Relevance-based Content Modeling and Object Retrieval from Multi-source Image Data
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
The problem of object retrieval for design automation based on semi-semantic representation of objects of interest in images is addressed in this article. The concept of an ordered set of salient feature vectors (SFVs) is introduced to concisely describe multi-source image data in different application areas. A system architecture is presented which combines statistical learning modules with multi-scale morphological modeling and analysis of image contents. In the presented approach, the object retrieval is based on establishing correspondence between two ordered sets of SFVs: a query reference image (or concise description of the object) and a database image. On a higher level, new rules of association are established between the design objects, based on the extracted SFVs and their spatial relations in images. Experiments with different types of images confirmed the utility of the proposed content modeling and proved the adequacy of the extraction accuracy of the SFVs.
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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 | 0.001 |
| 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