Visualization of personal history for video navigation
Bibliographic record
Abstract
We present an investigation of two different visualizations of video history: Video Timeline and Video Tiles. Video Timeline extends the commonly employed list-based visualization for navigation history by applying size to indicate heuristics and occupying the full screen with a two-sided timeline. Video Tiles visualizes history items in a grid-based layout by following pre-defined templates based on items' heuristics and ordering, utilizing screen space more effectively at the expense of a clearer temporal location. The visualizations are compared against the state-of-the-art method (a filmstrip-based visualization), with ten participants tasked with sharing their previously-seen affective intervals. Our study shows that our visualizations are perceived as intuitive and both outperform and are strongly preferred to the current method. Based on these results, Video Timeline and Video Tiles provide an effective addition to video viewers to help manage the growing quantity of video. They provide users with insight into their navigation patterns, allowing them to quickly find previously-seen intervals, leading to efficient clip sharing, simpler authoring and 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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".