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The application of transfer learning in film and television works
Author(s): Bihan Lian; Cong Jin; Nansu Wang; Yajie Li; Hongliang Wang
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Paper Abstract

Many personalized advertisement recommendation studies suffer from the problem of only certain tagged items can be recommended in video playback, which mean it can’t recommend more produces to users that they really like . It also doesn’t know the users really like at the source. Due to the large number of scene changes in different video, the users can choose more items they like. This study attempts to adopt transfer knowledge to solve the problem of data volume to provide users with a variety of options. Aiming at the image classification model of learning on big data set, this paper proposes a method to solve the problem of scene object recognition in TV program,such as movies,TV plays, variety shows and short video, by transferring a pre-trained depth image classification model to a specific task. In a small training set, Learning high-level representations on a small training set to produce a task-specific target model. Experiments on small data sets and real face sets collected by myself show that the transfer learning is effective and efficient. In the application of video, this study provides a theoretical basis for personalized click recommendation of video users.

Paper Details

Date Published: 27 November 2019
PDF: 6 pages
Proc. SPIE 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence, 113210A (27 November 2019); doi: 10.1117/12.2538839
Show Author Affiliations
Bihan Lian, Communication Univ. of China (China)
Cong Jin, Communication Univ. of China (China)
Nansu Wang, Communication Univ. of China (China)
Yajie Li, Communication Univ. of China (China)
Hongliang Wang, Communication Univ. of China (China)


Published in SPIE Proceedings Vol. 11321:
2019 International Conference on Image and Video Processing, and Artificial Intelligence
Ruidan Su, Editor(s)

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