Wide Separate 3D Convolution for Video Super Resolution
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
Video super-resolution (VSR) aims to recover realistic high-resolution (HR) frame from its corresponding center low-resolution (LR) frame and some neighbouring supporting frames. To utilize the extra temporal information of supporting LR frames, most of VSR methods highly rely on accurate motion estimation and compensation models to align LR frames. However, the motions between frames have no ground truth, and it is difficult to train motion estimation and compensation models. Inaccurate results will lead to artifacts and blurs, which also will damage the recovery of high-resolution frames. We propose an effective separate 3D Convolution Neural Network (CNN) with wide activation to overcome the drawback of utilizing motion estimation and compensation models. Separate 3D convolution is factorizing the 3D convolution into 2D convolution along spatial domain and 1D convolution along temporal domain, which can not only capture temporal and spatial information simultaneously but also reduce the computation complexity compared to 3D CNN.
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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 | 0.001 |
| 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