{"id":"W2143097569","doi":"10.1109/robio.2007.4522430","title":"A Task-driven Object-based Attention Model for Robots","year":2007,"lang":"en","type":"article","venue":"","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Natural Sciences and Engineering Research Council of Canada; Memorial University of Newfoundland","keywords":"Object (grammar); Artificial intelligence; Computer science; Computer vision; Task (project management); Object detection; Representation (politics); Robot; Segmentation; Mobile robot; Top-down and bottom-up design; Motion (physics); Engineering","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003646774,0.00008806412,0.00008187592,0.0001368957,0.0001373787,0.00007468457,0.0002509895,0.00006244618,0.000007716475],"category_scores_gemma":[0.00001639144,0.00007933092,0.0001164804,0.0002513556,0.00001375936,0.0002872296,0.000033699,0.00005353334,0.00004919768],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004348668,"about_ca_system_score_gemma":0.00003411004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000009514553,"about_ca_topic_score_gemma":0.0000724988,"domain_scores_codex":[0.9990721,0.00001412116,0.0001973399,0.0002839121,0.0001906239,0.0002418664],"domain_scores_gemma":[0.9994968,0.000032385,0.00005749476,0.0002291672,0.0001077083,0.00007640282],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001766083,0.001114416,0.002329244,0.0001296444,0.00006235546,0.00001182889,0.0008071319,0.1883888,0.2342948,0.3524455,0.008939127,0.2113006],"study_design_scores_gemma":[0.0004683695,0.0001017622,0.001859963,0.0000055848,0.000003796367,0.00000209788,0.00001167063,0.9927325,0.003077978,0.001382729,0.0002372754,0.0001162865],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01436365,0.000004099147,0.9827945,0.0003994351,0.0003000008,0.0002333812,9.449402e-7,0.0003082343,0.001595784],"genre_scores_gemma":[0.8638923,3.913761e-7,0.1333639,0.0006021343,0.00003177049,0.00001911446,0.000004592547,0.000006016233,0.002079806],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8495286,"threshold_uncertainty_score":0.3235021,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03243832661806109,"score_gpt":0.3050596195552461,"score_spread":0.272621292937185,"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."}}