DVD: Constructing a Discriminative Video Descriptor by Convolving Frame Features
Bibliographic record
Abstract
The core to organize, classify, search, compare and retrieve videos is comparing the video descriptors. In this paper, we propose a Discriminative Video Descriptor (DVD) which is a general way to build the video descriptors on top of various frame features. We built the DVD on top of the HSV-color distribution and evaluated its performance for the Near-Duplicate Video Detection task by using the CC_WEB_VIDEOS dataset. The average detection accuracy achieved 94.4%. We also evaluated the DVD for Human Action Recognition task by building the DVD on top of the 3D-SIFT with Weizmann human action dataset. The average recognition accuracy achieved 97.84%. In practice, the DVD only introduce slightly computational overhead. The average time to build the DVD on top of the HSV-color distribution and 3D-SIFT for a single video was 0.128 s (average 11 frames) and 0.04 s (200 interest points), respectively.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".