CASIA OpenIR
Multi-view pedestrian captioning with an attention topic CNN model
Liu, Quan1,3,4; Chen, Yingying1,2; Wang, Jinqiao1,2; Zhang, Sijiong1,3,4
Source PublicationCOMPUTERS IN INDUSTRY
ISSN0166-3615
2018-05-01
Volume97Pages:47-53
Corresponding AuthorLiu, Quan(quanliu@niaot.ac.cn)
AbstractImage captioning is a fundamental task connecting computer vision and natural language processing. Recent researches usually concentrate on generic image captioning or video captioning among thousands of classes. However, they fail to cover detailed semantics and cannot effectively deal with a specific class of objects, such as pedestrian. Pedestrian captioning plays a critical role for analysis, identification and retrieval in massive collections of video data. Therefore, in this paper, we propose a novel approach to generate multi-view captions for pedestrian images with a topic attention mechanism on global and local semantic regions. Firstly, we detect different local parts of pedestrian and utilize a deep convolutional neural network (CNN) to extract a series of features from these local regions and the whole image. Then, we aggregate these features with a topic attention CNN model to produce a representative vector richly expressing the image from a different view at each time step. This feature vector is taken as input to a hierarchical recurrent neural network to generate multi-view captions for pedestrian images. Finally, a new dataset named CASIA_Pedestrian including 5000 pedestrian images and sentences pairs is collected to evaluate the performance of pedestrian captioning. Experiments and comparison results show the superiority of our proposed approach. (C) 2018 Elsevier B.V. All rights reserved.
KeywordImage captioning Pedestrian description Multi-view captions
DOI10.1016/j.compind.2018.01.015
Indexed BySCI
Language英语
Funding ProjectNational Natural Science Foundation of China[61772527]
Funding OrganizationNational Natural Science Foundation of China
WOS Research AreaComputer Science
WOS SubjectComputer Science, Interdisciplinary Applications
WOS IDWOS:000432504700006
PublisherELSEVIER SCIENCE BV
Citation statistics
Cited Times:2[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/28179
Collection中国科学院自动化研究所
Corresponding AuthorLiu, Quan
Affiliation1.Univ Chinese Acad Sci, 95 Zhongguancun East Rd, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
3.Chinese Acad Sci, Nanjing Inst Astron Opt & Technol, Natl Astron Observ, Nanjing 210042, Jiangsu, Peoples R China
4.Chinese Acad Sci, Nanjing Inst Astron Opt & Technol, Key Lab Astron Opt & Technol, Nanjing 210042, Jiangsu, Peoples R China
Recommended Citation
GB/T 7714
Liu, Quan,Chen, Yingying,Wang, Jinqiao,et al. Multi-view pedestrian captioning with an attention topic CNN model[J]. COMPUTERS IN INDUSTRY,2018,97:47-53.
APA Liu, Quan,Chen, Yingying,Wang, Jinqiao,&Zhang, Sijiong.(2018).Multi-view pedestrian captioning with an attention topic CNN model.COMPUTERS IN INDUSTRY,97,47-53.
MLA Liu, Quan,et al."Multi-view pedestrian captioning with an attention topic CNN model".COMPUTERS IN INDUSTRY 97(2018):47-53.
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