{"id":"W2609396050","doi":"10.1109/icpr.2016.7899761","title":"Shannon information based adaptive sampling for action recognition","year":2016,"lang":"en","type":"article","venue":"","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Benchmark (surveying); Computer science; Sampling (signal processing); Adaptive sampling; Action recognition; Pattern recognition (psychology); Grid; Artificial intelligence; Action (physics); Machine learning; State (computer science); Data mining; Mathematics; Algorithm; Statistics; Computer vision","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":[0.0001718514,0.00007396071,0.0000596989,0.000157705,0.0001272399,0.0001005533,0.0001073748,0.00005242014,0.0001309959],"category_scores_gemma":[0.00004736824,0.00005289124,0.00005259335,0.0001136261,0.00000908624,0.003158724,0.00001502411,0.00002929882,0.0004587299],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005930186,"about_ca_system_score_gemma":0.00003433074,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005617017,"about_ca_topic_score_gemma":0.000007229412,"domain_scores_codex":[0.9994186,0.00001966032,0.0001714906,0.000137132,0.0001164055,0.0001367332],"domain_scores_gemma":[0.999379,0.0001401358,0.00009739255,0.0001200385,0.0002218208,0.00004163618],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000273977,0.000016438,0.000004631384,0.000007939613,0.000004665855,5.305879e-8,0.00004011454,0.000005105135,0.002442583,0.003731588,0.0009065875,0.9928129],"study_design_scores_gemma":[0.006326716,0.001251396,0.002340283,0.0004002653,0.00004079801,0.00001725646,0.0002821046,0.2188296,0.4770918,0.1617666,0.1304798,0.001173419],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005251161,0.000001105987,0.9902833,0.0008109769,0.0003124817,0.0002430526,0.00001536341,0.000255908,0.00282668],"genre_scores_gemma":[0.8376074,0.000007324751,0.1603684,0.001327176,0.0001766908,0.000163874,0.00006833257,0.000006714065,0.000274087],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9916395,"threshold_uncertainty_score":0.5896198,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.130598036857029,"score_gpt":0.2978568727494035,"score_spread":0.1672588358923745,"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."}}