{"id":"W4287333308","doi":"10.48550/arxiv.2102.01586","title":"U-LanD: Uncertainty-Driven Video Landmark Detection","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Institutes of Health Research","keywords":"Computer science; Landmark; Artificial intelligence; Margin (machine learning); Key (lock); Computer vision; Frame (networking); Overhead (engineering); Bayesian probability; Bayesian inference; Pattern recognition (psychology); Machine learning","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002452123,0.000276086,0.0003015175,0.0002533586,0.0002336634,0.0003302105,0.001103782,0.0002791917,0.00008677126],"category_scores_gemma":[0.00007034955,0.000327037,0.0002314237,0.0006191102,0.00006018917,0.0003916286,0.001245789,0.0007199307,0.0001194451],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002379991,"about_ca_system_score_gemma":0.0002201686,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003474104,"about_ca_topic_score_gemma":0.0004255743,"domain_scores_codex":[0.998005,0.0002766629,0.0001944926,0.001058519,0.0001306741,0.0003346586],"domain_scores_gemma":[0.9983131,0.0001320051,0.0002386681,0.0009373912,0.0001923889,0.0001864289],"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":[0.00003038351,0.00005928091,0.002974748,0.00005376988,0.0001045904,0.0004949583,0.0007085674,0.9761557,0.0001524708,0.0124117,0.00009778887,0.006756064],"study_design_scores_gemma":[0.0005836672,0.00004201184,0.002822055,0.00008890684,0.00004176293,0.00001237352,0.000231622,0.9873657,0.0001104606,0.003783548,0.004455348,0.0004625626],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1680419,0.00005280156,0.8265387,0.00008969331,0.0008765353,0.0001556033,0.000003173383,0.0003341799,0.003907333],"genre_scores_gemma":[0.9953856,0.0001347139,0.002275313,0.0001797334,0.000095954,0.000001263421,0.00002941641,0.00001695733,0.001881065],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8273436,"threshold_uncertainty_score":0.9999182,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05413472597586622,"score_gpt":0.1857636321285387,"score_spread":0.1316289061526725,"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."}}