{"id":"W4226369513","doi":"10.1016/j.patcog.2023.109830","title":"CrowdMLP: Weakly-supervised crowd counting via multi-granularity MLP","year":2023,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph; Memorial University of Newfoundland","funders":"Zhejiang Sci-Tech University; China Postdoctoral Science Foundation; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Granularity; Computer science; Cardinality (data modeling); Pattern recognition (psychology); Artificial intelligence; Feature (linguistics); Data mining; Spatial analysis; Machine learning; Mathematics; Statistics","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":[0.002081984,0.001929964,0.002562773,0.001996164,0.001017559,0.001775752,0.004201929,0.002863412,0.005309852],"category_scores_gemma":[0.006177668,0.00126689,0.001337257,0.001619596,0.0009401548,0.002582611,0.005499421,0.002649664,0.003706388],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009864451,"about_ca_system_score_gemma":0.001524577,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007345111,"about_ca_topic_score_gemma":0.008988278,"domain_scores_codex":[0.9980986,0.0004298181,0.00009419061,0.0006524085,0.000458758,0.0002662532],"domain_scores_gemma":[0.9979377,0.0007941052,0.0001582914,0.0005294603,0.0004096857,0.0001707435],"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":[0.0006611741,0.000325976,0.002044018,0.0002479605,0.0002789971,0.0002635247,0.0001557187,0.2226198,0.01439372,0.005414711,0.01884222,0.7347522],"study_design_scores_gemma":[0.000009292327,0.00002106221,0.0002106192,0.000008539456,0.000009778236,0.00002265528,0.00001078429,0.9942805,0.001777161,0.002781239,0.0008607031,0.000007743812],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009216333,0.0002288226,0.9795544,0.0001644905,0.0001120738,0.00008676102,0.0004099573,0.009021766,0.001205391],"genre_scores_gemma":[0.3304535,0.0002395037,0.6539593,0.0004433177,0.0002910395,0.0004839529,0.002905554,0.001286856,0.009936923],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007345111,"threshold_uncertainty_score":0.0177632,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07720168428992544,"score_gpt":0.3076822966566913,"score_spread":0.2304806123667658,"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."}}