FGVC8

Call for Papers

Deadline for Submission - 8th April 2nd April 2021 17:59 Pacific Standard Time

Notification of Acceptance - 7th May 2021

Camera Ready - 28th May 2021

Submission URL - https://cmt3.research.microsoft.com/FGVC2021

Submission and Reviews

We invite submission of 4 page (excluding references) extended abstracts (using the CVPR 2021 format) describing work in the domains suggested above or in closely-related areas. Accepted submissions will be presented as posters online at the workshop. Reviewing of abstract submissions will be double-blind. The purpose of this workshop is not 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 2021 workshop proceedings. Submissions of work which has been previously published, including papers accepted to the main CVPR 2021 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 (object recognition) 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

• Appropriate error metrics for fine-grained categorization

• Low/few shot learning

• Self-supervised learning

• Semi-supervised learning

• Transfer-learning from known to novel subcategories

• Attribute and part based approaches

• Taxonomic predictions

• Addressing long-tailed distributions

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 geographical priors

• Learning shape

Fine-grained applications

• Product recognition

• Animal biometrics and camera traps

• Museum collections

• Agricultural

• Medical

• Fashion