Bibliographic record
Abstract
In this paper, we propose to perform shape recognition on a Riemannian manifold. Shape representation on a manifold have the advantage to be intrinsically invariant to shape preserving transformation, such as scaling and translation. Also, shape distance can be naturally computed because Riemannian manifolds are metric spaces. We propose to use the square-root velocity manifold (SRV), which model the shape external contour as a unit-length curve. We detail a dynamic programming algorithm for curve alignment w.r.t. parameterization, which respects the unit-length constraint. Then, we increase the robustness of the SRV representation to shape deformations with additional features. In order to be resilient to occlusion, the distance between two curves is performed in two steps. First the curves are aligned and the less matching parts are removed; then the resulting curves are aligned and the distance is evaluated. Finally, a support vector machine classifier is trained based on the pairwise shape distance for a robust recognition. Promising results are obtained using state-of-the-art benchmarks.
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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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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".