Research Article
CIC Chinese Image Captioning Based on Image Label Information
@INPROCEEDINGS{10.1007/978-3-030-77428-8_9, author={Xindong You and Likun Lu and Hang Zhou and Xueqiang Lv}, title={CIC Chinese Image Captioning Based on Image Label Information}, proceedings={Tools for Design, Implementation and Verification of Emerging Information Technologies. 15th EAI International Conference, TridentCom 2020, Virtual Event, November 13, 2020, Proceedings}, proceedings_a={TRIDENTCOM}, year={2021}, month={5}, keywords={Chinese image caption Convolution neural network Recurrent neural network Deep learning Chinese image tagging}, doi={10.1007/978-3-030-77428-8_9} }- Xindong You
Likun Lu
Hang Zhou
Xueqiang Lv
Year: 2021
CIC Chinese Image Captioning Based on Image Label Information
TRIDENTCOM
Springer
DOI: 10.1007/978-3-030-77428-8_9
Abstract
Although image captioning technology has made great progress in recent years, the quality of Chinese image description is far from enough. In this paper, we focus on the problem of Chinese image captioning with the aim to improve the quality of Chinese image description. A novel framework for Chinese image captioning based on image label information (CIC) is proposed in this paper. Firstly, image label information is extracted by a multi-layer model with shortcut connections. Then the label information is input into the neural network with an extension of LSTM, which we coin L-LSTM for short, to generate the Chinese image descriptions. Extensive experiments are conducted on various image caption datasets such as Flickr8k-cn, Flickr30 k-cn. The experimental results verify the effectiveness of the proposed framework (CIC). It obtains 27.1% and 21.2% BLEU4 average values of Flickr8k-cn and Flickr30k-cn, respectively, which outperforms the state-of-art model in Chinese image captioning domain.
