Re-mapping of visual saliency in overt attention: A particle filter approach for robotic systems
Bibliographic record
Abstract
‘Saliency map’, a scalar, two-dimensional representation of visual saliency, is at the center of almost all of the existing models of visual attention. Attention is directed toward the most salient location in the saliency map. The mechanism of ‘inhibition of return’ (IOR) suppresses the saliency of recently attended location and thereby enabling the shifts of attention toward different locations in the saliency map in order of decreasing saliency. This process performs well as longs as the attention is directed covertly. For overt attention with head movements, which is practically the case in robotic applications, the visual saliency as well as the frame of reference in which the IOR is expressed change after every head movement. These pose a set of computational challenges in implementing attention behavior and IOR. This paper argues that the re-mapping of visual saliency and dynamic shift of IOR emerge naturally in a particle filter based framework of visual attention. Experiments on a real camera head validate the arguments.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".