{"id":"W4213150508","doi":"10.1109/wacv51458.2022.00203","title":"VCSeg: Virtual Camera Adaptation for Road Segmentation","year":2022,"lang":"en","type":"article","venue":"2022 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Artificial intelligence; Computer science; Computer vision; Segmentation; Mean-shift; Generalization; Camera auto-calibration; Camera resectioning; Image segmentation; Domain (mathematical analysis); 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009288604,0.001832852,0.001215452,0.001233646,0.0006634535,0.001062166,0.002906236,0.001970458,0.005967201],"category_scores_gemma":[0.002642143,0.0007484404,0.001250767,0.001286903,0.0008594029,0.001774353,0.001833194,0.002304121,0.002387896],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000964475,"about_ca_system_score_gemma":0.001338763,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0144666,"about_ca_topic_score_gemma":0.02371096,"domain_scores_codex":[0.9992749,0.0001283341,0.00002183194,0.0003363628,0.000139203,0.00009933609],"domain_scores_gemma":[0.999255,0.0001914801,0.00005456667,0.0002617969,0.0001879767,0.0000491739],"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":[0.0001948916,0.0002071984,0.001573079,0.0001810386,0.0001756451,0.0001771392,0.0001711675,0.4083821,0.0170465,0.004835153,0.02072311,0.546333],"study_design_scores_gemma":[0.00001051528,0.00003621198,0.0003593742,0.000008815217,0.000009168211,0.00006790517,0.00002238145,0.9891789,0.004705646,0.002222003,0.003367714,0.00001149024],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02710598,0.0005876476,0.95433,0.000211996,0.0001524427,0.0001496119,0.0005235458,0.01323498,0.003703835],"genre_scores_gemma":[0.2671342,0.0003244557,0.7212139,0.00038123,0.00009951431,0.0002290304,0.0031136,0.001359347,0.006144684],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0144666,"threshold_uncertainty_score":0.02876484,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01936086054548443,"score_gpt":0.2752015544924593,"score_spread":0.2558406939469749,"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."}}