{"id":"W4283729703","doi":"10.1109/sera54885.2022.9806761","title":"Predicting Episodic Video Memorability using Deep Features Fusion Strategy","year":2022,"lang":"en","type":"article","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Episodic memory; Artificial intelligence; Histogram; Fuse (electrical); Set (abstract data type); Pattern recognition (psychology); Term (time); Image (mathematics); Cognition","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":[0.0003804365,0.0008021218,0.0006091183,0.001199445,0.0001724408,0.0005218964,0.0007816491,0.0005581644,0.0012868],"category_scores_gemma":[0.001643927,0.0001876004,0.0006111664,0.000588436,0.0001989825,0.001041337,0.0006428444,0.0007316449,0.0003570311],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005231043,"about_ca_system_score_gemma":0.0003670006,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006019209,"about_ca_topic_score_gemma":0.006406771,"domain_scores_codex":[0.9997745,0.00002007443,0.00001367617,0.00008932293,0.00005259715,0.00004981708],"domain_scores_gemma":[0.9996074,0.0001344295,0.00007177285,0.00004483157,0.0001039209,0.0000377388],"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":[0.0008552134,0.0005263278,0.02295274,0.0001365552,0.0002310463,0.0003405633,0.0001063108,0.1529537,0.02750601,0.001115591,0.006160113,0.7871158],"study_design_scores_gemma":[0.00001087049,0.0001751561,0.005828565,0.00001053235,0.0000501272,0.00007003856,0.00002389439,0.9850166,0.007426499,0.0008939075,0.000482676,0.00001118089],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7098387,0.002135691,0.2798539,0.0004055612,0.0001471666,0.0001434582,0.001764892,0.002563726,0.003146886],"genre_scores_gemma":[0.9745155,0.0002846874,0.02115063,0.00006772677,0.00004823446,0.00004287456,0.001475034,0.0000291074,0.002386318],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006019209,"threshold_uncertainty_score":0.01196831,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03834183365465828,"score_gpt":0.3061822953502328,"score_spread":0.2678404616955745,"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."}}