Proceedings of the Eighth Eurographics Symposium on Sketch-Based Interfaces and Modeling
Bibliographic record
Abstract
These proceedings contain the papers presented at the eighth instantiation of the annual academic gathering on Sketch-Based Interfaces and Modeling. SBIM was initiated as a workshop in 2004 to provide a unique venue to discuss novel algorithms, interfaces, perspectives, and uses for sketch-based technologies. Since that time, SBIM has become a popular and respected symposium (in 2009) associated with both ACM SIGRGRAPH and Eurographics, and remains the top venue dedicated to the discussion and presentation of 2D or 3D sketch-based academic work representing recognition, interfaces, and modeling research perspective. SBIM is an international conference that alternates between North American and European locations. Last year, SBIM was located in Annecy, France for the second time, in conjunction with the Annecy Animation Film Festival. This year, SBIM is located in Vancouver, Canada for the first time. Additionally, this is the first year that SBIM is a joint and is co-located with SIGGRAPH and is joint with Computational Aesthetics and Non-Photorealistic Animation and Rendering. We had 36 paper submissions this year, which matches the number we had last year. Each paper was double blind reviewed by at least four members of our international program committee and we were able to accept 18 of them for the symposium. As in previous years, the quality of the submitted papers was strong, resulting in a high acceptance rate, a testament to those researchers who work in this field. The papers in these proceedings present a mix of innovative ideas in sketch recognition, 3D modeling, and sketch-based interface usability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.158 | 0.054 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".