{"id":"W7160128938","doi":"10.1109/iccv51701.2025.00583","title":"Learning Large Motion Estimation from Intermediate Representations with a High-Resolution Optical Flow Dataset Featuring Long-Range Dynamic Motion","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Vision and Imaging","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Genome Alberta; National Research Foundation of Korea","keywords":"Optical flow; Motion (physics); Motion estimation; Flow (mathematics); Feature (linguistics); Representation (politics)","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008427106,0.002000018,0.001415001,0.001843081,0.0006312152,0.001261727,0.001492331,0.00243328,0.001327239],"category_scores_gemma":[0.003620251,0.001043532,0.00148368,0.001928833,0.0008222642,0.001975444,0.001113079,0.003594521,0.0009141349],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008124177,"about_ca_system_score_gemma":0.001443866,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0123697,"about_ca_topic_score_gemma":0.01619919,"domain_scores_codex":[0.9994973,0.00006892897,0.0000222827,0.0002185856,0.0001123906,0.00008054348],"domain_scores_gemma":[0.9989061,0.0003855116,0.0001421184,0.0002696899,0.0001884717,0.0001080879],"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":[0.000852962,0.001220204,0.00702976,0.0004692936,0.0004975492,0.0007182605,0.000161497,0.4855277,0.06086011,0.005957341,0.03020316,0.4065022],"study_design_scores_gemma":[0.00003114231,0.00009456035,0.001572846,0.0000225138,0.00002982374,0.0001330179,0.00002391845,0.9883972,0.004657293,0.003657423,0.001354013,0.0000263017],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2219018,0.002553892,0.7598381,0.001221862,0.0004170517,0.0002290454,0.005585763,0.006690395,0.001562144],"genre_scores_gemma":[0.7023042,0.001055942,0.2754341,0.0003764864,0.0002387644,0.0001820898,0.01681387,0.0003385696,0.00325602],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0123697,"threshold_uncertainty_score":0.02459538,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008943064413944815,"score_gpt":0.2928528859149843,"score_spread":0.2839098215010394,"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."}}