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Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 41, 2024 No. 7
2215-2220

Diverse image description generation via global and sequential latent embedding

Liu Mingming1,2
Liu Hao2
Wang Dong2
Zhang Haiyan1
1. School of Intelligent Manufacturing, Jiangsu Vocational Institute of Architectural Technology, Xuzhou Jiangsu 221116, China
2. School of Computer Science & Technology, China University of Mining & Technology, Xuzhou Jiangsu 221116, China
DOI: 10.19734/j.issn.1001-3695.2023.09.0510

Abstract

The Transformer-based image captioning models have shown remarkable performance based on the powerful sequence modeling capability. However, most of them focus only on learning deterministic mappings from image space to caption space, i. e., learning how to improve the accuracy of predicting "average" captions, which generally tends to common words, repeated phrases and single sentence, leading to the severe mode collapse problem. To this end, this paper combined the conditional variational encoder with the Transformer-based image captioning model, and proposed the sentence-level and word-level diverse image captioning models, respectively. The proposed models introduced the global and sequential latent embedding learning based on the evidence lower bound(ELBO), which promoted the diversity of Transformer-based image captioning. Quantitative and qualitative experiments on MSCOCO dataset show that both models have the ability of learning one-to-many projections between the image space and the caption space. Compared with the state-of-the-art COS-CVAE, the proposed method with 20 samples improves the CIDEr and Div-2 scores by 1.3 and 33% respectively in the case of 20 samples, improves the CIDEr and Div-2 scores by 11.4 and 14%, respectively in the case of 100 samples. The proposed method can fit the distribution of ground-truth captions well, and achieve a better balance between diversity and accuracy.

Foundation Support

国家自然科学基金资助项目(61801198)
江苏省自然科学基金资助项目(BK20180174)
江苏省青蓝工程资助项目

Publish Information

DOI: 10.19734/j.issn.1001-3695.2023.09.0510
Publish at: Application Research of Computers Printed Article, Vol. 41, 2024 No. 7
Section: Technology of Graphic & Image
Pages: 2215-2220
Serial Number: 1001-3695(2024)07-042-2215-06

Publish History

[2023-12-29] Accepted Paper
[2024-07-05] Printed Article

Cite This Article

刘明明, 刘浩, 王栋, 等. 基于全局与序列变分自编码的图像描述生成 [J]. 计算机应用研究, 2024, 41 (7): 2215-2220. (Liu Mingming, Liu Hao, Wang Dong, et al. Diverse image description generation via global and sequential latent embedding [J]. Application Research of Computers, 2024, 41 (7): 2215-2220. )

About the Journal

  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

Application Research of Computers has many high-level readers and authors, and its readers are mainly senior and middle-level researchers and engineers engaged in the field of computer science, as well as teachers and students majoring in computer science and related majors in colleges and universities. Over the years, the total citation frequency and Web download rate of Application Research of Computers have been ranked among the top of similar academic journals in this discipline, and the academic papers published are highly popular among the readers for their novelty, academics, foresight, orientation and practicality.

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