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Deep Cross-Modal Face Naming for People News Retrieval by Yong Tian; Lian Zhou; Yuejie Zhang; Tao Zhang; Weiguo Fan is a Computer Science article available to read on EtoBox.

What is Deep Cross-Modal Face Naming for People News Retrieval about?

How to integrate multimodal information sources for face naming in multimodal news is a hot and yet challenging problem. A novel deep cross-modal face naming scheme is developed in this paper to facilitate more effective people news retrieval for large-scale multimodal news. This scheme integrates deep multimodal analysis, cross-modal correlation learning, and multimodal information mining, in which the efficient naming mechanism aims to cluster the deep features of different modalities into a common space to explore their inter-related correlations, and a special Web mining pattern is designed to optimize the name-face matching for rare non-celebrity. Such a cross-modal face naming model can be treated as a problem of bi-media semantic mapping and modeled as an inter-related correlation distribution over deep representations of multimodal news, in which the most important is to create more effective cross-modal name-face correlation and measure to what degree they are correlated. The experiments on a large number of public data from Yahoo! News have obtained very positive results and demonstrated the effectiveness of the proposed model.

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Author

Yong Tian; Lian Zhou; Yuejie Zhang; Tao Zhang; Weiguo Fan

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Published

2021

Language

EN

Field

Computer Science (Physical Sciences)