A novel robotic visual perception method using object-based attention
Bibliographic record
Abstract
The object-based attention theory has shown that perception processes only select relevant objects of the world which are then represented for action. Thus this paper proposes a novel computational method of robotic visual perception based on the object-based attention mechanism. It involves three modules: pre-attentive processing, attentional selection and perception learning. Visual scene is firstly segmented into discrete proto-objects pre-attentively and the gist of scene is identified as well. The attentional selection module simulates two types of modulation: bottom-up competition and top-down biasing. Bottom-up competition is evaluated by center-surround contrast; Given the task or scene category, the task-relevant object and a task-relevant feature of it is determined based on perception control rules and then used to evaluate top-down biasing. Following attentional selection, the attended object is put into perception learning module to update the existing object representations and perception control rules in long-term memory. An object representation consisting of between-object and within-object codings is built using probabilistic neural networks. An association memory using Bayesian network is also built to model perception control rules. Two types of robotic tasks are used to test this proposed model: task-specific object detection and landmark 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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".