Institutional Repository of Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
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 |
ISSN | 0031-3203 |
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 |
DOI | 10.1016/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 | |
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 |
推荐引用方式 GB/T 7714 | 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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