedia, and provides benchmarks to support future algorithm development.
Findings
Reviewed over 100 references on cross-media retrieval
Constructed the first publicly available multi-media dataset XMedia
Provided benchmarks and experimental results for evaluation
Abstract
Multimedia retrieval plays an indispensable role in big data utilization. Past efforts mainly focused on single-media retrieval. However, the requirements of users are highly flexible, such as retrieving the relevant audio clips with one query of image. So challenges stemming from the "media gap", which means that representations of different media types are inconsistent, have attracted increasing attention. Cross-media retrieval is designed for the scenarios where the queries and retrieval results are of different media types. As a relatively new research topic, its concepts, methodologies and benchmarks are still not clear in the literatures. To address these issues, we review more than 100 references, give an overview including the concepts, methodologies, major challenges and open issues, as well as build up the benchmarks including datasets and experimental results. Researchers can…
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Taxonomy
TopicsAdvanced Image and Video Retrieval Techniques · Multimodal Machine Learning Applications · Video Analysis and Summarization
