{"id":"W1947746128","doi":"10.1109/cvpr.2015.7298875","title":"Visual recognition by counting instances: A multi-instance cardinality potential kernel","year":2015,"lang":"en","type":"article","venue":"","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":84,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"ENCODE; Computer science; Cardinality (data modeling); Automatic summarization; Event (particle physics); Ambiguity; Kernel (algebra); Artificial intelligence; Activity recognition; Clutter; Internet video; Pattern recognition (psychology); Relation (database); Machine learning; The Internet; Data mining; Mathematics","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":[0.002111927,0.0007060283,0.001856203,0.001051892,0.0004333096,0.001752031,0.002968033,0.001842488,0.001817244],"category_scores_gemma":[0.009248786,0.0005023283,0.001470449,0.001391684,0.00114455,0.004139854,0.002349699,0.00319797,0.0004839697],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001189422,"about_ca_system_score_gemma":0.0007260186,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002887941,"about_ca_topic_score_gemma":0.001752864,"domain_scores_codex":[0.9988307,0.0004161314,0.00007730962,0.0003219066,0.0002281032,0.0001257805],"domain_scores_gemma":[0.9966056,0.001954029,0.0003309887,0.0006276332,0.0003292283,0.000152561],"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":[0.0004518737,0.0002336186,0.003884746,0.0002035073,0.0001995433,0.0002308951,0.0001801299,0.648805,0.006307854,0.05488607,0.004190761,0.2804261],"study_design_scores_gemma":[0.000003442867,0.00001055464,0.0001782207,0.000005115929,0.000005791475,0.00001986536,0.0000057804,0.9899001,0.0003778271,0.009330332,0.0001572419,0.000005751367],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03093128,0.0003656396,0.9669038,0.0004052191,0.00003534829,0.00004247308,0.0001493549,0.0004743335,0.0006925153],"genre_scores_gemma":[0.7772504,0.0004353331,0.218432,0.0002375606,0.0001266358,0.0001472565,0.0007345959,0.0001762301,0.002459929],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002968033,"threshold_uncertainty_score":0.01116908,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05171560923527599,"score_gpt":0.2858088036242283,"score_spread":0.2340931943889523,"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."}}