Bibliographic record
Abstract
Tremendous strides have been made in image-based scene understanding over the past decade, thanks to larger datasets and enhanced model capacity. However, 3D-based understanding still struggles, in part because 3D data are so costly to annotate. Top 3D instance segmentation models, trained exclusively on 3D data, outperform models using both 2D and 3D data, suggesting untapped potential in merging 2D data to enrich 3D pipelines. Interestingly, while instance segmentation has not yet benefited from 2D-3D data fusion, the sequential fusion of outputs from 2D and 3D models that are trained separately does improve object detection. This thesis applies sequential fusion to instance segmentation and investigates what and where to fuse. We demonstrate that current 2D models do not perform well enough compared to 3D models to enhance instance segmentation results, but that future, higher-performing 2D models should show performance gains using the sequential fusion method.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.003 | 0.001 |
| 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".