Summary of the 2022 Low-Power Deep Learning Semantic Segmentation Model Compression Competition for Traffic Scene In Asian Countries-Reference-Cited by-同舟云学术

Summary of the 2022 Low-Power Deep Learning Semantic Segmentation Model Compression Competition for Traffic Scene In Asian Countries

Published:2022-07-18 Issue: Volume: Page:
ISSN:
Container-title:2022 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)
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Author:

Ni Yu-Shu¹,Tsai Chia-Chi²,Chen Chih-Cheng³,Chen Po-Yu³,Kuo Hsien-Kai³,Lee Man-Yu³,Chin-Chuan Kuo¹,Hu Zhe-Ln¹,Hu Po-Chi⁴,Kuo Ted T.⁵,Hwang Jenq-Neng⁶,Guo Jiun-In¹

Affiliation:

1. National Yang Ming Chiao Tung University,Department of Electronics Engineering and Institute of Electronics,Taiwan

2. National Cheng Kung University,Department of Electronics Engineering,Taiwan

3. MediaTek Inc.,Taiwan

4. Pervasive Artificial Intelligence Research Labs (PAIR Labs),Hsinchu,Taiwan

5. College of AI National Yang Ming Chiao Tung University,Institute of Intelligent Systems,Taiwan

6. University of Washington,Department of Electrical & Computer Engineering,Seattle,WA,USA

Funder

Ministry of Science and Technology

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/9859264/9859265/09859367.pdf?arnumber=9859367

Reference10 articles.

1. Rethinking BiSeNet For Real-time Semantic Segmentation

2. YOLOv5 (2020);jocher,0

3. Searching for MobileNetV3

4. U-Net: Convolutional Networks for Biomedical Image Segmentation

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