Interaction Techniques for Comparing Video
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
Comparison is a well-studied task in visual analytics, but there is still little support for comparison of temporal streams such as video. There are a wide range of tasks that involve video comparison, but there are very few systems or techniques to support this kind of analysis. To help address this problem, we have developed new interaction techniques that explicitly support video comparison. We provide techniques for equalizing the reference frame of videos to be compared, juxtaposition techniques for enhancing side-by-side and small-multiples comparisons, superposition techniques for comparing overlaid videos, explicit-encoding techniques that visualize differences between extracted points, and temporal-to-linear techniques that translate between a temporal sequence of frames and a 1D timeline. We built a demonstration system with five different datasets, and evaluated our interaction techniques in two ways: an analysis of steps to show their efficiency, and a preliminary user study to explore learnability, utility, and usability.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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 it