A sharper definition of alignment for Panoptic Quality
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 Panoptic Quality metric, developed by Kirillov et al. in 2019, makes object-level precision, recall and F1 measures available for evaluating image segmentation, and more generally any partitioning task, against a gold standard. Panoptic Quality is based on partial isomorphisms between hypothesized and true segmentations. Kirillov et al. desire that functions defining these one-to-one matchings should be simple, interpretable and effectively computable. They show that for t and h , true and hypothesized segments, the condition stating that there are more correct than wrongly predicted pixels, formalized as I o U ( t , h ) > . 5 or equivalently as | t ∩ h | > . 5 | t ∪ h | has these properties. We show that a weaker function, requiring that more than half of the pixels in the hypothesized segment are in the true segment and vice-versa, formalized as | t ∩ h | > . 5 | t | and | t ∩ h | > . 5 | h | , is not only sufficient but also necessary. With a small proviso, every function defining a partial isomorphism satisfies this condition. We theoretically and empirically compare the two conditions. • We present θ & , a sharper definition of the object alignment in Panoptic Quality. • We provide an extensive theoretical evaluation of both matchings. • We empirically evaluate both matchings on three image segmentation datasets.
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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 | 0.000 |
| Open science | 0.000 | 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