{"id":"W2141160399","doi":"10.1109/icra.2011.5980376","title":"Integrating visual exploration and visual search in robotic visual attention: The role of human-robot interaction","year":2011,"lang":"en","type":"article","venue":"","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Visual search; Set (abstract data type); Artificial intelligence; Gaze-contingency paradigm; Robot; Human visual system model; Visual attention; Human–computer interaction; Visualization; Human–robot interaction; Robot vision; Visual perception; Computer vision; Mobile robot; Perception; Image (mathematics); Psychology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008119089,0.000364272,0.0004312573,0.0003419637,0.0003185424,0.001111277,0.0007037139,0.0006426968,0.001003316],"category_scores_gemma":[0.002430275,0.0002382569,0.0004248951,0.0002383178,0.0009011921,0.001961732,0.001200297,0.0005390373,0.0001276766],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005406204,"about_ca_system_score_gemma":0.0005428202,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002914985,"about_ca_topic_score_gemma":0.002084665,"domain_scores_codex":[0.9996725,0.0001135072,0.00001026974,0.00006776763,0.00008331246,0.00005270778],"domain_scores_gemma":[0.9993418,0.0003680819,0.00007220896,0.00006238696,0.00008262929,0.00007287768],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0009374204,0.000363611,0.005807872,0.0004682844,0.0002276191,0.0004583495,0.002471988,0.09720615,0.1793043,0.1173048,0.002747956,0.5927017],"study_design_scores_gemma":[0.00008858068,0.000509149,0.01289293,0.00006123354,0.0001285534,0.0005285428,0.0004457425,0.8249184,0.03124634,0.1232876,0.005790855,0.0001022488],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1541891,0.002292466,0.8265473,0.0008733713,0.00009808032,0.00006905362,0.00002465322,0.0007100289,0.01519598],"genre_scores_gemma":[0.9523689,0.0003209941,0.04600756,0.00007136021,0.00004069291,0.00002777968,0.0000146726,0.00003569145,0.001112408],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002914985,"threshold_uncertainty_score":0.005796015,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06458631816823952,"score_gpt":0.3450341151448488,"score_spread":0.2804477969766093,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}