{"id":"W2032266162","doi":"10.5244/c.25.100","title":"Improved Spatio-temporal Salient Feature Detection for Action Recognition","year":2011,"lang":"en","type":"article","venue":"","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Artificial intelligence; Computer science; Affine transformation; Pattern recognition (psychology); Feature (linguistics); Salient; Computer vision; Action recognition; Filter (signal processing); Feature extraction; Motion (physics); Visualization; Transformation (genetics); 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001570791,0.0001115853,0.00008581821,0.0001342649,0.0001853757,0.00008641585,0.0001348887,0.0001049366,0.0001536968],"category_scores_gemma":[0.00001947603,0.0001035668,0.00008695526,0.0001643878,0.00001171254,0.0009252754,0.00002540983,0.00009378316,0.0001028313],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005167819,"about_ca_system_score_gemma":0.00002116067,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009493808,"about_ca_topic_score_gemma":0.0004543784,"domain_scores_codex":[0.999232,0.00003197704,0.0001541898,0.0003042713,0.0001015797,0.000175975],"domain_scores_gemma":[0.9994339,0.0000208469,0.0001165076,0.0001868134,0.0001775597,0.00006439684],"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":[0.0001050003,0.0001469853,0.00005756609,0.00002922741,0.00002389714,9.625969e-7,0.000451331,4.213839e-7,0.01251635,0.0008396207,0.002675028,0.9831536],"study_design_scores_gemma":[0.001239393,0.000819965,0.002227023,0.00002401266,0.00003253244,0.00002532245,0.0001819945,0.05106993,0.8916928,0.03876881,0.01343898,0.0004792425],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0596496,0.000005034396,0.9351282,0.0001641871,0.0009920077,0.0004257387,0.000005232823,0.0003986218,0.003231452],"genre_scores_gemma":[0.9425883,0.000007186767,0.05558771,0.0003936869,0.000204929,0.0001454366,0.00007262861,0.00001109939,0.000989022],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9826744,"threshold_uncertainty_score":0.4223331,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06920335217167513,"score_gpt":0.2621946115222843,"score_spread":0.1929912593506092,"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."}}