Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
Bibliographic record
Abstract
It is our great pleasure to welcome you to the 7th ACM SIGMM International Workshop on Information Retrieval -- MIR'05. This year's workshop continues its tradition of being the premier forum for presentation of research results and experience reports on leading edge issues of multimedia information retrieval, including models, systems, applications, and theory. The mission of the workshop is to share novel multimedia information retrieval solutions that fulfill the needs of heterogeneous applications and environments and identify new directions for future research and development. MIR gives researchers and practitioners a unique opportunity to share their perspectives with others interested in the various aspects of multimedia information retrieval.The call for papers attracted 47 submissions to regular sessions from Asia, Europe, South America, Australia, Canada, and the United States. The program committee accepted 21 (10 oral and 11 poster) papers. There are two invited special sessions on Machine Learning for Visual Information Retrieval, and Multimedia Information Retrieval: and Real-world Applications. The workshop program covers a variety of topics, including image/video indexing, annotation, and retrieval, web-based searching and mining, learning techniques, and real world applications. In addition, the program includes a panel on Multimedia Information Retrieval: What is it and Why isn't Anyone Using It? and keynote speeches by Ramesh Sarukkai from Yahoo! Inc. on Video Search: Opportunities & Challenges and Wei-Ying Ma from Microsoft Research Asia on From Relevance to Intelligence: Toward Next Generation Web Search. We hope that these proceedings will serve as a valuable reference for MIR 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 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.082 | 0.057 |
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".