Proceedings of the 4th symposium on Applied perception in graphics and visualization
Bibliographic record
Abstract
This book contains the proceedings of the Fourth Symposium on Applied Perception in Graphics and Visualization, which was held in Tubingen, Germany on July 25-27, 2007. APGV is an annual event, sponsored by ACM SIGGRAPH, which brings together researchers from the fields of perception, graphics and visualization. The general goals are to use insights from perception to advance the design of methods for visual, auditory and haptic representation, and to use computer graphics to enable perceptual research that would otherwise not be possible. We received 39 full paper submissions for this year's AGPV. Each submission was reviewed by at least three members of the Program Committee, and we decided to accept 17 of these as full papers, to be presented as Oral presentations at the conference (14 as long papers, and 3 as short papers). The Proceedings also include 15 one-page abstracts describing Poster presentations. The posters include summaries of paper submissions that were not accepted for Oral presentation, as well as separate poster submissions. The Oral Papers cover a wide range of topics. We have classified the papers into four categories, corresponding to the sessions: Faces and Animation, Virtual Environments and Space Perception, Rendering and Surfaces I and II, and Images and Displays. For the first time at APGV this year we have a Keynote Speaker, Greg Ward (Dolby Canada), whose talk is entitled Dynamic Range and Visual Perception. Greg Ward is a pioneer in global illumination and high dynamic range imaging, and his work has drawn heavily from and contributed substantially to research on human vision. We also have several other Invited Speakers: Volker Blanz (University of Siegen), Oliver Bimber (University of Weimar), Philip Dutre (University of Leuven) and Rafal Mantiuk (Max Planck Institute for Computer Science in Saarbrucken).
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.116 | 0.040 |
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".