{"id":"W7160043273","doi":"10.1109/iccv51701.2025.02161","title":"DC-TTA: Divide-and-Conquer Framework for Test-Time Adaptation of Interactive Segmentation","year":2025,"lang":"","type":"article","venue":"","topic":"Multimodal Machine Learning Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Ministry of Science and ICT, South Korea; National Research Foundation of Korea","keywords":"Segmentation; Adaptation (eye); Feature (linguistics); Pattern recognition (psychology); Image segmentation","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":[0.000485939,0.0002666361,0.0003545643,0.000290228,0.0002774164,0.0002151726,0.0005120467,0.0001831184,0.0001423868],"category_scores_gemma":[0.001785362,0.0002756213,0.0001150257,0.0006889032,0.0001381857,0.0006348244,0.0003311285,0.0003376152,0.00005318927],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001078977,"about_ca_system_score_gemma":0.0002088211,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008765281,"about_ca_topic_score_gemma":0.00001707462,"domain_scores_codex":[0.997906,0.0001435726,0.0006905586,0.0007341998,0.0002462137,0.0002794963],"domain_scores_gemma":[0.9928783,0.005502095,0.0004777865,0.0005737782,0.0004849024,0.0000831259],"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":[0.00009392822,0.0004158219,0.005749661,0.0002413717,0.0001364393,2.500819e-7,0.00529271,0.005744482,0.008757288,0.3214874,0.000361326,0.6517193],"study_design_scores_gemma":[0.0007004823,0.0002534197,0.02962432,0.0002701639,0.00010495,0.000001163394,0.0003083951,0.9137884,0.01111874,0.04307138,0.0005173143,0.000241301],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01898878,0.0001045099,0.9697817,0.008146793,0.0002826342,0.001470364,0.00004521979,0.0001016666,0.00107832],"genre_scores_gemma":[0.6089035,0.00001537472,0.3890988,0.0004657365,0.00003654747,0.0002184464,0.00001645444,0.00001086278,0.001234238],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9080439,"threshold_uncertainty_score":0.9999696,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01516478191265817,"score_gpt":0.3258266459429268,"score_spread":0.3106618640302686,"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."}}