Low-Power Computer Vision

2021 ICCV Workshop (Virtual)

Call for Papers

Nashville, Tennessee, USA


Call For Papers

Computer vision technologies have made impressive progress in recent years, but often at the expenses of increasingly complex models needing more computational and storage resources. This workshop aims to improve the energy efficiency of computer vision solutions for running on systems with stringent resources, for example, mobile phones, drones, or renewable energy systems. Efficient computer vision can enable many new applications (e.g., wildlife observation) powered by ambient renewable energy (e.g., solar, vibration, and wind). 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. Authors are encouraged to present innovation in any part of the entire systems, such as new hardware components, new algorithms, new methods for system integration, new semiconductor devices, and new computing paradigms. Authors are encouraged to discuss the following issues in their papers: the solution, the data for evaluation, and integration of the system, metrics for evaluation, and comparison with the state-of-the-art. This workshop emphasizes “system-level” solutions with implementations for demonstrations and experiments. Conceptual designs or solutions for individual components without integration into functional systems are discouraged.


All submissions must be in the PDF format. Papers are limited to eight pages, including figures and tables, in the ICCV style. Additional pages containing only cited references are allowed.

Important Dates:

Preliminary Program for the Workshop

TimeSpeakerEvent Title
08:00OrgnizersWelcome from Orgnizers
Invited Speeches, Moderator: Kate Saenko, Boston University
08:05Forrest Iandola, Co-founder and CEO, DeepScale (acquired by Tesla in 2018What have we actually accomplished in five years of efficient neural network research?
8:25Kristen Grauman, Professor, University of TexasAnticipating unobserved maps for active perception
8:45Hee-Jun Park, Architect Automotive System-on-Chip Architect, QualcommLow-power & Low-thermal Vision Processing in Automotive
9:05Carole-Jean Wu, Research Scientist, Facebook AIUnderstanding and optimizing machine learning for the edge
Panel: Challenges and directions of low-power computer vision
Moderator: Yiran Chen, Duke University
  1. Kate Saenko, Boston University
  2. Kurt Keutzer, Berkeley (to be confirmed)>
  3. Song Han, MIT (to be confirmed)
  4. Adam FUKS, NXP (to be confirmed)
10:30Winners present solutions of 2021 low-power computer vision challenge
11:00Selected Papers

Organizers (ordered alphabetically by last names)

NameAffiliationContact Info
Ming-Ching ChangUniversity at Albanymchang2@albany.edu
Shu-Ching ChenFlorida International Universitychens@cs.fiu.edu
Yiran ChenDuke Universityyiran.chen@duke.edu
Callie HaoGeorgia Institute of Technologycallie.hao@gatech.edu
Mark LiaoInstitute of Information Science Academia Sinicaliao@iis.sinica.edu.tw
(contact) Yung-Hsiang Lu, ProfessorPurdue Universityyunglu@purdue.edu
Naveen PurushothamXilinxnpurusho@xilinx.com
Mei-Ling ShyuUniversity of Miamishyu@miami.edu
Joseph SpisakFacebookjspisak@fb.com
George K ThiruvathukalLoyola University Chicagogthiruvathukal@luc.edu
Xuefeng XiaoByteDance Incxiaoxuefeng.ailab@bytedance.com
Wei ZakharovPurdue Universitywzakharov@purdue.edu


Special Interest Group on Design Automation

Organizations interested in sponsoring the workshop, please contact Dr. Yung-Hsiang Lu, yunglu@purdue.edu. Check all sponsors at homepage Sponsors