{"id":"W2922282711","doi":"10.1109/wacv.2019.00141","title":"Crowd Counting Using Scale-Aware Attention Networks","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":156,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Benchmark (surveying); Computer science; Scale (ratio); Artificial intelligence; Pixel; Image (mathematics); Focus (optics); Computer vision; Pattern recognition (psychology); Geography; Cartography","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":[0.001440933,0.001775948,0.001245731,0.002259304,0.0008002609,0.001263428,0.002101646,0.001538019,0.001809971],"category_scores_gemma":[0.005149214,0.0006791704,0.0009916773,0.001362992,0.001064671,0.002888144,0.002181481,0.001383804,0.0005111797],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002134373,"about_ca_system_score_gemma":0.0008616889,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01425176,"about_ca_topic_score_gemma":0.01220829,"domain_scores_codex":[0.9991919,0.0001911794,0.00003011139,0.000303065,0.0001640673,0.0001196637],"domain_scores_gemma":[0.9986448,0.0006651002,0.0002254293,0.00011222,0.0002622453,0.00009023224],"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":[0.0003084898,0.0001523931,0.005541459,0.0001710169,0.0002008621,0.0003089392,0.0004010094,0.7015617,0.005104461,0.01845613,0.00757528,0.2602182],"study_design_scores_gemma":[0.000005672176,0.0000162513,0.000553944,0.00001586957,0.00002201717,0.00004622046,0.00002525519,0.9871158,0.001007497,0.01022368,0.0009584096,0.000009254351],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06200616,0.001929834,0.9252414,0.001135879,0.0002817746,0.0001341448,0.0003119699,0.001750141,0.007208792],"genre_scores_gemma":[0.8833669,0.001179812,0.1048336,0.0006044616,0.0004695493,0.0001555744,0.0005447956,0.0002243871,0.008620947],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01425176,"threshold_uncertainty_score":0.02833766,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0456069500258801,"score_gpt":0.3181265693827793,"score_spread":0.2725196193568992,"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."}}