Recurrent attention LSTM model for image chinese caption generation

Chaoying Zhang, Yaping Dai, Yanyan Cheng, Zhiyang Jia, Kaoru Hirota

科研成果: 书/报告/会议事项章节会议稿件同行评审

2 引用 (Scopus)

摘要

A Recurrent Attention LSTM model (RAL) is proposed for image Chinese caption generation. The model uses Inception-v4 as CNN model developed by Google to extract image features while the recurrent attention LSTM mechanism determines feature weights. The model can generate words accurately because of adding the weights of image region. Therefore, the proposed model is able to generate more relevant descriptions and improve the efficiency of the system. Compared with Neural Image Caption (NIC) model, the experiment results show that the performance of the proposed model is improved by 1.8% with BLEU-4 metrics and 6.2% with CIDEr metrics on the AI Challenger Image Chinese Captioning dataset.

源语言英语
主期刊名Proceedings - 2018 Joint 10th International Conference on Soft Computing and Intelligent Systems and 19th International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2018
出版商Institute of Electrical and Electronics Engineers Inc.
808-813
页数6
ISBN(电子版)9781538626337
DOI
出版状态已出版 - 2 7月 2018
活动Joint 10th International Conference on Soft Computing and Intelligent Systems and 19th International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2018 - Toyama, 日本
期限: 5 12月 20188 12月 2018

出版系列

姓名Proceedings - 2018 Joint 10th International Conference on Soft Computing and Intelligent Systems and 19th International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2018

会议

会议Joint 10th International Conference on Soft Computing and Intelligent Systems and 19th International Symposium on Advanced Intelligent Systems, SCIS-ISIS 2018
国家/地区日本
Toyama
时期5/12/188/12/18

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