Dense Image Captioning Based on Precise Feature Extraction

被引:4
|
作者
Zhang, Zhiqiang [1 ]
Zhang, Yunye [1 ]
Shi, Yan [1 ]
Yu, Wenxin [1 ]
Nie, Li [1 ]
He, Gang [2 ]
Fan, Yibo [3 ]
Yang, Zhuo [4 ]
机构
[1] Southwest Univ Sci & Technol, Mianyang, Sichuan, Peoples R China
[2] Xidian Univ, Xian, Peoples R China
[3] Fudan Univ, State Key Lab ASIC & Syst, Shanghai, Peoples R China
[4] Guangdong Univ Technol, Guangzhou, Peoples R China
来源
NEURAL INFORMATION PROCESSING, ICONIP 2019, PT V | 2019年 / 1143卷
基金
中国国家自然科学基金;
关键词
Dense captioning; Computer vision; Feature extraction; Location and description; Deep learning;
D O I
10.1007/978-3-030-36802-9_10
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
Image captioning is a challenging problem in computer vision, which has numerous practical applications. Recently, the method of dense image captioning has emerged, which realizes the full understanding of the image by localizing and describing multiple salient regions covering the image. Despite there are state-of-the-art approaches encouraging progress, the ability to position and to describe the target area correspondingly is not enough as we expect. To alleviate this challenge, a precise feature extraction method (PFE) is proposed in this paper to further enhance the effect of dense image captioning. Our model is evaluated on the Visual Genome dataset. It demonstrated that our method is better than other state-of-the-art methods.
引用
收藏
页码:83 / 90
页数:8
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