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
In recent years, stereo correspondence algorithms based on graph cuts have gained popularity due to the significant improvement in accuracy over the local methods. Even though there has been a noticeable progress in efficient max-flow algorithms, the computational cost for graph cut stereo is still quite heavy, especially if the disparity search range is large. In this paper, we investigate and compare several ways of limiting the disparity search range. We show that the immediately obvious ideas based on thresholding or the hierarchical approach do not work reasonably well. We do, however, find that we can utilise the results of fast local correspondence methods for disparity range reduction of the more expensive graph cuts method. The idea is to understand and exploit the ways in which the local stereo correspondence methods fail. We are able to achieve 2.8 times average speed-up with only a modest degradation in performance, 1.7 % average energy increase. 1
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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 | 0.000 | 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