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- W2973131617 endingPage "3764" @default.
- W2973131617 startingPage "3740" @default.
- W2973131617 abstract "With the rapid development of Internet and multimedia services in the past decade, a huge amount of user-generated and service provider-generated multimedia data become available. These data are heterogeneous and multi-modal in nature, imposing great challenges for processing and analyzing them. Multi-modal data consist of a mixture of various types of data from different modalities such as texts, images, videos, audios etc. In this article, we present a deep and comprehensive overview for multi-modal analysis in multimedia. We introduce two scientific research problems, data-driven correlational representation and knowledge-guided fusion for multimedia analysis. To address the two scientific problems, we investigate them from the following aspects: 1) multi-modal correlational representation: multi-modal fusion of data across different modalities, and 2) multi-modal data and knowledge fusion: multi-modal fusion of data with domain knowledge. More specifically, on data-driven correlational representation, we highlight three important categories of methods, such as multi-modal deep representation, multi-modal transfer learning, and multi-modal hashing. On knowledge-guided fusion, we discuss the approaches for fusing knowledge with data and four exemplar applications that require various kinds of domain knowledge, including multi-modal visual question answering, multi-modal video summarization, multi-modal visual pattern mining and multi-modal recommendation. Finally, we bring forward our insights and future research directions." @default.
- W2973131617 created "2019-09-19" @default.
- W2973131617 creator A5006822602 @default.
- W2973131617 creator A5014011362 @default.
- W2973131617 creator A5076495171 @default.
- W2973131617 date "2020-10-01" @default.
- W2973131617 modified "2023-10-03" @default.
- W2973131617 title "Multi-Modal Deep Analysis for Multimedia" @default.
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