{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003416517,0.0002323286,0.00025125,0.0001615269,0.0005628988,0.00007184585,0.0005966887,0.0000586002,0.001397616],"category_scores_gemma":[0.00000604397,0.000248624,0.0001550348,0.0004981267,0.0001609914,0.0001660903,0.0002489093,0.0002415585,0.0002545484],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002236329,"about_ca_system_score_gemma":0.00004582527,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001363416,"about_ca_topic_score_gemma":0.00002746992,"domain_scores_codex":[0.9977997,0.0001305037,0.0005428812,0.0006753845,0.0005881476,0.0002633838],"domain_scores_gemma":[0.9986313,0.0001260273,0.0003486268,0.0007049449,0.00007540289,0.000113699],"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":[0.0001538834,0.0007222189,0.00007934059,0.00001471971,0.00004138498,7.449454e-7,0.001952933,0.04811658,0.05441997,0.005785119,0.03330086,0.8554122],"study_design_scores_gemma":[0.001855095,0.002282996,0.003107465,0.00004492669,0.00008078461,0.00002598615,0.002431661,0.8515781,0.01160899,0.004511729,0.1216526,0.0008196741],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07846099,0.000009722574,0.9140489,0.001705224,0.000310495,0.001772105,0.000146147,0.0001156909,0.003430704],"genre_scores_gemma":[0.9729104,0.00001152428,0.02390956,0.0006019579,0.0001180672,0.0006756018,0.0002987038,0.00003415773,0.001440036],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8944494,"threshold_uncertainty_score":0.9999966,"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."}}