A Decoupled Approach to Illumination-Robust Optical Flow Estimation
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Bibliographic record
Abstract
Despite continuous improvements in optical flow in the last three decades, the ability for optical flow algorithms to handle illumination variation is still an unsolved challenge. To improve the ability to interpret apparent object motion in video containing illumination variation, an illumination-robust optical flow method is designed. This method decouples brightness into reflectance and illumination components using a stochastic technique; reflectance is given higher weight to ensure robustness against illumination, which is suppressed. Illumination experiments using the Middlebury and University of Oulu databases demonstrate the decoupled method's improvement when compared with state-of-the-art. In addition, a novel technique is implemented to visualize optical flow output, which is especially useful to compare different optical flow methods in the absence of the ground truth.
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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 | 0.001 | 0.003 |
| 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