{"id":"W4293868171","doi":"10.1109/crv55824.2022.00034","title":"TemporalNet: Real-time 2D-3D Video Object Detection","year":2022,"lang":"en","type":"article","venue":"","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Object detection; Frame (networking); Block (permutation group theory); Convolution (computer science); Computer vision; Feature (linguistics); Network architecture; Frame rate; Feature extraction; Pattern recognition (psychology); Artificial neural network","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006370831,0.001938676,0.0008829852,0.001250888,0.0003955976,0.001232869,0.002690487,0.001075655,0.008398733],"category_scores_gemma":[0.001684038,0.0009019996,0.0009498328,0.001049626,0.0003814853,0.002104118,0.001455971,0.0009552913,0.003240568],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001307946,"about_ca_system_score_gemma":0.001245674,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01856469,"about_ca_topic_score_gemma":0.03125184,"domain_scores_codex":[0.9995753,0.00003106644,0.00001480957,0.0001812978,0.0001382188,0.0000592917],"domain_scores_gemma":[0.9997386,0.00005703758,0.00002778107,0.0000590551,0.00008726213,0.00003021986],"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":[0.001021312,0.000390549,0.003405934,0.0005472829,0.0004444832,0.000381343,0.0001001269,0.08718425,0.04482466,0.006086998,0.1250925,0.7305207],"study_design_scores_gemma":[0.00005843766,0.0001437075,0.001215338,0.00003099456,0.00003823717,0.0002293684,0.00003708521,0.9568084,0.02119187,0.003419016,0.01679186,0.00003568402],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04037048,0.00170023,0.8417864,0.000552301,0.0007351997,0.0004417369,0.01094285,0.09532387,0.008146911],"genre_scores_gemma":[0.2448612,0.001021982,0.7085093,0.0006237971,0.0001772492,0.000564831,0.02951337,0.00196075,0.01276759],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01856469,"threshold_uncertainty_score":0.03691328,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01267292955629841,"score_gpt":0.2464527322028261,"score_spread":0.2337798026465276,"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."}}