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Call for Papers
Call for Papers
Workshop - Tuesday, June 18, 2024
FGVC11 will have two paper tracks including an 8-page CVPR proceedings and a 4-page non-archival track. Papers in both tracks will be reviewed. They will showcase new work, along with applications of fine-grained learning. Submission will be via CMT. Note the different deadlines below.
1) Proceedings track:
1) Proceedings track:
Submitted papers will consist of 8-page full papers following the CVPR24 paper guidelines. These papers will be published with the CVPR proceedings. (Submissions will be given the option to be considered for the 4-page track as well if not accepted to the 8-page track. If you wish to opt in to this, please submit only to the 8-page track.)
Deadline for Submission: March 01, 2024 March 08, 2024
Notification of Acceptance: April 05, 2024
Camera Ready: April 14, 2024
Submission Website -> CMT-FGVC2024
In the Top Left Menu, select FGVC2024 - CVPR Proceedings 8-pages
2) Non-archival track:
2) Non-archival track:
Submitted papers will consist of 4-page extended abstracts, not full papers. These will not be published with the main conference and the timeline is closer to the conference in June.
Deadline for Submission: March 20, 2024 March 27, 2024
Notification of Acceptance: April 20, 2024
Camera Ready: May 05, 2024
Submission Website - CMT-FGVC2024
In the Top Left Menu, select FGVC2024 - Non-archival 4-pages
All deadlines are set for 11:59 pm PT.
The authors guarantee that the submitted paper has not been previously published or accepted for publication in a substantially similar form. CVPR rules regarding plagiarism, double submission, etc. apply.
Registration fee
Registration fee
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.
More information: CVPR Registration
Please email fgvcworkshop@googlegroups.com with any questions.
Scope
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
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
Multi-modal learning
Using audio and video data
Using meta data e.g. geographical priors
Learning shape
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
Fine-grained applications
Product recognition
Animal biometrics and camera traps
Museum collections
Agricultural
Medical
Fashion
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