{"id":"W2950274094","doi":"10.48550/arxiv.1412.1194","title":"Gradient Boundary Histograms for Action Recognition","year":2014,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Histogram; Artificial intelligence; Computer science; Boundary (topology); Pattern recognition (psychology); Action recognition; Action (physics); Computer vision; Motion (physics); Image (mathematics); Mathematics; Physics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002513024,0.0002562882,0.0002461635,0.0003115875,0.0003399964,0.0002129784,0.000629284,0.000286624,0.00004461278],"category_scores_gemma":[0.00002551773,0.0003177518,0.0003067835,0.0002277745,0.00006830884,0.0004863234,0.0003304812,0.0003783994,0.0002263986],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003357176,"about_ca_system_score_gemma":0.000111909,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005730386,"about_ca_topic_score_gemma":0.0000612234,"domain_scores_codex":[0.9983764,0.0001076107,0.000201467,0.0009401356,0.00007918532,0.000295153],"domain_scores_gemma":[0.9985844,0.00008326946,0.0003192559,0.0006223673,0.000252359,0.000138324],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000485779,0.001460404,0.0004548348,0.001776942,0.000754375,0.0001988493,0.001368002,0.02716987,0.0006351409,0.1639698,0.03282496,0.7689011],"study_design_scores_gemma":[0.001123237,0.0002567731,0.0003250132,0.0001991595,0.0001796358,0.00001428128,0.00006764084,0.4564964,0.0007027144,0.4775914,0.06215075,0.0008929725],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1829999,0.00001920992,0.8124424,0.0000976733,0.001925651,0.0004291002,0.00002487126,0.0003716979,0.001689509],"genre_scores_gemma":[0.9947659,0.0001224006,0.002943022,0.0002215324,0.0003074274,0.000009992191,0.0002794722,0.00002043673,0.001329776],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.811766,"threshold_uncertainty_score":0.9999275,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1444959800099551,"score_gpt":0.2107624596502893,"score_spread":0.06626647964033411,"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."}}