2020年
ACMU-nets: Attention cascading modular U-nets incorporating squeeze and excitation blocks
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
- ,
- ,
- 巻
- 12116 LNCS
- 号
- 開始ページ
- 118
- 終了ページ
- 130
- 記述言語
- 掲載種別
- 研究論文(国際会議プロシーディングス)
- DOI
- 10.1007/978-3-030-57058-3_9
In document analysis research, image-to-image conversion models such as a U-Net have been shown significant performance. Recently, cascaded U-Nets research is suggested for solving complex document analysis studies. However, improving performance by adding U-Net modules requires using too many parameters in cascaded U-Nets. Therefore, in this paper, we propose a method for enhancing the performance of cascaded U-Nets. We suggest a novel document image binarization method by utilizing Cascading Modular U-Nets (CMU-Nets) and Squeeze and Excitation blocks (SE-blocks). Through verification experiments, we point out the problems caused by the use of SE-blocks in existing CMU-Nets and suggest how to use SE-blocks in CMU-Nets. We use the Document Image Binarization (DIBCO) 2017 dataset to evaluate the proposed model.
- リンク情報
- ID情報
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- DOI : 10.1007/978-3-030-57058-3_9
- ISSN : 0302-9743
- eISSN : 1611-3349
- SCOPUS ID : 85090094559