Feature correspondence with constrained global spatial structures
Bibliographic record
Abstract
In this paper, we consider the feature correspondence task as a graph matching problem. Our approach tends to maximize a similarity objective function, which consists of not only the feature vectors but also their corresponding constrained global spatial structures, by a new polynomial-time approximate optimization algorithm. This algorithm allows every node in a smaller graph to potentially be linked with any node in a larger graph, and thus it can handle one-to-one, many-to-one, and no match cases. Especially, our approach does not necessarily require a training set. We test on the “hotel” and “house” sequences. Matching a pair of frames takes on average 1.24 and 1.22 seconds respectively using a Matlab implementation without any optimization (over an order of magnitude speedup compared to [1]), and with a 2-frame interval our errors are merely 0.07% and 0.09% respectively. Even going up to a 25-frame gap, errors are only 5.66% and 5.00% respectively.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".