{"id":"W2130820701","doi":"10.1109/crv.2012.69","title":"Evaluation of Local Spatio-temporal Salient Feature Detectors for Human Action Recognition","year":2012,"lang":"en","type":"article","venue":"","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":25,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Discriminative model; Artificial intelligence; Salient; Detector; Computer science; Pattern recognition (psychology); Computer vision; Motion (physics); Representation (politics); Feature (linguistics); Action recognition; Feature extraction; Sparse approximation; Motion detection","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":[0.003330612,0.0009194145,0.001141002,0.001257845,0.0002532314,0.0006785616,0.001043236,0.001171231,0.002527255],"category_scores_gemma":[0.008762191,0.00024437,0.0004341793,0.0005022594,0.0003700086,0.001060019,0.0006376502,0.0004460155,0.0006238385],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008024647,"about_ca_system_score_gemma":0.0007200214,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00469878,"about_ca_topic_score_gemma":0.003919153,"domain_scores_codex":[0.9986213,0.0002901779,0.00008493555,0.0002831479,0.0005815728,0.0001389441],"domain_scores_gemma":[0.997043,0.001652712,0.0001455944,0.0001844988,0.0007284642,0.0002457629],"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":[0.00611374,0.001714937,0.01173261,0.0007407403,0.0006795506,0.0003708485,0.0001114516,0.2881628,0.06422637,0.001996406,0.007958761,0.6161917],"study_design_scores_gemma":[0.0001113726,0.0008687832,0.004578657,0.0000111917,0.00006214088,0.0001167472,0.00003716367,0.9825017,0.01100479,0.0003408308,0.0003488679,0.0000178043],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7216158,0.004610349,0.2640743,0.0003919043,0.0003879652,0.0005455075,0.0009394124,0.002490197,0.004944654],"genre_scores_gemma":[0.9420931,0.0005564541,0.05347627,0.00008579113,0.0000670869,0.0001240142,0.001540918,0.00006011262,0.001996392],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.00469878,"threshold_uncertainty_score":0.01761419,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1265306666725108,"score_gpt":0.3480644755262002,"score_spread":0.2215338088536894,"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."}}