Fast Forward with your VCR: Visualizing Single-Video Viewing Statistics for Navigation and Sharing
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
Online video viewing has seen explosive growth, yet simple tools to facilitate navigation and sharing of the large video space have not kept pace. We propose the use of single-video viewing statistics as the basis for a visualization of video called the View Count Record (VCR). Our novel visualization utilizes variable-sized thumbnails to represent the popularity (or affectiveness) of video intervals, and provides simple mechanisms for fast navigation, informed search, video previews, simple sharing and summarization. The viewing statistics are generated from an individual’s video consumption, or crowd-sourced from many people watching the same video; both provide different scenarios for application (e.g. implicit tagging of interesting events for an individual, and quickly navigating to others’ most-viewed scenes for crowd-sourced). A comparative user study evaluates the effectiveness of the VCR by asking participants to share previously-seen affective parts within videos. Experimental results demonstrate that the VCR outperforms the state-of-the-art in a search task, and has been welcomed as a recommendation tool for clips within videos (using crowd-sourced statistics). It is perceived by participants as effective, intuitive and strongly preferred to current methods.
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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 | 0.001 | 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