{"id":"W4375868851","doi":"10.1109/icassp49357.2023.10094728","title":"Deformable Cross Attention for Learning Optical Flow","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"Fundamental Research Funds for the Central Universities; Natural Science Foundation of Heilongjiang Province; National Natural Science Foundation of China","keywords":"Computer science; Optical flow; Flow (mathematics); Artificial intelligence; Physics; Mechanics","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":[0.0002052557,0.00004673067,0.00005232698,0.00005077172,0.0001953599,0.0001646779,0.00019432,0.00001634348,0.00001275751],"category_scores_gemma":[0.00007038466,0.00003883971,0.00004386964,0.0002476057,0.00001345202,0.00066339,0.0001436512,0.00005839539,0.0003726361],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001199933,"about_ca_system_score_gemma":0.00001010119,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":8.037705e-7,"about_ca_topic_score_gemma":3.188432e-7,"domain_scores_codex":[0.9994041,0.000006133289,0.00009429792,0.0001689117,0.0001004078,0.000226159],"domain_scores_gemma":[0.9997116,0.00005269306,0.0000164939,0.0001245983,0.00004842765,0.00004621788],"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":[0.000005843886,0.00001415659,0.001492363,0.00001735249,0.000004439748,0.000004072188,0.0001158965,0.02474631,0.003247858,0.04796172,0.002350419,0.9200396],"study_design_scores_gemma":[0.0002122785,0.00002608411,0.00194107,0.000005240453,4.975981e-7,0.000002967802,0.00002292195,0.9770124,0.001514979,0.00251279,0.01668683,0.00006197383],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004474942,0.00000483156,0.9898342,0.000561245,0.000222486,0.00006607187,1.753678e-7,0.0005113085,0.004324683],"genre_scores_gemma":[0.1719137,0.00000790706,0.8019426,0.0003216982,0.00005948869,0.0000214266,0.000008101878,0.000009676057,0.02571544],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.952266,"threshold_uncertainty_score":0.4789608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02252279120886944,"score_gpt":0.3351124578698309,"score_spread":0.3125896666609614,"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."}}