Workshop Overview Official Website
- Computer Vision research has given little consideration to speed or computation time, and even less to constraints such as power/energy, memory footprint and model size.
- Nevertheless, addressing all of these metrics is essential if advances in Computer Vision are going to be widely available on mobile and AR/VR devices.
- This workshop will focus on efficient deep learning algorithms, models, and systems for computer vision.
2021 ICCV Workshop on Low-Power Computer Vision Organizing Committee
Ming-Ching ChangUniversity at Albany - SUNY
Shu-Ching ChenFlorida International University
Yiran ChenACM Speical Interest Group on Design AutomationDuke University
Callie HaoGeorgia Institute of Technology
Xiao Humanage lpcv.ai websitePurdue student
Mark LiaoAcademia Sinica, Taiwan
Naveen PurushothamXilinx Inc.
Qiang QiuPurdue University
Mei-Ling ShyuIEEE Multimedia Technical CommitteeMiami University
George K. ThiruvathukalLoyola University Chiicago
Xuefeng XiaoByteDance Inc
Wei ZakharovPurdue University
Computer vision technologies have made impressive progress in recent years, but often at the expense of increasingly complex models needing more and more computational and storage resources.
This workshop aims to improve the energy efficiency of computer vision for running on systems with stringent resource constraints.
This workshop will discuss the state of the art of low-power computer vision, challenges in creating efficient vision solutions, promising technologies that can achieve the goals, methods to acquire and label data, benchmarks and metrics to evaluate progress and success.