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
The widespread use of the internet and affordable video capturing devices has dramatically changed the landscape of video creation and consumption. In particular, user-created videos are more prevalent than ever with the evolution of video streaming services and social networks. The rapid growth of video creation necessitates advanced technologies that enable efficient consumption of desired video content. The scenarios include enhancing user experience for viewers on video streaming services, enabling quick video browsing for video creators who need to go through a massive amount of video rushes, and for security teams who need to monitor surveillance videos. With the broad growth of video capturing devices and applications on the web, it is more demanding to provide desired video content for users efficiently. Video summarization facilitates quickly grasping video content by creating a compact summary of videos. Much effort has been devoted to automatic video summarization, and various problem settings and approaches have been proposed. This monograph provides an overview of this field, and covers early studies as well as recent approaches which take advantage of deep learning techniques. Video summarization approaches and their underlying concepts are described, and benchmarks and evaluations are included. Evaluation techniques in prior work in this field are addressed, and the pros and cons of the evaluation protocols are detailed. The monograph concludes with current and open challenges in this field. This monograph is a useful reference for students and professionals who are active in, or wish to enter into the field of Video Summarization.
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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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