Bibliographic record
Abstract
Domain shift limits generalization in many problem domains. For road segmentation, one of the principal causes of domain shift is variation in the geometric camera parameters, which results in misregistration of scene structure between images. To address this issue, we decompose the shift into two components: Between-camera shift and within-camera shift. To handle between-camera shift, we assume that average camera parameters are known or can be estimated and use this knowledge to rectify both source and target domain images to a standard virtual camera model. To handle within-camera shift, we use estimates of road vanishing points to correct for shifts in camera pan and tilt. While this approach improves alignment, it produces gaps in the virtual image that complicates network training. To solve this problem, we introduce a novel projective image completion method that fills these gaps in a plausible way. Using five diverse and challenging road segmentation datasets, we demonstrate that our virtual camera method dramatically improves road segmentation performance when generalizing across cameras, and propose that this be integrated as a standard component of road segmentation systems to improve generalization.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".