{"id":"W2122604950","doi":"10.1109/wacv.2007.43","title":"Motion Estimation Using a General Purpose Neural Network Simulator for Visual Attention","year":2007,"lang":"en","type":"article","venue":"Proceedings","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Computer science; Visualization; Artificial neural network; Artificial intelligence; Motion (physics); Computer vision; Motion estimation; Rendering (computer graphics); Orientation (vector space); Measure (data warehouse); Tensor (intrinsic definition); Formalism (music); Data mining; Mathematics","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":[0.0006995305,0.0005367783,0.0006310816,0.0004149788,0.0004130904,0.0006252862,0.001771838,0.001615992,0.004683102],"category_scores_gemma":[0.002792745,0.0003704311,0.0005757207,0.0004816093,0.0004584525,0.0007138998,0.0006307816,0.001180385,0.0004696697],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001275468,"about_ca_system_score_gemma":0.001285855,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01585502,"about_ca_topic_score_gemma":0.01310747,"domain_scores_codex":[0.9998538,0.00004092464,0.000008195761,0.00003147607,0.00004606154,0.00001952937],"domain_scores_gemma":[0.9992471,0.0004345134,0.00004820949,0.00006121949,0.0001695973,0.00003932261],"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":[0.00006772855,0.00003280731,0.0004987674,0.0000242234,0.00001826434,0.00004047899,0.00002382032,0.9897906,0.001870951,0.002714246,0.0003414052,0.004576644],"study_design_scores_gemma":[0.000006286715,0.00000586763,0.00003025605,6.008103e-7,0.000001395102,0.0000026024,0.000001080028,0.9993698,0.0001933736,0.0003239218,0.00006344303,0.000001391557],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1613206,0.0001185917,0.8284797,0.0003831082,0.00009478005,0.0002725066,0.000591732,0.002373511,0.006365439],"genre_scores_gemma":[0.8061432,0.0001059467,0.1880231,0.0001138563,0.00002548319,0.000567226,0.0004657884,0.0001986052,0.004356998],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01585502,"threshold_uncertainty_score":0.03152543,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02405596760081805,"score_gpt":0.3368182554394988,"score_spread":0.3127622878386808,"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."}}