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Record W2121994777 · doi:10.1109/icpr.1992.201502

A prototype for data-driven visual attention

2003· article· en· W2121994777 on OpenAlexafffund
Sean M. Culhane, John K. Tsotsos

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicVisual Attention and Saliency Detection
Canadian institutionsUniversity of Toronto
FundersOntario Centres of ExcellenceNatural Sciences and Engineering Research Council of CanadaGovernment of CanadaCanadian Institute for Advanced Research
KeywordsComputer scienceHierarchyTask (project management)Artificial intelligenceVisual attentionVisual processingVisualizationVisual searchEnhanced Data Rates for GSM EvolutionHuman–computer interactionComputer visionPerceptionPsychologyEngineering

Abstract

fetched live from OpenAlex

Mounting evidence suggests that attentional mechanisms may be required to successfully perform many vision tasks. The paper presents an attentional prototype for early visual processing. The model is composed of a processing hierarchy and an attention beam that traverses the hierarchy, passing through the regions of greatest interest and inhibiting the regions that are not relevant. The type of input to the prototype is not limited to visual stimuli. Aspects of attention such as localizing spatial regions of interest and ordering their importance are addressed; other aspects of attention such as the role of task guidance are encompassed by the model but are not detailed here. Simulations using high-resolution digitized images were conducted, with oriented edge information as the input to the model. The results confirm that this prototype is both robust and fast, and promises to be essential to any real-time vision system.>

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0000.001
Scholarly communication0.0020.003
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.001

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.

Opus teacher head0.059
GPT teacher head0.346
Teacher spread0.287 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

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".

Quick stats

Citations22
Published2003
Admission routes2
Has abstractyes

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