Full papers
Original research contributions with substantial technical depth and experimental validation.
Computer Vision/CORE Unranked
LFSD-CV 2026
International Workshop on Learning from Small Data in Computer Vision
LFSD-CV 2026 is a conference focused on learning from small data in computer vision, addressing challenges in scenarios with limited labeled data, data scarcity, and efficient learning paradigms. It brings together researchers to present innovative methods and applications that enable robust vision systems with minimal data.
Paper fit
Contribution paths
A strong submission should clearly identify its contribution and evaluate it appropriately.
Short papers
Concise presentations of novel ideas, preliminary results, or system demonstrations.
Workshop papers
Extended abstracts or position papers suitable for workshop-style presentation and discussion.
Research areas in scope
01
Topics
Learning from small datasetsData-efficient learningFew-shot and zero-shot learningSelf-supervised and semi-supervised learningData augmentation and synthesisTransfer learning and domain adaptationActive learningWeakly supervised learningMeta-learning for visionEfficient architectures for small dataBenchmarking and evaluation protocols for small dataApplications in medical imaging, robotics, and remote sensing with limited data
Policies worth checking twice
- Submissions must be anonymized for double-blind review.
- Papers must not exceed 8 pages for full papers and 4 pages for short papers, excluding references.
- Dual submission to other conferences or journals is not permitted during review.
- Author lists and affiliations cannot be changed after the submission deadline.
- Authors must include a statement regarding the use of AI tools in the preparation of the paper.
Official sources
Compiled from the official call for papers. The organizers’ pages remain authoritative.
Last verified September 9, 2026