{"id":"W4413147183","doi":"10.1109/cvpr52734.2025.01412","title":"DEIM: DETR with Improved Matching for Fast Convergence","year":2025,"lang":"en","type":"article","venue":"","topic":"Neural Networks and Applications","field":"Computer Science","cited_by":162,"is_retracted":false,"has_abstract":true,"ca_institutions":"Innovation Cluster (Canada)","funders":"Hefei Normal University","keywords":"Convergence (economics); Matching (statistics); Computer science; Mathematics; Statistics","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":[0.001370633,0.001360102,0.0008313749,0.000699088,0.0003875856,0.001250798,0.003209586,0.001268174,0.006107679],"category_scores_gemma":[0.005224362,0.0006466848,0.0007618567,0.0005522323,0.0005537909,0.001971558,0.002721872,0.002447472,0.003627213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007870653,"about_ca_system_score_gemma":0.001477718,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003942966,"about_ca_topic_score_gemma":0.007881952,"domain_scores_codex":[0.9994067,0.00008579952,0.00002855145,0.0001505349,0.0002373695,0.00009107452],"domain_scores_gemma":[0.9992387,0.0002277858,0.00005448435,0.0002073379,0.0002081236,0.00006359626],"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":[0.0004501795,0.0002922379,0.003421571,0.0002140227,0.0001399761,0.0001931606,0.0001463452,0.3291708,0.02844462,0.01495399,0.02798373,0.5945894],"study_design_scores_gemma":[0.00001718183,0.00004944751,0.0001418785,0.000008143554,0.000006531745,0.0000631782,0.00001168884,0.9867286,0.007098814,0.002778073,0.003087578,0.000008958148],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0137764,0.0002303284,0.97284,0.0002070208,0.0001039953,0.00007052624,0.0002152481,0.009714839,0.002841695],"genre_scores_gemma":[0.2351671,0.0002058535,0.7502605,0.0008683163,0.0000742909,0.0002814484,0.00220008,0.002005662,0.008936822],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006107679,"threshold_uncertainty_score":0.02043223,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007133822222443246,"score_gpt":0.2442538496037568,"score_spread":0.2371200273813136,"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."}}