{"id":"W4401017512","doi":"10.1109/tvcg.2024.3434386","title":"FACEMUG: A Multimodal Generative and Fusion Framework for Local Facial Editing","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Visualization and Computer Graphics","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Computer science; Image editing; Artificial intelligence; Feature (linguistics); Generative grammar; Generative model; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001517214,0.0001910581,0.0001617409,0.0002099893,0.0004810141,0.0004479621,0.0001132573,0.0001320325,0.000005424809],"category_scores_gemma":[0.000003153662,0.0001760699,0.00008222501,0.0004079675,0.00008109944,0.0004000485,0.000007118513,0.0001662611,0.000001935811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001601225,"about_ca_system_score_gemma":0.00002964295,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008951857,"about_ca_topic_score_gemma":0.00001091113,"domain_scores_codex":[0.9988655,0.00008754351,0.000205786,0.0004842519,0.0001649019,0.0001919763],"domain_scores_gemma":[0.99941,0.0002394616,0.00003709969,0.0001180653,0.00009763682,0.00009778768],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001837024,0.00006851181,0.000005976614,0.00006039918,0.00007704929,0.000003903896,0.002431784,0.01351025,0.00008484293,0.7357171,0.0001935373,0.2478282],"study_design_scores_gemma":[0.0002305098,0.000188415,0.00002729955,0.00009042199,0.00002178109,0.000007361763,0.00005009163,0.990232,0.002388691,0.005395383,0.001178915,0.0001891465],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001391062,0.0001871065,0.9962558,0.0002139191,0.001459818,0.0002737446,0.0000221794,0.0001903939,0.000005942982],"genre_scores_gemma":[0.9453077,0.00022317,0.05335826,0.0006392411,0.0003822828,0.00004303312,0.000006015032,0.0000180561,0.00002219767],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9767218,"threshold_uncertainty_score":0.717992,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01948815635380958,"score_gpt":0.2846601965708095,"score_spread":0.2651720402169999,"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."}}