Bibliographic record
Abstract
CRFs model spatial coherence in classical and deep learning computer vision. The most common CRF is called pairwise, as it connects pixel pairs. There are two types of pairwise CRF: sparse and dense. A sparse CRF connects the nearby pixels, leading to a linear number of connections in the image size. A dense CRF connects all pixel pairs, leading to a quadratic number of connections. While dense CRF is a more general model, it is much less efficient than sparse CRF. In fact, only Gaussian edge dense CRF is used in practice, and even then with approximations. We propose a new pairwise CRF, which we call sparse non-local CRF. Like dense CRF, it has non-local connections, and, therefore, it is more general than sparse CRF. Like sparse CRF, the number of connections is linear, and, therefore, our model is efficient. Besides efficiency, another advantage is that our edge weights are unrestricted. We show that our sparse non-local CRF models properties similar to that of Gaussian dense CRF. We also discuss connections to other CRF models. We demonstrate the usefulness of our model on classical and deep learning applications, for two and multiple labels.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".