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porn streamer recognition in Live Video Based on Multimodal Knowledge Distillation

Chinese Journal of Electronics 2021年第6期30卷 1096-1102页

作者： WANG Liyuan ZHANG Jing YAO Jiacheng ZHUO Li Faculty of Information Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology

Although deep learning has reached a higher accuracy for video content analysis, it is not satisfied with practical application demands of porn streamer recognition in live video because of multiple parameters,complex structures of deep network model. In order to improve the recognition efficiency of porn streamer in live video, a deep network model compression method based on multimodal knowledge distillation is ***, the teacher model is trained with visual-speech deep network to obtain the corresponding porn video prediction score. Second, a lightweight student model constructed with Mobile Net V2 and Xception transfers the knowledge from the teacher model by using multimodal knowledge distillation strategy. Finally, porn streamer in live video is recognized by combining the lightweight student model of visualspeech network with the bullet screen text recognition network. Experimental results demonstrate that the proposed method can effectively drop the computation cost and improve the recognition speed under the proper accuracy.

关键词： recognition speed lightweight student model computer aided instruction live video video content analysis feature extraction data compression video signal processing recognition efficiency multimodal knowledge distillation strategy deep learning bullet screen text recognition network corresponding porn video prediction score visual-speech deep network teacher model learning (artificial intelligence) deep network model compression method user modelling porn streamer recognition

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