Submission
The paper submission deadline is now over. Accepted papers can be found here.
Call for Papers
Deadline for Submission - 25th March 2022 17:59 Pacific Standard Time
Notification of Acceptance - 25th April 2022
Camera Ready - 13th May 2022
Workshop - 19th June 2022
Submission Website - Submission is via CMT: https://cmt3.research.microsoft.com/FGVC2022
For information about whether the workshop will be in-person, virtual, or hybrid please visit the CVPR 2022 website.
Participation
Each workshop paper must be registered under a full, in-person registration type (student registration type is fine). Virtual registrations will not cover a paper submission.
https://cvpr2022.thecvf.com/registration
Please consult the CVPR 2022 website for information regarding health and safety requirements.
Submission and Reviews
We invite submission of 4 page (excluding references) extended abstracts (using the CVPR 2022 template) on topics related to fine-grained recognition. Reviewing of abstract submissions will be double-blind. The purpose of this workshop is not specifically as a venue for publication so much as a place to gather together those in the community working on or interested in FGVC. The workshop proceedings will not appear in the official CVPR 2022 workshop proceedings. Submissions of work which has been previously published, including papers accepted to the main CVPR 2022 conference are allowed.
In the case of previously published work, it is not necessary for the authors to maintain anonymity. Instead, please cite the existing publication in the submitted abstract. These will be reviewed single-blind (much as a journal is reviewed: authors are known to reviewers, reviewers unknown to authors).
Please mail fgvcworkshop@googlegroups.com with any questions.
Scope
The purpose of this workshop is to bring together researchers to explore visual recognition across the continuum between basic level categorization and identification of individuals within a category population. Topics of interest include:
Fine-grained categorization
Novel datasets and data collection strategies for fine-grained categorization
Low/few shot learning
Self-supervised learning
Semi-supervised learning
Transfer-learning
Attribute and part based approaches
Taxonomic prediction
Long-tailed learning
Human-in-the-loop
Fine-grained categorization with humans in the loop
Embedding human experts’ knowledge into computational models
Machine teaching
Interpretable fine-grained models
Multi-modal learning
Using audio and video data
Using meta data e.g. geographical priors
Learning shape
Fine-grained applications
Product recognition
Animal biometrics and camera traps
Museum collections
Agricultural
Medical
Fashion