VideoWhiz: Non-Linear Interactive Overviews for Recipe Videos
Bibliographic record
Abstract
With millions of recipe videos increasingly available online, viewers often face the challenge of browsing through these videos and deciding among different styles of recipe demonstrations and instructions. Although state-of-the-art video summarization techniques using linear presentation formats have been shown to be effective in domains such as surveillance, sports or lecture videos, recipe videos are often more complex and may require a different summarization approach. We first investigated how viewers navigate recipe videos and what information they look for when seeking quick overviews of such videos. Based on our findings, we designed VideoWhiz, a novel interactive video summarization tool that provides a non-linear overview design allowing easy access to the key stages or milestones within the recipe and inter-milestone relationships. VideoWhiz uses a combination of computer vision techniques and an annotation workflow to generate these interactive overviews. Our evaluation showed that viewers found VideoWhiz to be effective and useful in providing quick overviews of recipe videos. We discuss the potential for future work to investigate non-linear overviews for other types of instructional videos and to explore more powerful representations for video summarization.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 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".