Safely caching HOG pyramid feature levels, to speed up facial landmark detection
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
<p>This thesis presents an algorithm for improving the execution time of existing Histogram of Oriented Gradients (HOG) pyramid analysis based facial landmark detection. It extends the work of [1] to video data. A Bayesian Network (Bayes Net) is used as a policy network to determine when previously calculated features can be safely reused. This avoids the problem of recalculating expensive features every frame. The algorithm leverages a set of lightweight features to minimize additional overhead. Additionally, it takes advantage of the wide spread adoption of H.264 encoding in consumer grade recording devices, to acquire cheap motions vectors. Experimental results on a difficult real world data set show that policy network is effective, and that the error introduced to the system remains relatively low. A large performance benefit is realized due to the use of the cached features.</p>
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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 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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