Automatic Video Intro and Outro Detection on Internet Television
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
Content Delivery Networks aim to deliver multimedia content to end-users with high reliability and speed. However, the transmission costs are very high due to large volume of video data. To cost-effectively deliver bandwidth-intensive video data, content providers have become interested in detection of redundant content that most probably are not of user's interest and then providing options for stopping their delivery. In this work, we target intro and outro (IO) segments of a video which are traditionally duplicated in all episodes of a TV show and most viewers fast-forward to skip them and only watch the main story. Using computationally-efficient features such as silence gaps, blank screen transitions and histogram of shot boundaries, we develop a framework that identifies intro and outro parts of a show. We test the proposed intro/outro detection methods on a large number of videos. Performance analysis shows that our algorithm successfully delineates intro and outro transitions, respectively, by a detection rate of 82% and 76% and an average error of less than 2.06 seconds.
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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 | 0.000 |
| 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