{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008674836,0.001300443,0.001067772,0.003323511,0.0005116286,0.001253173,0.001145537,0.0006042747,0.00281034],"category_scores_gemma":[0.003189287,0.0002665783,0.0007470901,0.001818114,0.0002971851,0.001638614,0.001095462,0.0007140675,0.001318937],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000629854,"about_ca_system_score_gemma":0.0005754777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00297472,"about_ca_topic_score_gemma":0.003269736,"domain_scores_codex":[0.9990315,0.0001316871,0.00007776814,0.0003071943,0.000352284,0.0000995463],"domain_scores_gemma":[0.9982873,0.0003093459,0.000185467,0.0001568427,0.0009823045,0.00007885902],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004297494,0.0001151608,0.0009414813,0.0004123797,0.00009261732,0.0001657443,0.0002649153,0.009691875,0.07744767,0.002396545,0.008457908,0.899584],"study_design_scores_gemma":[0.0001290704,0.001376466,0.01597473,0.0001170113,0.0004384238,0.001037409,0.0006496911,0.724071,0.2070919,0.01054071,0.03842187,0.0001518808],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02868538,0.001567171,0.9614403,0.00015713,0.0001880019,0.0003454611,0.0006629456,0.004478228,0.002475465],"genre_scores_gemma":[0.2968,0.001086567,0.6896783,0.0001592876,0.0003419986,0.000355367,0.004164517,0.0004395782,0.006974556],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.003323511,"threshold_uncertainty_score":0.0094015,"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."}}