Events

Location

  • Seattle

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CVPR 2024

June 17 - 21, 2024

CVPR 2024

Seattle

IEEE / CVF Computer Vision and Pattern Recognition Conference

We look forward to this year's exciting sponsorship and exhibition opportunities, featuring a variety of ways to connect with participants in person.


Workshop

Workshop 01

3rd Workshop on Federal Learning for Computer Vision

The recent trend of migrating computation from the centralized cloud to distributed edge devices is reshaping the landscape of today’s Internet. Distributed machine learning, specifically federated learning (FL), has been envisioned as a key technology for enabling next generation AI at-scale. Moreover, with privacy being a critical concern in data aggregation, FL emerges as a promising solution to such privacy-utility challenges. It pushes the computation towards the consumer’s edge devices, where the data is generated. By exchanging statistical information rather than the original data, the participants perform collaborative learning in a distributed fashion.

Although FL has become an important privacy-preserving paradigm in various machine learning tasks, the potential of FL in computer vision (CV) applications, such as face recognition, person re-identification, and action recognition, is far from being fully exploited. Moreover, FL has rarely been demonstrated effectively in advanced computer vision tasks such as object detection, image segmentation, and video understanding, compared to the traditional centralized training paradigm.

This workshop aims at bringing together researchers and practitioners with common interest in FL for computer vision. This workshop is an attempt at studying the different synergistic relations in this interdisciplinary area. This day-long event will facilitate interaction among students, scholars, and industry professionals from around the world to discuss the future research challenges and opportunities.

Date:
June 17, 2024
Time:
All Day
Sony AI Participants:
Lingjuan Lyu

Publications

Publication 01

FedMef: Towards Memory-efficient Federated Dynamic Pruning

Authors:

Hong Huang, Weiming Zhuang, Chen Chen, Lingjuan Lyu

Publication 02

Hearing Anything Anywhere

Authors:

Ryosuke Sawata, Mason Long Wang*, Samuel Clarke, Ruohan Gao, Shangzhe Wu, Jiajun Wu

Recruiting Information

Recruiting Information

We look forward to working with highly motivated individuals to fill the world with emotion and to pioneer future innovation through dreams and curiosity. With us, you will be welcomed onto diverse, innovative, and creative teams set out to inspire the world.

At this time, the full-time and internship roles previously listed on this page are closed. Please see all other open positions through the links below.

NOTE: For those interested in Japan-based full-time and internship opportunities, please note the following points and benefits

  • Japanese language skills are NOT required, as your work will be conducted in English.
  • Regarding Japan-based internships, please note that they are paid, and that we additionally cover round trip flights, visa expenses, commuting expenses, and accommodation expenses as part of our support package.
  • Regarding Japan-based full-time roles, in addition to your compensation and benefits package, we cover your flight to Japan, shipment of your belongings to Japan, visa expenses, commuting expenses, and more!

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