Bibliographic record
Abstract
The quality of a video clip is considered to be poor if the resolution or the frame rate is low. Video interpolation is thus introduced to enhance video quality and provide a better viewing experience to users. However, there are still some challenges, like the blur caused by motion changes. In this paper, we introduce a dual refinement technique for video interpolation (DRVI). It has three main steps, namely flow refinement, frame synthesis, and Haar refinement. The flow refinement can generate accurate bi-directional flows, which are more suitable for frame interpolation tasks. The Haar refinement uses the Discrete Wavelet Transform (DWT). It can preserve information in different frequency domains and also speed up the learning process. We also add an arbitrary time approximation module to allow multi-frame generation. The number of learnable parameters in our model is much less than existing methods; still, it has excellent performance. Our method is trained on Vimeo90K (Xue et al., 2019) and tested on three well-known datasets to demonstrate its effectiveness.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".