{"id":"W2535980383","doi":"10.1109/iccv.2009.5459332","title":"A theory of active object localization","year":2009,"lang":"en","type":"article","venue":"","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Object (grammar); Computer science; Set (abstract data type); Artificial intelligence; Constraint (computer-aided design); Maximization; Imperfect; Viewpoints; Object detection; Computer vision; Cognitive neuroscience of visual object recognition; Algorithm; Pattern recognition (psychology); Theoretical computer science; Mathematics; Mathematical optimization","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00003846824,0.00004727263,0.00006649306,0.00004271591,0.00001149007,0.0000045737,0.00002816329,0.00003312615,0.00006813215],"category_scores_gemma":[0.00001265652,0.00004238828,0.00001968068,0.0001169149,0.000007955494,0.00004242977,0.000001528573,0.00002377097,0.000006493157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000148132,"about_ca_system_score_gemma":0.000004429136,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000002103598,"about_ca_topic_score_gemma":0.000001591701,"domain_scores_codex":[0.9997321,0.00001157731,0.00009039847,0.00004511124,0.000058701,0.0000621014],"domain_scores_gemma":[0.999851,0.00001686865,0.00001064754,0.00007383334,0.00003030685,0.00001731777],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000008379315,0.00001449519,0.00002791305,0.000009112536,0.000009140338,4.716835e-7,0.0001771079,0.915758,0.003516368,0.05389801,0.0002692062,0.02631181],"study_design_scores_gemma":[0.0001791877,0.0000572573,0.001731758,0.00001352097,0.00001057923,8.086781e-7,0.0001132641,0.8920174,0.09625241,0.009242605,0.0002819851,0.00009919221],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01175052,0.00003104007,0.955627,0.00001476421,0.00004747368,0.0000540379,0.000001067419,0.0001118672,0.03236224],"genre_scores_gemma":[0.9991422,0.00001903462,0.0006288743,0.00007494359,0.00001545312,4.547304e-7,0.0000090084,0.000006527819,0.000103461],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9873917,"threshold_uncertainty_score":0.1728544,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007597806942788564,"score_gpt":0.2014262897502176,"score_spread":0.1938284828074291,"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."}}