{"id":"W4311034190","doi":"10.1145/3550469.3555420","title":"StyleBin: Stylizing Video by Example in Stereo","year":2022,"lang":"en","type":"article","venue":"","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Stylized fact; Computer science; Computer vision; Artificial intelligence; Viewpoints; Set (abstract data type); Stereoscopy; Process (computing); Sequence (biology); Semantics (computer science); Frame (networking); Visualization; Computer graphics (images)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006498533,0.0005820409,0.0004869931,0.0004752265,0.0002746785,0.0006292054,0.0008167089,0.0006362025,0.004196025],"category_scores_gemma":[0.001876306,0.0003378953,0.0005416424,0.0003406438,0.0008290348,0.0008847016,0.00135348,0.0008913692,0.0007659777],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005333905,"about_ca_system_score_gemma":0.0003318312,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001111073,"about_ca_topic_score_gemma":0.001634866,"domain_scores_codex":[0.9995925,0.00008880118,0.00001316289,0.0000852132,0.0001797143,0.00004072071],"domain_scores_gemma":[0.9995607,0.0001569763,0.00005232697,0.0001598954,0.00004180269,0.00002827956],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003049429,0.00009825377,0.0008760989,0.000165223,0.00007369542,0.000336486,0.0003866693,0.4591087,0.111945,0.05770431,0.004927842,0.3640727],"study_design_scores_gemma":[0.00001714824,0.00008111947,0.000228063,0.00001609085,0.00001180029,0.0001970201,0.00002962297,0.9547789,0.02414057,0.0137303,0.006749166,0.00002027006],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01664258,0.0001393892,0.9784429,0.00009640177,0.00003616198,0.00006983718,0.00006508622,0.0008065104,0.003701037],"genre_scores_gemma":[0.5545363,0.0004408883,0.4357183,0.0003003237,0.00008212367,0.0001210782,0.0002506488,0.0004099641,0.008140257],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004196025,"threshold_uncertainty_score":0.01403707,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0149302602315066,"score_gpt":0.2072958558040871,"score_spread":0.1923655955725805,"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."}}