Frame Augmentation for Imbalanced Object Detection Datasets
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
A major challenge in most object detection datasets is class imbal-ance. It is especially apparent in uncurated datasets where framesoriginate from a real-world setup such as a set of cameras col-lecting data from fixed locations. In that case, the dataset classdistribution mirrors the real-world distribution, causing a bias to-wards over-represented classes if used for model training. In thispaper we propose a synthesis technique for balancing the dataset,which exploits having sets of frames from the same camera view.The result is synthesized frames containing only rare objects, whileguaranteeing realistic object placement both in terms of scene con-text and perspective. We train a deep learning object detectionmodel on the augmented dataset and compare its performance toa model trained on the original, imbalanced dataset. Results showthat including the synthesized frames in the training results in asignificant performance boost for the rare classes.
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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 | 0.000 |
| 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 it