The 2020 Low-Power Computer Vision Challenge

被引：1

作者：

Hu, Xiao ^{[1
]}

Chang, Ming-Ching ^{[2
]}

Chen, Yuwei ^{[2
]}

Sridhar, Rahul ^{[3
]}

Hu, Zhenyu ^{[3
]}

Xue, Yunhe ^{[3
]}

Wu, Zhenyu ^{[3
]}

Pi, Pengcheng ^{[3
]}

Shen, Jiayi ^{[3
]}

Tan, Jianchao ^{[4
]}

Lian, Xiangru ^{[4
]}

Liu, Ji ^{[4
]}

Wang, Zhangyang ^{[5
]}

Liu, Chia-Hsiang ^{[6
]}

Han, Yu-Shin ^{[6
]}

Sung, Yuan-Yao ^{[6
]}

Lee, Yi ^{[6
]}

Wu, Kai-Chiang ^{[6
]}

Guo, Wei-Xiang ^{[7
]}

Lee, Rick ^{[8
]}

Liang, Shengwen ^{[8
]}

Wang, Zerun ^{[9
]}

Ding, Guiguang ^{[9
]}

Zhang, Gang ^{[10
]}

Xi, Teng ^{[10
]}

Chen, Yubei ^{[11
]}

Cai, Han ^{[12
]}

Zhu, Ligeng ^{[12
]}

Zhang, Zhekai ^{[12
]}

Han, Song ^{[12
]}

Jeong, Seonghwan ^{[13
]}

Kwon, YoungMin ^{[13
]}

Wang, Tianzhe ^{[14
]}

Pan, Jeffery ^{[15
]}

机构：

[1] Purdue Univ, W Lafayette, IN 47907 USA

[2] SUNY Albany, Albany, NY 12222 USA

[3] Texas A&M Univ, College Stn, TX USA

[4] Kwai Inc, Seattle AI Lab, Seattle, WA USA

[5] Univ Texas Austin, Austin, TX 78712 USA

[6] Natl Chiao Tung Univ, Hsinchu, Taiwan

[7] Natl Tsing Hua Univ, Hsinchu, Taiwan

[8] Chinese Acad Sci, Inst Comp Technol, SKLCA, Beijing, Peoples R China

[9] Tsinghua Univ, Sch Software, Beijing, Peoples R China

[10] Baidu Inc, Depariment Comp Vis Technol VIS, Beijing, Peoples R China

[11] Univ Calif Berkeley, Berkeley, CA 94720 USA

[12] MIT, 77 Massachusetts Ave, Cambridge, MA 02139 USA

[13] State Univ New York, Incheon, South Korea

[14] Georgia Inst Technol, Atlanta, GA 30332 USA

[15] Phillips Acad, Andover, MA USA

来源：

2021 IEEE 3RD INTERNATIONAL CONFERENCE ON ARTIFICIAL INTELLIGENCE CIRCUITS AND SYSTEMS (AICAS) | 2021年

关键词：

Low-power; computer vision; challenge; drone; scene text; FPGA; model compression; knowledge distilling; NAS;

D O I：

10.1109/AICAS51828.2021.9458522

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

AI computer vision has advanced significantly in recent years. IoT and edge computing devices such as mobile phones have become the primary computing platform for many end users. Mobile devices such as robots and drones that rely on batteries demand for energy efficient computation. Since 2015, the IEEE Annual International Low-Power Computer Vision Challenge (LPCVC) was held to identify energy-efficient AI and computer vision solutions. The 2020 LPCVC includes three challenge tracks: (1) PyTorch UAV Video Track, (2) FPGA Image Track, and (3) On-device Visual Intelligence Competition (OVIC) Tenforflow Track. This paper summarizes the 2020 winning solutions from the three tracks of LPCVC competitions. Methods and future directions for energy-efficient AI and computer vision research are discussed.

引用

页数：4

共 1 条

[1] Low-Power Computer Vision: Status, Challenges, and Opportunities
Alyamkin, Sergei
Ardi, Matthew
Berg, Alexander C.
Brighton, Achille
Chen, Bo
Chen, Yiran
Cheng, Hsin-Pai
Fan, Zichen
Feng, Chen
Fu, Bo
Gauen, Kent
Goel, Abhinav
Goncharenko, Alexander
Guo, Xuyang
Ha, Soonhoi
Howard, Andrew
Hu, Xiao
Huang, Yuanjun
Kim, Jaeyoun
Ko, Jong Gook
Kondratyev, Alexander
Lee, Junhyeok
Lee, Seungjae
Lee, Suwoong
Li, Zichao
Liang, Zhiyu
Liu, Juzheng
Liu, Xin
Lu, Yang
Lu, Yung-Hsiang
Malik, Deeptanshu
Nguyen, Hong Hanh
Park, Eunbyung
Repin, Denis
Shen, Liang
Sheng, Tao
Sun, Fei
Svitov, David
Thiruvathukal, George K.
Zhang, Baiwu
Zhang, Jingchi
Zhang, Xiaopeng
Zhuo, Shaojie
Kang, D.
[J]. IEEE JOURNAL ON EMERGING AND SELECTED TOPICS IN CIRCUITS AND SYSTEMS, 2019, 9 (02) : 411 - 421

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