Bibliographic record
Abstract
This paper describes a biologically motivated computational model, termed as early recurrent inhibition, to improve edge representation. The computation borrows the idea from the primate visual system that visual features are calculated in the two main visual pathways with different speeds and thus one can positively affect the other via early recurrent mechanisms. Based on the collected results, we conclude such a recurrent processing from area MT to the ventral layers of the primary visual area (V1) may be at play, and hypothesize that one effect of this recurrent mechanism is that V1 responses to high-spatial frequency edges are suppressed by signals sent from MT, leading to a cleaner edge representation. The operation is modeled as a weighted multiplicative inhibition process. Depending on the weighting methods, two types of inhibition are investigated, namely isotropic and anisotropic inhibition. To evaluate the inhibited edge representation, our model is attached to a contour operator to generate binary contour maps. Using real images, we quantitatively compared contours calculated by our work with those by a well-known biologically motivated model. Results clearly demonstrate that early recurrent inhibition has a positive and consistent influence on edge detection.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".