Multi-modal Scene Understanding Using Probabilistic Models
Bibliographic record
Abstract
First, I would like to especially thank my advisor, Prof. Gerhard Sagerer, for his openminded support, continual encouragement, and fruitful discussions. His way of mediating experiences beyond technical aspects of research was very inspiring for me. I enjoyed many joint conference visits with him and the cooperative organization of the workshop “Integration of Speech and Image Understanding ” which was associated with the International Conference of Computer Vision 1999. Secondly, I very much acknowledge Prof. Sven Dickinson from the University of Toronto for being the second reviewer in my thesis committee. His comments encourage me to continue research in this area. I have to thank Enno Ohlebusch for his offhanded readiness to substitute Prof. Dickinson during my defence who had to cancel his flight because of the terrorist attack happening in New York, Sept. 2001. The present thesis was embedded in the Collaborative Research Center 360 “Situated Artificial Communicators ” that has been funded by the German Research Foundation. I joined the research project “Interaktion sprachlicher und visueller Informationsverarbeitung” (Interaction of speech and image processing) for three exciting and inspiring
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".