{"id":"W4407475891","doi":"10.1109/dicta63115.2024.00107","title":"Attention Based Simple Primitives for Open-World Compositional Zero-Shot Learning","year":2024,"lang":"en","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"","keywords":"Simple (philosophy); Computer science; Zero (linguistics); Shot (pellet); Artificial intelligence; Materials science","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.0004715658,0.0001165201,0.0001183668,0.0001892892,0.0003174478,0.001213118,0.0005017847,0.00002929765,0.0002895445],"category_scores_gemma":[0.00003845015,0.0001096315,0.00009682898,0.0003982696,0.00002959918,0.0008188428,0.0001667118,0.0001582023,0.0001302022],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004772221,"about_ca_system_score_gemma":0.0001062345,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001158044,"about_ca_topic_score_gemma":0.00001029801,"domain_scores_codex":[0.9988469,0.00009085497,0.0002025275,0.0004160129,0.000217467,0.0002261975],"domain_scores_gemma":[0.9991896,0.0004516627,0.00004497227,0.0001502315,0.00008418081,0.00007931973],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00002058967,0.00006435006,0.0004317016,0.00005164579,0.00003106601,0.000008839028,0.0002084072,0.004552082,0.006077098,0.9493399,0.007582505,0.03163177],"study_design_scores_gemma":[0.0004669238,0.00009492126,0.003004433,0.00005462169,0.000006900453,0.000004343045,0.00004137466,0.8091798,0.0008288777,0.007207502,0.1789261,0.0001841904],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0007703212,0.00005775983,0.9650897,0.002308431,0.0002588579,0.0003118847,0.000003761914,0.000413752,0.0307855],"genre_scores_gemma":[0.8122371,0.000001295162,0.1735988,0.001085768,0.00005940082,0.00006653373,0.0001093228,0.00001542205,0.01282642],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9421324,"threshold_uncertainty_score":0.9998237,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05881952603098508,"score_gpt":0.3351968587941862,"score_spread":0.2763773327632011,"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."}}