On Structuring Multiple Grouping Hypotheses in Generic Object Detection
Bibliographic record
Abstract
A novel contour grouping method was recently proposed for the difficult task of detecting and delineating unexpected multi-part objects of unknown specific shape and appearance in a variety of natural images. This method, in many ways original and unique, was generally able to obtain object-level groups of quite good quality for a variety of objects and images. For each tested image, a number of object-level groups are hypothesized and ranked using a generic multicriteria objective function. Experiments shown that object-level groups most similar to the human ground truth usually rank high. However, no object-level group was obtained with some difficult images. This paper proposes three important improvements to that original method. Firstly, fixed parameters are replaced by adaptive parameters, improving the robustness of the method even for the most difficult images. Secondly, a parallel version of the method is developed to either speedup or scale-up the computation, making the method adaptive to practical space and time constraints. Finally, a further structuring of the object-level groups makes it possible to isolate the interesting ones and determine their number. A comparison with previous results illustrates the significance of the improvements.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".