Full papers
Original research contributions with substantial technical depth and experimental results.
Computer Vision/CORE Unranked
SFM-DL 2026
Structure-from-Motion in the Age of Deep Learning ECCV'26 workshop
SFM-DL 2026 is a workshop affiliated with ECCV'26 focused on the intersection of symbolic and neural methods for deep learning, aiming to bridge the gap between symbolic reasoning and deep learning techniques. It provides a forum for researchers to present and discuss novel approaches that integrate structured knowledge with neural architectures.
Paper fit
Contribution paths
A strong submission should clearly identify its contribution and evaluate it appropriately.
Short papers
Preliminary results, position papers, or work in progress with clear research value.
Research areas in scope
01
Topics
Symbolic reasoning in deep learningNeural-symbolic integrationExplainable AI with symbolic methodsKnowledge representation and learningNeural architectures with symbolic componentsReasoning and inference in deep learningSymbolic grounding in neural networksHybrid models for AIApplications of neural-symbolic systems
Policies worth checking twice
- Submissions must be anonymous for double-blind review
- Papers must not exceed 12 pages (including references) for full papers
- Papers must not exceed 6 pages (including references) for short papers
- Dual submission to other conferences or journals is not allowed
- Authors must declare the use of AI tools in the submission
Official sources
Compiled from the official call for papers. The organizers’ pages remain authoritative.
Last verified September 9, 2026