Learning-to-Count by Learning-to-Rank
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
Object counting methods rely on density maps, which are heatmaps produced by placing Gaussian density over object locations. However, density maps are expensive to collect. To reduce the annotation burden, we propose a form of weak supervision that only requires object-based pairwise image rankings. These annotations can be collected rapidly with a single click per image pair and supply a weak signal for object quantity. However, a model learn to fit spurious patterns that satisfy the ranking constraint but do not rely on the objects. To encourage the network to solve the ranking constraints by localizing objects, we propose adversarial density map estimation. This method regularizes a ranking network's intermediate feature representation such that it corresponds to a plausible density map. We demonstrate the effectiveness of our method on several benchmark object counting datasets, and show results with a performance that approaches that of fully-supervised methods using data that can be collected with a fraction of the annotation burden. We release code for reproducibility: github.com/sfu-mial/Rank2Count
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.018 |
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