A smart video player with content-based fast-forward playback
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
In this paper, we develop a video player to allow the users to do fast-forward playback based on the semantic video content. The whole system has two modules, processing and playing. In the processing part, we present a video time density function (VTDF) to describe the temporal dynamics of video data first. A VTDF-based temporal quantization method is then developed to find the best quanta and partition in the time domain. The optimal quanta are used to extract key frames. The optimal number of key frames is determined by a temporal mean square error (TMSE)-based criterion. In the playing module, we combine the key frame sequence and a set of parameters together and feed them into a triangle-based transition function to generate the sampled frames in a non-uniform way. A built video player will play all sampled frames in its intelligent fast-forward mode for a given fast-forward speed factor. The implementation of video player demonstrates the feasibility of proposed method in practice.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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