Image Annotation through Adaptive Dependency Fusion
Wang Fangxin(王方心)1,2; Liu Jie2; Zhang Shuwu2,3; Zhang Guixuan2; Zheng Yang2; Li Xiaoqian1,2
2018-12
Conference NameIEEE International Conference on Progress in Informatics and Computing
Conference Date2018-12
Conference Place苏州
Abstract

In order to improve the performance of image annotation, recently proposed methods build their model combining multiple dependencies from relations between image and label (image/label), between images (image/image) and between labels (label/label). However, most of these methods cannot make multiple dependencies work jointly, and their performances is largely depending on the results predicted by image/label dependency. To address this problem, we propose an end-to-
end image annotation model to associate these dependencies with the prediction path, which is composed of a series of labels in the order they are detected. Specially, our model can adaptively adjust the prediction path: from those easy-to-detect relevant labels to these hard-to-detect relevant ones. To validate the effective of the model, we conduct experiments on three well-known public datasets, COCO 2014, IAPR TC-12 and NUSWIDE, and achieve better performance than the state-of-the-art methods.

KeywordImage Annotation Multiple Dependencies End-to-end Prediction Path
Language英语
Document Type会议论文
Identifierhttp://ir.ia.ac.cn/handle/173211/26112
Collection数字内容技术与服务研究中心_新媒体服务与管理技术
Corresponding AuthorLiu Jie
Affiliation1.中国科学院大学
2.中国科学院自动化研究所
3.北京电影学院,AICFVE
First Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
Recommended Citation
GB/T 7714
Wang Fangxin,Liu Jie,Zhang Shuwu,et al. Image Annotation through Adaptive Dependency Fusion[C],2018.
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