A new player-enabled rapid video navigation method using temporal quantization and repeated weighted boosting search
Why this work is in the frame
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Bibliographic record
Abstract
In this paper, we present a new temporal quantization-based method using repeated weighted boosting search (RWBS) to navigate the video content non-uniformly. In particular, we formulate the rapid video navigation problem as a generic sampling problem. We present a video temporal density function (VTDF) based on the inter-frame mutual information to describe the time density of video activities. A new VTDF based temporal quantization method using RWBS is then applied to find the best quanta and partition in time domain. The video frames that are the nearest neighbors to the quanta in the quantization codebook are sampled to navigate the video. A video player is implemented based on the proposed method to navigate all sampled frames in its intelligent fast-forward mode. The implementation of video player demonstrates the feasibility of proposed method in practice. Experimental results show that the proposed method is effective to capture the important semantic information of video during rapid navigation.
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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 | 0.001 | 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 | 0.000 | 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 it