Matting with Sequential Pair Selection Using Graph Transduction
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
We are concerned with the natural image matting problem, where the goal is to estimate the partial opacity of a foreground object so that it can be softly segmented from a background. In sampling-based matting techniques, user interactions are first acquired to provide prior information about foreground and background regions. Samples are then chosen from those interactions to calculate the alpha (opacity) value of every pixel in an image. In this research, we propose a new sampling approach that brings relevant samples to every pixel with an unknown alpha value; this yields accurate alpha maps. We also present two new formulations for objective functions used to assess the suitability of the chosen samples. The evaluation of the proposed method, on the alpha matting online benchmark, shows that its performance is close to the state-of-the-art techniques.
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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 | 0.001 |
| 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