Leveraging Temporal Data for Automatic Labelling of Static Vehicles
Bibliographic record
Abstract
The development of advanced 3D object detection algorithms for autonomous driving requires a variety of environments to be captured in labelled datasets. While a number of such datasets exist, new ones will continue to be needed to adapt to new domains, sensors and conditions. In this paper, we propose a method to ease the workload of annotators by automatically proposing high-recall labels for static vehicles. We make use of an object detection network pre-trained on an existing dataset to propose detections within a sequence. By determining the location of each frame in a common reference frame, all detections of a static vehicle will share the same location. By averaging these overlapping detections and extending the prediction to all reasonable frames, we generate identical labels for the same object throughout the sequence. We show how our method sequentially refines predictions in order to improve static object recall by over 20% and precision by 7% over an initial set of network proposals.
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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 | 0.000 |
| 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".