Dense semantic embedding network for image captioning
Xiao, Xinyu1,2; Wang, Lingfeng1; Ding, Kun1; Xiang, Shiming1,2; Pan, Chunhong1
发表期刊PATTERN RECOGNITION
ISSN0031-3203
2019-06-01
卷号90页码:285-296
通讯作者Xiao, Xinyu(xinyu.xiao@nlpr.ia.ac.cn)
摘要Recently, attributes that contain high-level semantic information of image are always used as a complementary knowledge to improve image captioning performance. However, the use of attributes in prior works cannot excavate the latent visual concepts effectively. At each time step, the semantic information which is sensitive to the predicted word could be different. In this paper, we propose a Dense Semantic Embedding Network (DSEN) for this task. The distinct operation of this network is to densely embed the attributes with the multi-modal of image and text at each step of word generation. The discriminative semantic information hidden in these attributes is formatted in form of global likelihood probabilities. As a result, this dense embedding can modulate the feature distributions of the image, text modals and the hidden states to explicit semantic representation. Furthermore, to improve the discrimination of attributes, a Threshold ReLU (TReLU) is proposed. In addition, a bidirectional LSTM structure is incorporated into the DSEN to capture both the previous and future contexts. Extensive experiments on the COCO and Flickr30K datasets achieve superior results when compared with the state-of-the-art models for the tasks of both image captioning and image-text cross modal retrieval. Most remarkably, our method obtains outstanding performance on the retrieval task, compared with the state-of-the-art models. (C) 2019 Published by Elsevier Ltd.
关键词Image captioning Retrieval High-level semantic information Visual concept Densely embedding Long short-term memory
DOI10.1016/j.patcog.2019.01.028
收录类别SCI
语种英语
资助项目Beijing Natural Science Foundation[L172053] ; Beijing Natural Science Foundation[4162064] ; National Natural Science Foundation of China[61573352] ; National Natural Science Foundation of China[61773377] ; National Natural Science Foundation of China[91646207] ; National Natural Science Foundation of China[91646207] ; National Natural Science Foundation of China[61773377] ; National Natural Science Foundation of China[61573352] ; Beijing Natural Science Foundation[4162064] ; Beijing Natural Science Foundation[L172053]
项目资助者National Natural Science Foundation of China ; Beijing Natural Science Foundation
WOS研究方向Computer Science ; Engineering
WOS类目Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号WOS:000463130400024
出版者ELSEVIER SCI LTD
七大方向——子方向分类图像视频处理与分析
引用统计
被引频次:33[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/23493
专题模式识别国家重点实验室_先进时空数据分析与学习
通讯作者Xiao, Xinyu
作者单位1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Sch Artificial Intelligence, Beijing 100049, Peoples R China
第一作者单位模式识别国家重点实验室
通讯作者单位模式识别国家重点实验室
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Xiao, Xinyu,Wang, Lingfeng,Ding, Kun,et al. Dense semantic embedding network for image captioning[J]. PATTERN RECOGNITION,2019,90:285-296.
APA Xiao, Xinyu,Wang, Lingfeng,Ding, Kun,Xiang, Shiming,&Pan, Chunhong.(2019).Dense semantic embedding network for image captioning.PATTERN RECOGNITION,90,285-296.
MLA Xiao, Xinyu,et al."Dense semantic embedding network for image captioning".PATTERN RECOGNITION 90(2019):285-296.
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