Proceedings of the 4th Joint Symposium on Computational Aesthetics, Non-Photorealistic Animation and Rendering, and Sketch-Based Interfaces and Modeling
Bibliographic record
Abstract
Expressive is the joint symposium on Computational Aesthetics, Non-Photorealistic Animation and Rendering, and Sketch-Based Interfaces and Modeling. Expressive 2014 is the fourth annual Expressive event and we have returned to Vancouver, where the first Expressive was also held. Expressive's three subconferences have distinct but related agendas. Computational Aesthetics bridges the analytic and synthetic by integrating aspects of computer science, philosophy, psychology, and the fine, applied, and performing arts. CAe seeks to facilitate both the analysis and the augmentation of creative behaviors. Non-Photorealistic Animation and Rendering is concerned with computational techniques for visual communication; such techniques usually generate imagery and motion which is expressive, rather than photorealistic, although possibly including realistic elements. The goal of the Sketch-Based Interfaces and Modeling symposium is to explore the models, algorithms, and technologies needed to enable effective sketch-based interfaces. SBIM investigates novel methods for classification and recognition of hand-drawn shapes, and ways of using these techniques for creating or editing text, mathematics, and 3D shapes.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.079 | 0.013 |
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".