{"id":"W4328029477","doi":"10.1111/cgf.14668","title":"A Drone Video Clip Dataset and its Applications in Automated Cinematography","year":2022,"lang":"en","type":"article","venue":"Computer Graphics Forum","topic":"Multimodal Machine Learning Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Korea Creative Content Agency","keywords":"Drone; Computer science; Computer vision; Artificial intelligence; Cinematography; CLIPS; Video capture; Computer graphics (images); Video processing","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":[0.0005271424,0.001835355,0.0007092644,0.00490792,0.000777742,0.0009012755,0.001374846,0.001321875,0.004742176],"category_scores_gemma":[0.002310731,0.0002313597,0.0008689463,0.00259022,0.0003805616,0.001114412,0.00086137,0.0009828649,0.002920147],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008804913,"about_ca_system_score_gemma":0.0005531747,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03238658,"about_ca_topic_score_gemma":0.06142705,"domain_scores_codex":[0.9991527,0.00009982019,0.00007551131,0.0002954285,0.0002704961,0.0001060357],"domain_scores_gemma":[0.9988637,0.0002554863,0.00008301269,0.0003117732,0.0003510398,0.0001350947],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.001462219,0.001661338,0.01713116,0.004207512,0.0005035638,0.002787253,0.000648762,0.02395124,0.03159783,0.002163549,0.5485721,0.3653135],"study_design_scores_gemma":[0.0005587211,0.001145364,0.1663762,0.001015741,0.0003509264,0.004502296,0.003145736,0.3044152,0.05455146,0.002853502,0.4606952,0.0003897618],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.3259149,0.007378282,0.02556112,0.0009551048,0.001311912,0.001789436,0.5951214,0.02517986,0.01678799],"genre_scores_gemma":[0.1473209,0.001145753,0.03426029,0.0001514171,0.0001812371,0.0003403864,0.8128154,0.0003795445,0.003405205],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03238658,"threshold_uncertainty_score":0.06439614,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01088023118937413,"score_gpt":0.2707722317890653,"score_spread":0.2598920005996911,"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."}}