Hybrid Unsupervised Scale-invariant Slide Detection (HUSSD) for Video Presentation
Bibliographic record
Abstract
This paper addresses the task of unsupervised scale-invariant slide detection for video presentations, which aims to detect slides and index them with an appropriate location in the presentation video. We propose two new methods that improve on Scale-Invariant Feature Transform (SIFT) in terms of computational cost as well as accuracy. Both methods are instantiations of our Hybrid Unsupervised Scale-invariant Slide Detection paradigm (HUSSD). The first is a feature-based HUSSD algorithm. This method utilizes SIFT as well as Oriented FAST and rotated BRIEF (ORB) tracker to resolve the computation cost issue. Feature-based HUSSD optimizes for speed and substantially improves the runtime to approximately 30 times faster than SIFT at the trivial cost of accuracy falling from 90.06% to 82.89%. Furthermore, our second novel method for slide detection, namely template-based HUSSD, employs a multiscale matching detector and a single scale template matching tracker to improve on SIFT in terms of both speed and accuracy by 7.91 times and to 95.05% respectively.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".