Proceedings of the 1st ACM international workshop on Events in multimedia
Bibliographic record
Abstract
It is our great pleasure to welcome you to the 2nd ACM International Workshop on in Multimedia -- EiMM'10. This is the second edition of the EiMM workshop, following the very successful last year's first workshop of this series in Beijing, China, as part of ACM Multimedia 2009. Goal of the workshop is to bring together researchers from the different areas of the multimedia research community that are interested in understanding the concept of events on domain level. It presents work in the areas of domain event modeling, detection of events from multimedia data, processing and composition of events, organization of multimedia data using events as unifying mechanism, and applications of these techniques. In addition, the workshop presents applications that make use of domain-level events in the context of multimedia data. The overall goal and vision of the workshop is to unify the research that deals with the understanding of events and to converge it into a generalized model that serves as a common understanding of events. The call for papers attracted 16 submissions from Europe, Asia/Pacific, United States and Canada, Latin America. The program committee accepted 9 papers that cover a variety of topics, including detection of events from multimedia data, event-based applications, and event models. In addition, the program includes two keynote talks, one by Alan Smeaton on Sensor Nets Discover Search and one by Fausto Giunchiglia on Events as media and knowledge aggregators. We hope that these proceedings will serve as a valuable reference for researchers and developers interested in the understanding of events.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.088 | 0.031 |
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".