{"id":"W4410719651","doi":"10.1007/978-3-031-91838-4_4","title":"IPAdapter-Instruct: Resolving Ambiguity in Image-Based Conditioning Using Instruct Prompts","year":2025,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"Membrane Reactor Technologies (Canada)","funders":"","keywords":"Computer science; Ambiguity; Conditioning; Image (mathematics); Computer vision; Artificial intelligence; Computer graphics (images); Human–computer interaction; Multimedia; Programming language; Mathematics; Statistics","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":[0.0003309986,0.0008542959,0.0004594252,0.0001886555,0.0002696444,0.0009097015,0.001416277,0.0007950591,0.06620277],"category_scores_gemma":[0.001748991,0.0003521493,0.0002795599,0.0001761549,0.0005497102,0.00163216,0.001284147,0.001454854,0.01164689],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002078135,"about_ca_system_score_gemma":0.0003487387,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004138894,"about_ca_topic_score_gemma":0.0005188188,"domain_scores_codex":[0.9998525,0.00002631833,0.000007071886,0.00004222621,0.00005026082,0.00002159624],"domain_scores_gemma":[0.9995603,0.0002759329,0.00002420833,0.00005354752,0.00004452635,0.00004146439],"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":[0.001129954,0.0003336575,0.0004355013,0.0004608162,0.0000213546,0.0003813942,0.0004442001,0.00652775,0.09919768,0.04376408,0.05611582,0.7911878],"study_design_scores_gemma":[0.0006316778,0.0009488316,0.002525427,0.0003382542,0.00009612735,0.001626204,0.0002463275,0.2750619,0.3471364,0.1083655,0.2628801,0.0001432816],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01932208,0.0004460215,0.8748346,0.0003079012,0.0004649081,0.0002497596,0.0005884397,0.04796761,0.05581867],"genre_scores_gemma":[0.3064142,0.000775568,0.5991657,0.0007618472,0.0002017155,0.0005475801,0.001435212,0.009223722,0.08147444],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.06620277,"threshold_uncertainty_score":0.2214703,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02204090998066032,"score_gpt":0.2778565481734686,"score_spread":0.2558156381928082,"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."}}