Bibliographic record
Abstract
This paper evaluates the performance of different stereo formulations in the context of cluttered scenes with large number of binocular-monocular boundaries (i.e. occlusion boundaries). Three stereo methods employing three different constraints are considered. These are basic (Basic), uniqueness (KZ-uni), and visibility (KZ-vis). Scenes for the experiments are synthetically generated and some are shown to have significantly more occlusion boundaries than the Middlebury scenes. This allows evaluating the methods with different types of scenes to understand the efficacy of different constraints for cluttered scenes. The evaluation considers mislabeled pixels of different types (binocular/monocular) in different regions (on or away from occlusion boundary). We have found that for sparse scenes (fewer occlusion boundaries) all three methods have similar performance. For dense scenes the performance is dominated by pixels on the boundary. For binocular pixels Basic always does better but for monocular pixels KZ-vis has the lowest error. If binary occlusion labeling is considered then the cross-checked version of basic constraint Basic-cc performs best followed by KZ-uni.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".