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
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| Source Publication | PATTERN RECOGNITION
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| ISSN | 0031-3203 |
| 2019-06-01 | |
| Volume | 90Pages:285-296 |
| Corresponding Author | Xiao, Xinyu(xinyu.xiao@nlpr.ia.ac.cn) |
| Abstract | 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. |
| Keyword | Image captioning Retrieval High-level semantic information Visual concept Densely embedding Long short-term memory |
| DOI | 10.1016/j.patcog.2019.01.028 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | 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] |
| Funding Organization | National Natural Science Foundation of China ; Beijing Natural Science Foundation |
| WOS Research Area | Computer Science ; Engineering |
| WOS Subject | Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic |
| WOS ID | WOS:000463130400024 |
| Publisher | ELSEVIER SCI LTD |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.ia.ac.cn/handle/173211/23493 |
| Collection | 模式识别国家重点实验室_先进时空数据分析与学习 |
| Corresponding Author | Xiao, Xinyu |
| Affiliation | 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 |
| First Author Affilication | Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China |
| Corresponding Author Affilication | Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China |
| Recommended Citation 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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