Data-driven image stylization using graph-based filtering
Why this work is in the frame
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Bibliographic record
Abstract
In this work, we consider the problem of image abstraction and stylization. A graph-based framework is proposed to render real images into painterly-style ones and create a simple stylized format from color images. The goal is to abstract images by simplifying their visual content while preserving edges and emphasizing most of the perceptually important information. To this end, the low-contrast regions of an image are first smoothened using iterative graph filters in both the vertex and spectral domains. The abstracted luminance channel is quantized and sharpened using an iterative highpass graph filter in the spectral domain. The effectiveness of the proposed graph-based image stylization method is verified through simulations. It is shown that the proposed method can yield significantly better visual quality for stylized images as compared to other existing works.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.001 |
| 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