{"id":"W2631558258","doi":"","title":"High Definition visual attention based video summarization","year":2015,"lang":"en","type":"article","venue":"Computer Vision Theory and Applications (VISAPP), 2014 International Conference on","topic":"Video Analysis and Summarization","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Toronto Metropolitan University","funders":"","keywords":"Automatic summarization; Artificial intelligence; Computer science; Frame (networking); Key frame; Feature (linguistics); Shot (pellet); Histogram; Computer vision; Construct (python library); Video tracking; Visualization; Pattern recognition (psychology); Reference frame; Histogram of oriented gradients; Block-matching algorithm; Key (lock); Video processing; Image (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":[0.001148026,0.0002558036,0.0002347909,0.0004099643,0.0002631185,0.000669404,0.0007217871,0.0001333372,0.00009659171],"category_scores_gemma":[0.00005300791,0.0002405205,0.00009521951,0.0003821079,0.00009758362,0.0008000597,0.0002433605,0.0001720728,0.0003269605],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008593503,"about_ca_system_score_gemma":0.0001031251,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001283645,"about_ca_topic_score_gemma":0.000004124869,"domain_scores_codex":[0.9976273,0.0003651178,0.00048796,0.0007343406,0.0005782779,0.0002069999],"domain_scores_gemma":[0.9980832,0.0002311677,0.0002941379,0.0004999508,0.0006930773,0.0001985121],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006288715,0.0002101876,0.0001789304,0.000006729091,0.00002579957,0.000001287224,0.00004814953,0.0009718445,0.0005736689,0.9000275,0.001261125,0.09663192],"study_design_scores_gemma":[0.001067225,0.0003676713,0.002503291,0.00008479005,0.0000303951,0.000007895613,0.00004413745,0.7004657,0.0004931638,0.2859828,0.008549125,0.0004038036],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005011483,0.00003131333,0.9883314,0.001909086,0.0004447275,0.0003320147,0.00001662704,0.0001945493,0.003728819],"genre_scores_gemma":[0.9746845,0.00005761256,0.02256364,0.001145144,0.0003755756,0.0001452798,0.0007229291,0.00001781701,0.0002874643],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.969673,"threshold_uncertainty_score":0.980814,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02894511363994841,"score_gpt":0.2952617320729818,"score_spread":0.2663166184330334,"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."}}