{"id":"W4200602317","doi":"10.1016/j.neucom.2021.12.027","title":"Towards more effective PRM-based crowd counting via a multi-resolution fusion and attention network","year":2021,"lang":"en","type":"article","venue":"Neurocomputing","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":9,"is_retracted":false,"has_abstract":false,"ca_institutions":"Toronto Metropolitan University","funders":"Natural Sciences and Engineering Research Council of Canada; U.S. Department of Agriculture","keywords":"Computer science; Benchmark (surveying); Margin (machine learning); Artificial intelligence; Feature (linguistics); Task (project management); Pattern recognition (psychology); Machine learning; Data mining","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001597857,0.001779412,0.002183553,0.002237954,0.0007954611,0.001413986,0.002312775,0.001801458,0.001961254],"category_scores_gemma":[0.003864733,0.0008837078,0.001107149,0.001732889,0.0005840159,0.002579028,0.003304308,0.001524201,0.00152633],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006183789,"about_ca_system_score_gemma":0.001030973,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006047399,"about_ca_topic_score_gemma":0.005834119,"domain_scores_codex":[0.9986052,0.0002731077,0.0000636706,0.0004805827,0.0003712912,0.0002061448],"domain_scores_gemma":[0.9987733,0.0003707579,0.0001302287,0.0001628596,0.0004753173,0.00008753894],"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":[0.0005491498,0.0004273179,0.00228924,0.0002121751,0.0002254779,0.0002482557,0.0002133994,0.1291456,0.05318296,0.005166133,0.005722867,0.8026173],"study_design_scores_gemma":[0.000006454075,0.00004018488,0.0005814637,0.000009089326,0.00002763846,0.00006859509,0.00002393155,0.9923024,0.00459162,0.001720762,0.0006147302,0.0000131547],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01437676,0.0004390869,0.9822099,0.0001528319,0.000116688,0.00006373264,0.00008310196,0.0009362212,0.001621708],"genre_scores_gemma":[0.4058968,0.0006539131,0.5854835,0.0004768713,0.0003752971,0.00016266,0.000505994,0.0002345643,0.006210308],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006047399,"threshold_uncertainty_score":0.0120244,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01810069708717075,"score_gpt":0.2871323211630011,"score_spread":0.2690316240758304,"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."}}