TY - GEN
T1 - Generating descriptions for sequential images with local-object attention and global semantic context modelling
AU - Su, Jing
AU - Lin, Chenghua
AU - Zhou, Mian
AU - Dai, Qingyun
AU - Lv, Haoyu
N1 - Publisher Copyright:
© 2018 Association for Computational Linguistics
PY - 2018
Y1 - 2018
N2 - In this paper, we propose an end-to-end CNN-LSTM model for generating descriptions for sequential images with a local-object attention mechanism. To generate coherent descriptions, we capture global semantic context using a multilayer perceptron, which learns the dependencies between sequential images. A paralleled LSTM network is exploited for decoding the sequence descriptions. Experimental results show that our model outperforms the baseline across three different evaluation metrics on the datasets published by Microsoft.
AB - In this paper, we propose an end-to-end CNN-LSTM model for generating descriptions for sequential images with a local-object attention mechanism. To generate coherent descriptions, we capture global semantic context using a multilayer perceptron, which learns the dependencies between sequential images. A paralleled LSTM network is exploited for decoding the sequence descriptions. Experimental results show that our model outperforms the baseline across three different evaluation metrics on the datasets published by Microsoft.
UR - http://www.scopus.com/inward/record.url?scp=85086364780&partnerID=8YFLogxK
M3 - Conference Proceeding
AN - SCOPUS:85086364780
T3 - 2IS and NLG 2018 - Workshop on Intelligent Interactive Systems and Language Generation, Proceedings of the Workshop
SP - 3
EP - 8
BT - 2IS and NLG 2018 - Workshop on Intelligent Interactive Systems and Language Generation, Proceedings of the Workshop
PB - Association for Computational Linguistics (ACL)
T2 - 2018 Workshop on Intelligent Interactive Systems and Language Generation, 2IS and NLG 2018, collocated with the 11th International Conference on Natural Language Generation, INLG 2018
Y2 - 5 November 2018
ER -