Transparent composite model for large scale image/video processing
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
This paper aims to tackle theoretical modeling and dimension reduction, two fundamental issues in large scale image/video data processing, together, by proposing a transparent composite model (TCM) for transformed image/video data. Specifically, to handle the heavy tail phenomenon commonly seen in Discrete Cosine Transform (DCT) coefficients of image/video data, a TCM first separates the tail of a sequence of DCT coefficients from the main body of the sequence. Then, a parametric distribution is used to model the main body while a uniform distribution is used to model the tail. Efficient online algorithms for establishing a TCM are proposed and proved to converge exponentially fast, which suits large-scale image/video data processing. It is also demonstrated that a TCM has an inherent non-linear data reduction capability - DCT coefficients of an image in the heavy tail identified by a TCM reveal some unique global features of the image while being insignificant statistically. This, together with its fast convergence, makes the proposed model a desirable choice for modeling DCT coefficients in large-scale image/video applications, such as online quantization design, entropy coding design, and image/video analytics in Big Data.
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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 | 0.002 |
| Open science | 0.001 | 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