{"id":"W4283800840","doi":"10.1609/aaai.v36i1.19923","title":"Event-Image Fusion Stereo Using Cross-Modality Feature Propagation","year":2022,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Advanced Memory and Neural Computing","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Ministry of Science and ICT, South Korea; Agency for Defense Development; National Research Foundation of Korea; National Research Foundation","keywords":"Artificial intelligence; Computer vision; Computer science; Feature (linguistics); Stereopsis; Event (particle physics); Visual odometry; Pixel; Pattern recognition (psychology); Motion blur; Image (mathematics); Robot","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":[],"consensus_categories":[],"category_scores_codex":[0.0003488113,0.0001848404,0.0001802577,0.00006996814,0.0004829334,0.0000930718,0.000526504,0.0000487601,0.0001448525],"category_scores_gemma":[0.0001066078,0.0001581181,0.00008992775,0.000428186,0.0001117819,0.0002971214,0.0002776958,0.0005275552,0.000008853898],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001158091,"about_ca_system_score_gemma":0.00002885004,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005333216,"about_ca_topic_score_gemma":0.000001211496,"domain_scores_codex":[0.9987127,0.00001825404,0.0003530111,0.000276141,0.0003747945,0.0002651647],"domain_scores_gemma":[0.9993641,0.00003309799,0.0001894352,0.0001465545,0.0002187447,0.00004808602],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00009037989,0.00004797817,0.0001514021,0.00009817873,0.000007712915,7.865716e-7,0.0005255564,0.03732893,0.9335175,0.01542145,0.00003321746,0.01277698],"study_design_scores_gemma":[0.00002152606,0.00006962835,0.0001167511,0.00007169187,0.000008306461,0.000008064176,0.0004405103,0.2069977,0.7775462,0.01451268,0.00004834342,0.0001585258],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9920717,0.00002087282,0.005032211,0.0002499992,0.00053864,0.000359293,0.00001172225,0.0001334445,0.001582069],"genre_scores_gemma":[0.9991024,0.000005481886,0.0005705921,0.00004868952,0.00008171462,0.00001713932,0.000001446809,0.00002195047,0.0001506386],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1696688,"threshold_uncertainty_score":0.644787,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07048611108253894,"score_gpt":0.3204239899933323,"score_spread":0.2499378789107934,"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."}}