New Branch Optimization Design Based on RefineNet

Gengyun Ren; Xiujie Qu

doi:10.1109/ICAICA50127.2020.9182507

New Branch Optimization Design Based on RefineNet

Gengyun Ren, Xiujie Qu

集成电路与电子学院

Beijing Institute of Technology

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

摘要

Usually, in semantic segmentation, researchers only use the final output layer for the network training, which means, we ignore information from other layers. To this situation, we design new branches for output, which can output rough small-scale prediction from the middle of the network. With the new branches, the middle of the network will get better control and guidance in the process of training, and also we can make better use of balanced semantic information and spatial information in the middle of the network. With the addition of new branches, more information can be used in the network training. This makes mIoU accuracy 4% increase on CamVid dataset based on RefineNet.

源语言	英语
主期刊名	Proceedings of 2020 IEEE International Conference on Artificial Intelligence and Computer Applications, ICAICA 2020
出版商	Institute of Electrical and Electronics Engineers Inc.
页	591-594
页数	4
ISBN（电子版）	9781728170046
DOI	https://doi.org/10.1109/ICAICA50127.2020.9182507
出版状态	已出版 - 6月 2020
活动	2020 IEEE International Conference on Artificial Intelligence and Computer Applications, ICAICA 2020 - Dalian, 中国期限: 27 6月 2020 → 29 6月 2020

出版系列

姓名	Proceedings of 2020 IEEE International Conference on Artificial Intelligence and Computer Applications, ICAICA 2020

会议

会议	2020 IEEE International Conference on Artificial Intelligence and Computer Applications, ICAICA 2020
国家/地区	中国
市	Dalian
时期	27/06/20 → 29/06/20

访问文件

10.1109/ICAICA50127.2020.9182507

其它文件与链接

链接到 Scopus 的出版物

引用此

Ren, G., & Qu, X. (2020). New Branch Optimization Design Based on RefineNet. 在 Proceedings of 2020 IEEE International Conference on Artificial Intelligence and Computer Applications, ICAICA 2020 (页码 591-594). 文章 9182507 (Proceedings of 2020 IEEE International Conference on Artificial Intelligence and Computer Applications, ICAICA 2020). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICAICA50127.2020.9182507

Ren, Gengyun ; Qu, Xiujie. / New Branch Optimization Design Based on RefineNet. Proceedings of 2020 IEEE International Conference on Artificial Intelligence and Computer Applications, ICAICA 2020. Institute of Electrical and Electronics Engineers Inc., 2020. 页码 591-594 (Proceedings of 2020 IEEE International Conference on Artificial Intelligence and Computer Applications, ICAICA 2020).

@inproceedings{f434c48c27404d23be48e801c1f1ded8,

title = "New Branch Optimization Design Based on RefineNet",

abstract = "Usually, in semantic segmentation, researchers only use the final output layer for the network training, which means, we ignore information from other layers. To this situation, we design new branches for output, which can output rough small-scale prediction from the middle of the network. With the new branches, the middle of the network will get better control and guidance in the process of training, and also we can make better use of balanced semantic information and spatial information in the middle of the network. With the addition of new branches, more information can be used in the network training. This makes mIoU accuracy 4% increase on CamVid dataset based on RefineNet.",

keywords = "Convolutional neural network, RefineNet, computer vision, semantic segmentation",

author = "Gengyun Ren and Xiujie Qu",

note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 2020 IEEE International Conference on Artificial Intelligence and Computer Applications, ICAICA 2020 ; Conference date: 27-06-2020 Through 29-06-2020",

year = "2020",

month = jun,

doi = "10.1109/ICAICA50127.2020.9182507",

language = "English",

series = "Proceedings of 2020 IEEE International Conference on Artificial Intelligence and Computer Applications, ICAICA 2020",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "591--594",

booktitle = "Proceedings of 2020 IEEE International Conference on Artificial Intelligence and Computer Applications, ICAICA 2020",

address = "United States",

}

Ren, G & Qu, X 2020, New Branch Optimization Design Based on RefineNet. 在 Proceedings of 2020 IEEE International Conference on Artificial Intelligence and Computer Applications, ICAICA 2020., 9182507, Proceedings of 2020 IEEE International Conference on Artificial Intelligence and Computer Applications, ICAICA 2020, Institute of Electrical and Electronics Engineers Inc., 页码 591-594, 2020 IEEE International Conference on Artificial Intelligence and Computer Applications, ICAICA 2020, Dalian, 中国, 27/06/20. https://doi.org/10.1109/ICAICA50127.2020.9182507

New Branch Optimization Design Based on RefineNet. / Ren, Gengyun; Qu, Xiujie.
Proceedings of 2020 IEEE International Conference on Artificial Intelligence and Computer Applications, ICAICA 2020. Institute of Electrical and Electronics Engineers Inc., 2020. 页码 591-594 9182507 (Proceedings of 2020 IEEE International Conference on Artificial Intelligence and Computer Applications, ICAICA 2020).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

TY - GEN

T1 - New Branch Optimization Design Based on RefineNet

AU - Ren, Gengyun

AU - Qu, Xiujie

PY - 2020/6

Y1 - 2020/6

N2 - Usually, in semantic segmentation, researchers only use the final output layer for the network training, which means, we ignore information from other layers. To this situation, we design new branches for output, which can output rough small-scale prediction from the middle of the network. With the new branches, the middle of the network will get better control and guidance in the process of training, and also we can make better use of balanced semantic information and spatial information in the middle of the network. With the addition of new branches, more information can be used in the network training. This makes mIoU accuracy 4% increase on CamVid dataset based on RefineNet.

AB - Usually, in semantic segmentation, researchers only use the final output layer for the network training, which means, we ignore information from other layers. To this situation, we design new branches for output, which can output rough small-scale prediction from the middle of the network. With the new branches, the middle of the network will get better control and guidance in the process of training, and also we can make better use of balanced semantic information and spatial information in the middle of the network. With the addition of new branches, more information can be used in the network training. This makes mIoU accuracy 4% increase on CamVid dataset based on RefineNet.

KW - Convolutional neural network

KW - RefineNet

KW - computer vision

KW - semantic segmentation

UR - http://www.scopus.com/inward/record.url?scp=85092179842&partnerID=8YFLogxK

U2 - 10.1109/ICAICA50127.2020.9182507

DO - 10.1109/ICAICA50127.2020.9182507

M3 - Conference contribution

AN - SCOPUS:85092179842

T3 - Proceedings of 2020 IEEE International Conference on Artificial Intelligence and Computer Applications, ICAICA 2020

SP - 591

EP - 594

BT - Proceedings of 2020 IEEE International Conference on Artificial Intelligence and Computer Applications, ICAICA 2020

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2020 IEEE International Conference on Artificial Intelligence and Computer Applications, ICAICA 2020

Y2 - 27 June 2020 through 29 June 2020

ER -

Ren G, Qu X. New Branch Optimization Design Based on RefineNet. 在 Proceedings of 2020 IEEE International Conference on Artificial Intelligence and Computer Applications, ICAICA 2020. Institute of Electrical and Electronics Engineers Inc. 2020. 页码 591-594. 9182507. (Proceedings of 2020 IEEE International Conference on Artificial Intelligence and Computer Applications, ICAICA 2020). doi: 10.1109/ICAICA50127.2020.9182507

New Branch Optimization Design Based on RefineNet

摘要

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