Binary Networks for Computer Vision

CVPR 2021 Workshop

Covering the latest development of novel methodologies for Binary Neural Networks and their application to Computer Vision. Bringing together a diverse group of researchers working in several related areas.

Workshop Description

An open problem in deep learning is how to develop models which are more and more compact, lightweight and power efficient so that they can be effectively deployed on devices that billions of users use in their everyday life and work like cars, smart-phones, tablets, TVs etc. One of the most prominent methods for achieving all these goals is by training Binary Networks in which both the features and the weights can take only 2 values: +-1. Binarization can result in huge model compression and computational speeds; however, an open problem is how to train binary networks which maintain the same accuracy levels as their real-valued counterparts. Very recent research efforts have shown that training highly accurate Binary Networks is actually feasible opening-up the path of applying the models to a wide variety of Computer Vision problems. This workshop aims to cover both the development of novel methodologies for Binary Neural Networks and their application to Computer Vision, and bring together a diverse group of researchers working in several related areas.

Call for Papers

Authors are welcome to submit full 8-page papers or short 2-page extended abstracts on any of the following topics:

  • Binary Neural Networks (BNNs): New methodologies (optimization and objective functions), and architectures for training.
  • Neural Architecture Search (NAS) for BNNs.
  • BNNs for Computer Vision: image classification, semantic, instance & panoptic segmentation, pose estimation, object detection, 3D vision, and video recognition.
  • BNNs for generative models: GANs, VAE etc.
  • Hardware implementation and on-device deployment of BNNs.
  • New methodologies and architectures for extreme quantisation.
  • Frameworks and bare-metal implementations for binary and low-bit networks.
  • On-device learning.

Important Dates

Paper submission deadline: March 29th, 2021 (11:59pm PST)
Decisions: April 12th, 2021 (11:59pm PST)
Camera ready papers due: April 19th, 2021 (11:59pm PST)
Extended abstract submission: May 17th, 2021 (11:59pm PST)
Extended abstract decisions: May 31th, 2021 (11:59pm PST)
Workshop Date: June ??, 2021

Submission Guidelines

  • Papers included in CVPR proceedings: Submitted (full 8-page) papers must be formatted using the CVPR 2021 template and should adhere to CVPR submission guidelines. The maximum file size for submissions is 50MB. The CMT-based review process will be double-blind. These submissions will be included in the proceedings and must contain new previously unpublished material.
  • Extended abstracts NOT included in CVPR proceedings: We encourage the submission of extended abstracts (2 pages plus references) that summarize previously published or unpublished work. Extended abstracts will undergo a light single-blind review process. Template for extended abstract can be found here.
    • Previously published work: We welcome previously published papers from previous CV/ML conferences including CVPR 2021 which are within the scope of the workshop.
    • Unpublished work: We also encourage the submission of papers that summarize work in-progress. The idea of this type of submission is the dissemination of preliminary results or methods that fall within the overall scope of the workshop.

Please upload submissions at: cmt link will be added soon


To be defined...

Invited Speakers

Diana Marculescu

The University of Texas at Austin

Nicholas Lane

University of Cambridge and Samsung AI


Adrian Bulat

Samsung AI

Zechun Liu


Brais Martinez

Samsung AI

Georgios Tzimiropoulos

QMUL and Samsung AI