Proceedings of the 1st ACM international conference on Multimedia information retrieval
Bibliographic record
Abstract
Welcome to the 1st International ACM Conference on Multimedia Information Retrieval (MIR2008) held in Vancouver, Canada from October 30-31st, 2008. The goal of MIR is to illuminate new paradigms, theories, and insights in the area of multimedia information retrieval. Topics of special interest included exploration of media archives; interfaces for multimedia exploration; indexing and search of multimedia data: digital life experience analysis and retrieval; video surveillance browsing and retrieval; learning and relevance feedback in multimedia retrieval; and diverse applications in culture, society, and science. In the past decade there have been a wide variety of relatively small workshop activities from several ACM Special Interest Groups (SIGs) which were related to multimedia retrieval. The workshops delved into diverse questions involving digital life and lifebits, video surveillance and analysis of human activity, exploration of media archives, etc. This year we have attempted to cluster the disparate activities to create this conference. Last year, the combined MIR related workshop activities from ACM SIGs had less than 120 full paper submissions. However, this year we received 308 unique paper submissions of which 262 full papers were selected by the organizing committee for the double-blind peer review process. Based upon the peer reviews of the program committee, 21 percent or 56 papers were accepted for the research track of the conference including both oral and poster presentations. Many conferences do not have a best paper award because comparing two (or more) excellent papers could involve the old "apples vs. oranges" questions. Is a great theory paper better than a great application paper? To avoid such subjective issues we decided it would be relevant to take note of the papers which were considered to be excellent or outstanding by the program committee. This means that there could potentially be many excellent papers, each in a different way and that we would not have to select one as the best paper. This year we decided to archive as a citation the papers which received two or more outstanding ratings from the program committee. This was not a competitive process, nor an award, but more of a record of which papers the program committee considered to be exceptional. Furthermore, future generations will be able to look back on this proceedings and study how the papers were perceived by history.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.119 | 0.067 |
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".