The Journal of China Universities of Posts and Telecommunications ›› 2019, Vol. 26 ›› Issue (2): 52-58.doi: 10.19682/j.cnki.1005-8885.2019.1006

• Artificial intelligence • Previous Articles     Next Articles

Video description with subject, verb and object supervision

Wang Yue, Liu Jinlai, Wang Xiaojie   

  1. School of Computer Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
  • Received:2018-12-06 Revised:2019-04-01 Online:2019-04-30 Published:2019-06-14
  • Contact: Corresponding author: Wang Yue, E-mail:
  • About author:Corresponding author: Wang Yue, E-mail:
  • Supported by:
    This work was supported by the National Natural Science Foundation of China (61273365), and 111 Project (B08004).

Abstract: Video description aims to generate descriptive natural language for videos. Inspired from the deep neural network (DNN) used in the machine translation, the video description (VD) task applies the convolutional neural network (CNN) to extracting video features and the long short-term memory (LSTM) to generating descriptions. However, some models generate incorrect words and syntax. The reason may because that the previous models only apply LSTM to generate sentences, which learn insufficient linguistic information. In order to solve this problem, an end-to-end DNN model incorporated subject, verb and object (SVO) supervision is proposed. Experimental results on a publicly available dataset, i. e. Youtube2Text, indicate that our model gets a 58.4% consensus-based image
description evaluation (CIDEr) value. It outperforms the mean pool and video description with first feed (VD-FF) models, demonstrating the effectiveness of SVO supervision.

Key words: VD, DNN, CNN, LSTM

CLC Number: