Abstract
Multimedia data analysis is at the heart of multimedia research, where the patterns, information, and knowledge are mined and recognized from the multimedia data for various applications. We have witnessed significant performance improvements in multimedia data analysis driven by the recent advances in artificial intelligence and deep learning. While most of the existing methods focus on analyzing unimodal data, many novel multimodal multimedia data analysis techniques have also been developed and achieved better performance than those using unimodal data. Multimodal multimedia data has proven to be effective to improve the performance and reliability of data analysis systems and applications. It is also important to match and retrieve data with similar contents across modalities. However, due to the highly complex nature of multimodal multimedia data, further research in multimodal multimedia data analysis is needed to address the challenges including missing modalities, lack of benchmark datasets, and so on.
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