The application of transfer learning in film and television works

Bihan Lian; Cong Jin; Nansu Wang; Yajie Li; Hongliang Wang

doi:10.1117/12.2538839

27 November 2019 The application of transfer learning in film and television works

Bihan Lian, Cong Jin, Nansu Wang, Yajie Li, Hongliang Wang

Proceedings Volume 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence; 113210A (2019) https://doi.org/10.1117/12.2538839
Event: The Second International Conference on Image, Video Processing and Artifical Intelligence, 2019, Shanghai, China

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.

Citation Download Citation

Bihan Lian, Cong Jin, Nansu Wang, Yajie Li, and Hongliang Wang "The application of transfer learning in film and television works", Proc. SPIE 11321, 2019 International Conference on Image and Video Processing, and Artificial Intelligence, 113210A (27 November 2019); https://doi.org/10.1117/12.2538839

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KEYWORDS

Data modeling

Televisions

Convolution

Image classification

Video

Databases

Neural networks

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