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
In this paper, we address the video completion problem for two general cases: 1) filling-in the missing regions of videos captured by a non-stationary camera, and 2) filling-in the missing part of video sequences recorded by a stationary camera. For each case, a novel video completion technique based on the bandlet transform is presented. In the first case, a priority-based exemplar algorithm, which applies the bandlet transform and its generated coefficients along with motion information, is used to fill-in the occluded moving object or the removed region. In the second case, our proposed method is followed by a foreground/background segmentation preprocessing step to generate moving objects and background frames in order to facilitate the video completion task. The technique fills-in the background frames after removing objects by means of a precise optimization in the bandlet transform domain. Then, the occluded part of a moving object is completed by a priority-based algorithm which applies frames' geometry properties using the bandlet transform. Our experimental results indicate that the proposed video completion technique maintains both the spatial and temporal consistency and also demonstrate the effectiveness of the bandlet transform in video completion.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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