{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003872863,0.00009673315,0.0001251963,0.00007592639,0.0001981935,0.0001121501,0.0006426772,0.00001469805,0.0004256952],"category_scores_gemma":[0.00001149047,0.00009358954,0.00003783028,0.0004430523,0.00001379522,0.0003913211,0.0006524713,0.0001374923,0.00001988962],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006684435,"about_ca_system_score_gemma":0.00002965376,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004975559,"about_ca_topic_score_gemma":0.00005135339,"domain_scores_codex":[0.9987726,0.00020277,0.0001747068,0.0003569297,0.0002275972,0.0002654333],"domain_scores_gemma":[0.9994385,0.0001013352,0.00004179686,0.0003519153,0.00001541271,0.00005103761],"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":[0.00003946569,0.000709775,0.00450271,0.0000159726,0.00007251407,0.000123253,0.003974081,0.0517257,0.04259681,0.07611426,0.5069899,0.3131356],"study_design_scores_gemma":[0.00105794,0.0002597394,0.001651522,0.000007289114,0.000004516824,0.000008890773,0.0008683277,0.374516,0.0109441,0.002372069,0.6076815,0.0006280455],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01067915,0.0002798361,0.9659769,0.001560314,0.0004023982,0.0001810708,0.000005100222,0.0001120646,0.0208032],"genre_scores_gemma":[0.9740753,0.00000642856,0.0217256,0.001976336,0.00003868642,0.000042565,0.000003872597,0.000008003052,0.002123264],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9633961,"threshold_uncertainty_score":0.4661064,"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."}}