An Adaptive Model for Face Distortion Correction
Why this work is in the frame
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Bibliographic record
Abstract
The age of social media insists on developing devices that are able to capture and share ones' moments with high fidelity. Handheld devices such as smartphones with wide-angle cameras have shown the current trend in mobile photography. Although one can take great delight in a wide field of view through modern cameras, nearby objects or faces may be distorted significantly. Recent works have obtained impressive results in this research area, but there is still a tradeoff between image quality and processing time to consider. This work introduces an adaptive polynomial model that automatically selects faces and performs image distortion correction. Since the photos are processed locally, faces are undistorted, and the background is close to the original state. Unlike other content-aware based methods which rely on heavy computing components and high image resolution, our model is suitable for mobile devices to tackle face distortion issue in selfie photos.
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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