Video dissolve and wipe detection via spatio-temporal images of chromatic histogram differences
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
Gradual transitions represent a challenging problem for temporal segmentation of video. Here we present two new features for detecting these. Ngo et al. set out a method for edge detection in spatio-temporal images made out of the central column (or row, or diagonal) of a video. A wipe generates a diagonal edge in such an image. In this paper we make use of all available pixels to generate spatio-temporal images. For each column of the frame (using only the DC values from a video MPEG), we form a 2D histogram based on chromaticity, and then intersect that histogram with that of the previous frame (one or several frames earlier). The result is an image in which cuts and wipes appear as very strong edges, almost 1s in a background of zeroes. Dissolves require another approach; here we extend a color-distance based histogram metric due to Hafner et al. (1995) by applying the method to 2D Cb-Cr histograms and changing the definition so that the the metric displays a near-constant value during a dissolve, and zero elsewhere. We show results on videos that include fast subject motion and camera movements.
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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 | 0.000 |
| 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