Proceedings of the 1st ACM international workshop on Events in multimedia
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it