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:

For information about whether the workshop will be in-person, virtual, or hybrid please visit the CVPR 2022 website.


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.

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 with any questions.


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


  • 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