Bibliographic record
Abstract
Object trackers are typically ranked by the average of averages, that is, a performance measure averaged over all frames of a video and then averaged over the entire dataset. The average is not a robust estimator. We propose to rank trackers based on robust error norms: we divide the performances of a set of trackers for a video, sorted from best to worst, into outliers (edge trackers) and inliers (trackers with similar performances); we propose an edge-stopping function that assigns the highest score to the highest-performance (top) tracker and scores other trackers accordingly. Our edge-stopping function stops at edge trackers (outliers) using a robust scale defined using the difference (error) between the performances of the top tracker and neighboring trackers. Our method is not a new performance measure but an approach to rank trackers robustly and systematically. We test our methods using five video datasets and 20 trackers. We show that the proposed score is more robust and representative of a tracker’s performance than the widely-used average of averages.
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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 | 0.001 | 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 | 0.000 |
| 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 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".