Bibliographic record
Abstract
Accurate object tracking is a challenging problem due to numerous factors, that may cause the tracker to drift away from the target object. Typically, the output of a tracker is a bounding box (BB); such BB may not well discriminate the object from its background and may not be centered correctly around the object. This paper proposes a method that first detects, at each frame, if a tracker tends to drift by analyzing saliency features of the output BB of a tracker, and then applies automatic seeded object segmentation on the BB to correct the drift once detected. Such segmentation is meant to relocate (recenter) the BB adaptive to the object segmented. As seeds, we propose to use SIFT and salient points conditioned they are non-background pixels. Different than related work, our approach thus models drift external to a base tracker by examining its output BB at each and corrects drift, as needed, by updating that BB adaptive to segmentation. We show the ability of the proposed method to significantly improve the tracking quality of base trackers. We also show that the proposed method outperforms by far segmentation-based trackers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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 source (direct Gemma or distilled Codex), 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".