Summary of the 2023 Low-Power Deep Learning Object Detection and Semantic Segmentation Multitask Model Compression Competition for Traffic Scene in Asian Countries-Reference-Cited by-同舟云学术

Summary of the 2023 Low-Power Deep Learning Object Detection and Semantic Segmentation Multitask Model Compression Competition for Traffic Scene in Asian Countries

Published:2023-07 Issue: Volume: Page:
ISSN:
Container-title:2023 IEEE International Conference on Multimedia and Expo Workshops (ICMEW)
language:
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Author:

Ni Yu-Shu¹,Tsai Chia-Chi²,Chen Chih-Cheng³,Kuo Hsien-Kai³,Chen Po-Yu³,Hu Po-Chi⁴,Kuo Ted T.⁵,Hwang Jenq-Neng⁶,Guo Jiun-In⁷

Affiliation:

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

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

3. MediaTek Inc.,Hsinchu,Taiwan

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

5. National Yang Ming Chiao Tung University,Department of Institute of Intelligent Systems,Hsinchu,Taiwan

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

7. National Yang Ming Chiao Tung University,Department of Institute of Electronic,Hsinchu,Taiwan

Funder

MediaTek

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx7/10221939/10221894/10222776.pdf?arnumber=10222776

Reference13 articles.

1. RepVGG: Making VGG-style ConvNets Great Again

2. Spatial Pyramid Pooling in Deep Convolutional Networks for Visual Recognition

3. iVS Dataset and ezLabel: A Dataset and a Data Annotation Tool for Deep Learning Based ADAS Applications

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