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App. Res. of Comp. Accepted Paper
Vol. 41, 2024 No. 11

Deep hashing method based on cross-scale vision transformer

Yao Peiyuna
Yu Jiongb
Li Xueb
Li Ziyanga
Chen Pengchenga
a. School of software, b. School of Computer Science & Technology, Xinjiang University, Urumqi 830046, China
DOI: 10.19734/j.issn.1001-3695.2024.01.0062

Abstract

To solve the problems of insufficient ability of current deep hashing algorithms to extract cross-scale features and difficulty by fitting the global similarity distribution of data, this paper proposed a Deep Hashing Method Based on Cross-Scale Vision Transformer. Firstly, the method utilized pyramid convolution and cross scale attention mechanism to construct a multi-level encoder to capture the rich semantic information of the image. Secondly, the method proposed a proxy based deep hashing algorithm. This algorithm generated hash proxies for each category, allowing hash codes to learn discriminative class features to reduce the distance from hash proxies of the same category and fit the global similarity distribution of the data. Finally, the method added an angle margin term between the hash proxy and the hash code to expand intra class similarity and inter class differences to generate hash codes with high discriminability. The experimental results conducted on CIFAR-10, ImageNet-100, NUS Wide, and MS COCO show that the average retrieval accuracy of the algorithm is 4.42%, 19.61%, 0.35%, and 15.03% higher than the suboptimal method, respectively, demonstrating the effectiveness of the algorithm.

Foundation Support

国家自然科学基金资助项目(62262064,62266043,61966035)
新疆维吾尔自治区重点研发项目(2022295358)
新疆维吾尔自治区自然科学基金资助项目(2022D01C56)
新疆大学博士研究生创新项目(XJU2022BS072)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2024.01.0062
Publish at: Application Research of Computers Accepted Paper, Vol. 41, 2024 No. 11

Publish History

[2024-07-10] Accepted Paper

Cite This Article

姚佩昀, 于炯, 李雪, 等. 基于跨尺度VisionTransformer的深度哈希算法 [J]. 计算机应用研究, 2024, 41 (11). (2024-09-11). https://doi.org/10.19734/j.issn.1001-3695.2024.01.0062. (Yao Peiyun, Yu Jiong, Li Xue, et al. Deep hashing method based on cross-scale vision transformer [J]. Application Research of Computers, 2024, 41 (11). (2024-09-11). https://doi.org/10.19734/j.issn.1001-3695.2024.01.0062. )

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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