Class-Based Styling: Real-time Localized Style Transfer with Semantic\n Segmentation
Bibliographic record
Abstract
We propose a Class-Based Styling method (CBS) that can map different styles\nfor different object classes in real-time. CBS achieves real-time performance\nby carrying out two steps simultaneously. While a semantic segmentation method\nis used to obtain the mask of each object class in a video frame, a styling\nmethod is used to style that frame globally. Then an object class can be styled\nby combining the segmentation mask and the styled image. The user can also\nselect multiple styles so that different object classes can have different\nstyles in a single frame. For semantic segmentation, we leverage DABNet that\nachieves high accuracy, yet only has 0.76 million parameters and runs at 104\nFPS. For the style transfer step, we use a popular real-time method proposed by\nJohnson et al. [7]. We evaluated CBS on a video of the CityScapes dataset and\nobserved high-quality localized style transfer results for different object\nclasses and real-time performance.\n
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".