{"id":"W4402782556","doi":"10.1007/978-3-031-71602-7_16","title":"VAeViT: Fusing Multi-views for Complete 3D Object Recognition","year":2024,"lang":"en","type":"book-chapter","venue":"Lecture notes in computer science","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Concordia University","funders":"","keywords":"Computer science; Artificial intelligence; Object (grammar); Computer vision; Cognitive neuroscience of visual object recognition","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":[0.0007630381,0.001987443,0.001824199,0.001671066,0.0003774824,0.001697755,0.002570554,0.001811186,0.01366516],"category_scores_gemma":[0.001183383,0.001372554,0.001851025,0.001798482,0.0005143589,0.002458264,0.004054392,0.001602588,0.007498434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004098737,"about_ca_system_score_gemma":0.0005003399,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002477003,"about_ca_topic_score_gemma":0.003972713,"domain_scores_codex":[0.9991062,0.00008230069,0.00004218137,0.0002210641,0.0004580389,0.00009017068],"domain_scores_gemma":[0.999552,0.0001333535,0.00002770059,0.0001561971,0.0001002896,0.00003047606],"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":[0.0001955507,0.0000528806,0.0002305357,0.0002212628,0.0001544028,0.0001513413,0.00008404124,0.01260237,0.04727491,0.004652599,0.02548855,0.9088916],"study_design_scores_gemma":[0.00004430244,0.0001939613,0.001236177,0.0001354514,0.0001251349,0.001305584,0.0001112504,0.8019735,0.09543599,0.02758124,0.07175521,0.0001021596],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.002091022,0.0007713627,0.9844147,0.00005030079,0.0001101768,0.00004373753,0.0004907178,0.01046075,0.001567222],"genre_scores_gemma":[0.04139896,0.0008970121,0.9465898,0.0002428727,0.00006758155,0.0001403065,0.002647994,0.001866802,0.006148681],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01366516,"threshold_uncertainty_score":0.04571456,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0657369932955668,"score_gpt":0.2700905481321496,"score_spread":0.2043535548365828,"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."}}