Learning to Handle Large Obstructions in Video Frame Interpolation
Bibliographic record
Abstract
Video frame interpolation based on optical flow has made great progress in recent years. Most of the previous studies have focused on improving the quality of clean videos. However, many real-world videos contain large obstructions making the video discontinuous. To address this challenge, we propose our Obstruction Robustness Framework (ORF) that enhances the robustness of existing VFI networks in the face of large obstructions. The ORF contains two components: (1) A feature repair module that first captures ambiguous pixels in the synthetic frame by a region similarity map, then repairs them with a cross-overlap attention module. (2) A data augmentation strategy that enables the network to handle dynamic obstructions without extra data. To the best of our knowledge, this is the first work that explicitly addresses the error caused by large obstructions in video frame interpolation. By using previous state-of-the-art methods as backbones, our method does not only improve the results in original benchmarks but also significantly enhances the interpolation quality for videos with obstructions.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".