Proceedings of the 2nd ACM workshop on Multimedia semantics
Bibliographic record
Abstract
It is our great pleasure to welcome you to the Second ACM Workshop on The Many Faces of Multimedia Semantics -- MS'08. This symposium is the incarnation of the four Workshops on Multimedia Semantics, organized by us, and held in Sofia (Bulgaria), Varna (Bulgaria), Pisa (Italy), and Chania (Crete, Greece), and the First ACM Workshop on the Many Faces of Multimedia Semantics, held in conjunction with ACM Multimedia 2007, in Augsburg, Germany. It is a forum for the presentation of research results on leading-edge issues of multimedia semantics, including multimedia ontologies, emergent semantics, folksonomies, multimedia annotation, multimedia web mining, multimedia data integration and fusion, and many other related topics. The call for papers attracted 11 submissions from Asia, Canada, Europe, and the United States. The program committee accepted 8 papers (6 full papers and 2 short papers) that cover a variety of topics, including annotation, video semantics, ontologies, social tagging, subjective semantics, audio tools, and spatio/temporal multimedia queries. In addition, the program includes a keynote speech by Alberto del Bimbo on Learning Ontology Rules for Semantic Video Annotation. We hope that these proceedings will serve as a valuable reference for multimedia semantics researchers and developers.
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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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.009 | 0.012 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.055 | 0.017 |
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".