Flexible spatial models for grouping local image features
Why this work is in the frame
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Bibliographic record
Abstract
A key step for the effective use of local image features (i.e., highly distinctive and robust features) for recognition or image matching is the appropriate grouping of feature matches. Spatial constraints are important in this grouping because, during a recognition process, they allow for the reduction of the number of hypotheses that must be verified and also reduce the number of false positives present in each of these hypotheses. A common choice for this grouping task is to use the Hough transform on the global spatial transformation parameters of the hypothesized matches. Here, instead, we use semi-local spatial constraints which allow for a greater range of shape deformations. A comparison with Hough transform shows that our method is more robust to both rigid and non-rigid deformations. Its functionality is demonstrated in an exemplar-based object recognition system that deals well with severe non-rigid deformations. We also show the efficacy of our flexible spatial grouping far long range motion problems.
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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 | 0.000 | 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 | 0.002 |
| Open science | 0.001 | 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