{"id":"W2005219154","doi":"10.1109/robot.2010.5509703","title":"Viewpoint detection models for sequential embodied object category recognition","year":2010,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Computer science; Viewpoints; Cognitive neuroscience of visual object recognition; Artificial intelligence; Object (grammar); Object detection; Component (thermodynamics); Embodied cognition; Computer vision; 3D single-object recognition; Pattern recognition (psychology); Machine learning; Visualization","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":[0.0007673337,0.0005373489,0.0005912367,0.0004344278,0.0002752445,0.0006883987,0.0020136,0.0006848635,0.002699128],"category_scores_gemma":[0.00276235,0.0004714572,0.00098318,0.0003475028,0.0006911436,0.001468839,0.0008542463,0.001194238,0.0008078453],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009511046,"about_ca_system_score_gemma":0.0008761161,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006949671,"about_ca_topic_score_gemma":0.008556001,"domain_scores_codex":[0.9995927,0.00008027862,0.0000166926,0.0001320318,0.0001371265,0.00004109598],"domain_scores_gemma":[0.9989952,0.0004565745,0.0001355316,0.000214403,0.0001385679,0.00005968828],"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":[0.0001720227,0.00008798284,0.001864437,0.00009295659,0.00009268289,0.0001535375,0.0003085778,0.6963018,0.01931055,0.04910927,0.001548324,0.2309578],"study_design_scores_gemma":[0.000006147783,0.00003450457,0.0002532713,0.000004067965,0.000008698418,0.000051147,0.00001106566,0.9844094,0.002206256,0.01215657,0.0008467241,0.0000120993],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01015944,0.00007353318,0.9885428,0.00004453166,0.00001327669,0.00001751093,0.0000316484,0.0003080113,0.0008092887],"genre_scores_gemma":[0.5104628,0.0002546472,0.4836552,0.0000872936,0.00002976307,0.0001394099,0.0002331344,0.0001844342,0.004953324],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006949671,"threshold_uncertainty_score":0.01381844,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04398635365548369,"score_gpt":0.2992980365365113,"score_spread":0.2553116828810276,"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."}}