A Survey of Data Representation for Multi-Modality Event Detection and Evolution

Kejing Xiao, Zhaopeng Qian, Biao Qin
<span title="2022-02-20">2022</span> <i title="MDPI AG"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/smrngspzhzce7dy6ofycrfxbim" style="color: black;">Applied Sciences</a> </i> &nbsp;
The rapid growth of online data has made it very convenient for people to obtain information. However, it also leads to the problem of "information overload". Therefore, how to detect hot events from the massive amount of information has always been a problem. With the development of multimedia platforms, event detection has gradually developed from traditional single modality detection to multi-modality detection and is receiving increasing attention. The goal of multi-modality event detection
more &raquo; ... is to discover events from a huge amount of online data with different data structures, such as texts, images and videos. These data represent real-world events from different perspectives so that they can provide more information about an event. In addition, event evolution is also a meaningful research direction; it models how events change dynamically over time and has great significance for event analysis. This paper comprehensively reviews the existing research on event detection and evolution. We first give a series of necessary definitions of event detection and evolution. Next, we discuss the techniques of data representation for event detection, including textual, visual, and multi-modality content. Finally, we review event evolution under multi-modality data. Furthermore, we review several public datasets and compare their results. At the end of this paper, we provide a conclusion and discuss future work.
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