Bibliographic record
Abstract
Abstract: This paper describes an optimal method of inserting new frames or recovering missing frames in a video sequence. The method is based on an optimization scheme using graph-cuts that finds the ‘optimal ’ frames to be inserted in between two given frames. The core problem is a typical visual correspondence problem between pixels in two or more frames and having formulated the appropriate energy, graph-cuts can be used for optimization. The two frames are assumed to be ‘close ’ and the motion of the objects is small. The motion is seen as a set of two dimensional disparities, and the graph-cuts based optimization is able to find these. Once the disparities are found, an intermediate frame can be trivially placed at an arbitrary position in between the two original frames. The advantage of using graph-cuts instead of the typical techniques used in calculating optical flow lies in the global nature of the graph-cuts optimization. The success of our method is shown with synthetic and real image sequences. We show how the method can be extended to insert multiple frames in between the given two frames. One of the immediate applications is generation of synthetic slow-motion sequences. Key–Words: Video, Frame Synthesis, Energy minimization 1
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".