Summary of the 2021 Embedded Deep Learning Object Detection Model Compression Competition for Traffic in Asian Countries-Reference-Cited by-同舟云学术

Summary of the 2021 Embedded Deep Learning Object Detection Model Compression Competition for Traffic in Asian Countries

Published:2021-08-24 Issue: Volume: Page:
ISSN:
Container-title:Proceedings of the 2021 International Conference on Multimedia Retrieval
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Author:

Ni Yu-Shu¹,Tsai Chia-Chi²,Guo Jiun-In¹,Hwang Jenq-Neng³,Wu Bo-Xun¹,Hu Po-Chi⁴,Kuo Ted T.¹,Chen Po-Yu²,Kuo Hsien-Kai²

Affiliation:

1. National Yang Ming Chiao Tung University, Hsinchu, Taiwan Roc

2. MediaTek Inc., Hsinchu, Taiwan Roc

3. University of Washington, Seattle, WA, USA

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

Funder

Ministry of Science and Technology, Taiwan

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3460426.3466932

Reference16 articles.

1. Microsoft COCO: Common Objects in Context

2. J. Redmon and A. Farhadi. 2018. Yolov3: An incremental improvement. arXiv:1804.02767. J. Redmon and A. Farhadi. 2018. Yolov3: An incremental improvement. arXiv:1804.02767.

3. Alexey Bochkovskiy Chien-Yao Wang and HongYuan Mark Liao. 2020. Yolov4: Optimal speed and accuracy of object detection. arXiv:2004.10934. Alexey Bochkovskiy Chien-Yao Wang and HongYuan Mark Liao. 2020. Yolov4: Optimal speed and accuracy of object detection. arXiv:2004.10934.

4. F. Yu et al. 2018. Bdd100k: A diverse driving video database with scalable annotation tooling. arXiv:1805.04687. F. Yu et al. 2018. Bdd100k: A diverse driving video database with scalable annotation tooling. arXiv:1805.04687.

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