Retrieval Topic Recurrent Memory Network for Remote Sensing Image Captioning | |
Wang, Binqiang1,2; Zheng, Xiangtao2; Qu, Bo2; Lu, Xiaoqiang2 | |
刊名 | IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing |
2020 | |
卷号 | 13页码:256-270 |
关键词 | Controllable caption recurrentmemory network (MN) remote sensing image (RSI) caption generation retrieval topic |
ISSN号 | 19391404;21511535 |
DOI | 10.1109/JSTARS.2019.2959208 |
产权排序 | 1 |
英文摘要 | Remote sensing image (RSI) captioning aims to generate sentences to describe the content of RSIs. Generally, five sentences are used to describe the RSI in caption datasets. Every sentence can just focus on part of images' contents due to the different attention parts of annotation persons. One annotated sentence may be ambiguous compared with other four sentences. However, previous methods, treating five sentences separately, may generate an ambiguous sentence. In order to consider five sentences together, a collection of words, which named topic words contained common information among five sentences, is jointly incorporated into a captioning model to generate a determinate sentence that covers common contents in RSIs. Instead of employing a naive recurrent neural network, a memory network in which topic words can be naturally included as memory cells is introduced to generate sentences. A novel retrieval topic recurrent memory network is proposed to utilize the topic words. First, a topic repository is built to record the topic words in training datasets. Then, the retrieval strategy is exploited to obtain the topic words for a test image from topic repository. Finally, the retrieved topic words are incorporated into a recurrent memory network to guide the sentence generation. In addition to getting topics through retrieval, the topic words of test images can also be edited manually. The proposed method sheds light on controllability of caption generation. Experiments are conducted on two caption datasets to evaluate the proposed method. © 2008-2012 IEEE. |
语种 | 英语 |
出版者 | Institute of Electrical and Electronics Engineers |
WOS记录号 | WOS:000526639900021 |
内容类型 | 期刊论文 |
源URL | [http://ir.opt.ac.cn/handle/181661/93292] |
专题 | 西安光学精密机械研究所_光学影像学习与分析中心 |
通讯作者 | Zheng, Xiangtao |
作者单位 | 1.University of Chinese Academy of Sciences, Beijing; 100049, China 2.Key Laboratory of Spectral Imaging Technology CAS, Xi'an Institute of Optics and Precision Mechanics, Chinese Academy of Sciences, Xi'an; 710119, China; |
推荐引用方式 GB/T 7714 | Wang, Binqiang,Zheng, Xiangtao,Qu, Bo,et al. Retrieval Topic Recurrent Memory Network for Remote Sensing Image Captioning[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,2020,13:256-270. |
APA | Wang, Binqiang,Zheng, Xiangtao,Qu, Bo,&Lu, Xiaoqiang.(2020).Retrieval Topic Recurrent Memory Network for Remote Sensing Image Captioning.IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing,13,256-270. |
MLA | Wang, Binqiang,et al."Retrieval Topic Recurrent Memory Network for Remote Sensing Image Captioning".IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing 13(2020):256-270. |
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