Paper Submission

Call for Papers

Deadline for Submission - March 20, 2023 17:59 Pacific Standard Time

Notification of Acceptance - April 19, 2023

Camera Ready - Update: May 5, 2023 | May 2, 2023

Workshop - June 18, 2023

Submission Website - https://cmt3.research.microsoft.com/FGVC2023 

For information about whether the workshop will be in-person, virtual, or hybrid please visit the CVPR 2023 website. 

Participation

Each workshop paper must be registered under a full, in-person registration type (student registration is fine). Virtual registrations will not cover a paper submission.
https://cvpr2023.thecvf.com/registration

Submission and Reviews

We invite submission of 4 page (excluding references) extended abstracts (using the CVPR 2023 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 2023 workshop proceedings. Submissions of work which has been previously published, including papers accepted to the main CVPR 2023 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

This workshop aim 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

• Image captioning and generation

• Out-of-distribution detection

• Open-set recognition

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