Technology of Graphic & Image
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App. Res. of Comp. Printed Article
Vol. 40, 2023 No. 6
1876-1881

Method for long-tailed instance segmentation based on memory bank and confidence calibration

Fan Xinyue1,2
Liu Teng3
Bao Hong1,2
Pan Weiguo1,2
Liang Tianjiao1,2
Li Han1,2
1. Beijing Key Laboratory of Information Service Engineering, Beijing Union University, Beijing 100101, China
2. College of Robotics, Beijing Union University, Beijing 100020, China
3. Information Network Center, Beijing University of Posts & Telecommunications, Beijing 100876, China
DOI: 10.19734/j.issn.1001-3695.2022.09.0486

Abstract

Long-tail characteristic of data has always been a great challenge in solving computer vision problems. To solve the difficulties brought to instance segmentation, image re-sampling, a simple and efficient method has always been used. However, there may be many classes in one image, it's hard to balance different classes on the data level. This paper proposed a object-centric post-processable memory bank method. Firstly, it introduced a memory bank model and set up an object-based storage policy to address the number imbalance of categories. Then, in order to increase the predicted score for tail classes and common classes, it used a post-processing calibration to adjust the confidence score of each class. This paper verified the effectiveness of the proposed method by experiments on LVIS dataset. The accuracy can be improved by 2.2% compared to EQL method.

Foundation Support

国家自然科学基金资助项目(61932012,62102033,61802019,62272049,62171042)
北京联合大学科研资助项目(ZK10202202)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2022.09.0486
Publish at: Application Research of Computers Printed Article, Vol. 40, 2023 No. 6
Section: Technology of Graphic & Image
Pages: 1876-1881
Serial Number: 1001-3695(2023)06-044-1876-06

Publish History

[2022-12-12] Accepted Paper
[2023-06-05] Printed Article

Cite This Article

范馨月, 刘腾, 鲍泓, 等. 基于记忆库和后处理方法解决长尾实例分割问题 [J]. 计算机应用研究, 2023, 40 (6): 1876-1881. (Fan Xinyue, Liu Teng, Bao Hong, et al. Method for long-tailed instance segmentation based on memory bank and confidence calibration [J]. Application Research of Computers, 2023, 40 (6): 1876-1881. )

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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