{"id":"W4405033076","doi":"10.1109/iccv51701.2025.00974","title":"VISION-XL: High Definition Video Inverse Problem Solver using Latent Image Diffusion Models","year":2025,"lang":"en","type":"preprint","venue":"","topic":"Generative Adversarial Networks and Image Synthesis","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"National Research Foundation of Korea","keywords":"Solver; Problem solver; Computer vision; Computer science; Diffusion; Inverse; Image (mathematics); Computer graphics (images); Artificial intelligence; Inverse problem; Mathematics; Computational science; Geometry; Mathematical analysis; Physics","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0004415655,0.0005222792,0.00055841,0.0002976612,0.0003266427,0.0007330884,0.001121773,0.0003842155,0.0001073224],"category_scores_gemma":[0.00002942832,0.0004503678,0.0003054486,0.0003472666,0.0000800632,0.00111318,0.004562477,0.0005417158,0.00004216791],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002225777,"about_ca_system_score_gemma":0.0003184019,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001469899,"about_ca_topic_score_gemma":0.00008115565,"domain_scores_codex":[0.9968393,0.0002608656,0.0006058646,0.001274319,0.0005306549,0.000489004],"domain_scores_gemma":[0.997827,0.0001221574,0.0003055772,0.0011897,0.0004013583,0.0001542133],"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":[0.00003892185,0.0005741058,0.00004341167,0.000346693,0.0002318989,0.00004979339,0.0007183569,0.9017429,0.01309693,0.05294931,0.01456886,0.01563875],"study_design_scores_gemma":[0.0002959378,0.00003116469,0.0000431821,0.0004538141,0.00006323616,0.000001839966,0.000009754528,0.8722632,0.002407548,0.1238727,0.0001095658,0.0004480428],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003150597,0.00008202263,0.9849393,0.0009326965,0.001176592,0.0006904393,0.00003126134,0.0002863322,0.008710772],"genre_scores_gemma":[0.1129771,0.0002582475,0.8846329,0.0007754979,0.0002111593,0.00004924807,0.00005766307,0.00002410128,0.001014094],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1098265,"threshold_uncertainty_score":0.9997948,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03997770058442579,"score_gpt":0.2576535222003682,"score_spread":0.2176758216159424,"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."}}