{"id":"W4309630083","doi":"10.1016/j.isprsjprs.2022.10.013","title":"VIBUS: Data-efficient 3D scene parsing with VIewpoint Bottleneck and Uncertainty-Spectrum modeling","year":2022,"lang":"en","type":"article","venue":"ISPRS Journal of Photogrammetry and Remote Sensing","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":19,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University","funders":"Baidu","keywords":"Computer science; Bottleneck; Artificial intelligence; Machine learning; Metric (unit); Parsing; Representation (politics); Benchmark (surveying); Information bottleneck method; Data mining; Cluster analysis","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":[],"consensus_categories":[],"category_scores_codex":[0.0009753839,0.0002321893,0.0004546758,0.0003523731,0.0004231076,0.0001192413,0.000161311,0.00004971048,0.000008079576],"category_scores_gemma":[0.00002805868,0.0001925516,0.00007344352,0.0004544949,0.00005847419,0.00009891862,0.0001430297,0.0006555187,3.110819e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000888623,"about_ca_system_score_gemma":0.00003930989,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002901146,"about_ca_topic_score_gemma":0.00004366524,"domain_scores_codex":[0.99836,0.00008910502,0.0005068479,0.0002722873,0.0004095134,0.0003622852],"domain_scores_gemma":[0.9992452,0.00006198387,0.00015496,0.0002867354,0.00007246073,0.000178687],"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":[0.00004481486,0.00001343729,0.00000622881,0.00004350058,0.0001327975,0.00009988166,0.0003169952,0.812594,0.001509386,8.424805e-7,0.00001027025,0.1852279],"study_design_scores_gemma":[0.0005240061,0.0001105879,0.000002979032,0.0002597391,0.0002268718,0.001353694,0.0012475,0.9951455,0.0004060362,0.0001011722,0.0003727909,0.0002491119],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4434665,0.005268933,0.550765,0.000183236,0.0001700956,0.0000533129,0.000007061879,0.00003925105,0.00004670668],"genre_scores_gemma":[0.9655431,0.0009220918,0.03324675,0.0001038246,0.0001306552,3.192537e-8,0.000007962538,0.00004078146,0.00000483378],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5220766,"threshold_uncertainty_score":0.7852028,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01834941260965555,"score_gpt":0.2250356734510523,"score_spread":0.2066862608413968,"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."}}