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Deep Cross-Modal Face Naming for People News Retrieval.
- Source :
-
IEEE Transactions on Knowledge & Data Engineering . May2021, Vol. 33 Issue 5, p1891-1905. 15p. - Publication Year :
- 2021
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
- Subjects :
- *INFORMATION resources
*FEATURE extraction
*DATA mining
Subjects
Details
- Language :
- English
- ISSN :
- 10414347
- Volume :
- 33
- Issue :
- 5
- Database :
- Academic Search Index
- Journal :
- IEEE Transactions on Knowledge & Data Engineering
- Publication Type :
- Academic Journal
- Accession number :
- 149773601
- Full Text :
- https://doi.org/10.1109/TKDE.2019.2948875