Porn Streamer Recognition in Live Video Based on Multimodal Knowledge Distillation
Porn Streamer Recognition in Live Video Based on Multimodal Knowledge Distillation作者机构：Faculty of Information Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing University of Technology
出 版 物：《Chinese Journal of Electronics》 (电子学报(英文))
年 卷 期：2021年第30卷第6期
页 面：1096-1102页
核心收录：
学科分类：0710[理学-生物学] 08[工学] 080203[工学-机械设计及理论] 0802[工学-机械工程] 0812[工学-计算机科学与技术（可授工学、理学学位）]
基 金：supported by the National Natural Science Foundation of China(No.61971016, No.61531006) the Beijing Education Committee Cooperation Beijing Natural Science Foundation (No.KZ201910005007)
主 题：Live video Porn streamer recognition Multimodal Knowledge distillation Lightweight student model
摘 要：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 proposed.First, 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.
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