{"id":"W4288346518","doi":"10.48550/arxiv.1905.12759","title":"Distant Pedestrian Detection in the Wild using Single Shot Detector with\\n Deep Convolutional Generative Adversarial Networks","year":2019,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Pedestrian detection; Detector; Computer science; Object detection; Artificial intelligence; Generative adversarial network; Single shot; Convolutional neural network; Deep learning; Generative grammar; Set (abstract data type); Training set; Computer vision; Object (grammar); One shot; Shot (pellet); Generative model; Pedestrian; Pattern recognition (psychology); Adversarial system; Engineering; Telecommunications; Optics","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.001533777,0.001225377,0.0009808985,0.0007433401,0.0003281175,0.0008088131,0.001360476,0.0009961749,0.001454287],"category_scores_gemma":[0.001736314,0.000546596,0.0008668289,0.0003917819,0.0007317021,0.001351807,0.001628074,0.001343645,0.0008067064],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008904032,"about_ca_system_score_gemma":0.0006617691,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004435171,"about_ca_topic_score_gemma":0.006237484,"domain_scores_codex":[0.9992458,0.0001811809,0.00001767997,0.0002966124,0.0001396954,0.0001191039],"domain_scores_gemma":[0.9993547,0.0002864005,0.00004729349,0.0001692891,0.00008673637,0.00005561396],"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":[0.0006248138,0.000361012,0.004833469,0.0001189971,0.000309393,0.0003807739,0.0001404798,0.6414651,0.02825194,0.008702031,0.005381618,0.3094305],"study_design_scores_gemma":[0.000003918119,0.00003943526,0.0003108922,0.000005156714,0.000008854417,0.00004968908,0.000007373314,0.9934412,0.004219872,0.001460529,0.0004470451,0.000005945114],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08382189,0.0005353895,0.9084284,0.0003177707,0.0001830693,0.00007494709,0.0002271771,0.002828812,0.003582545],"genre_scores_gemma":[0.8081437,0.0002776702,0.1835044,0.0004363078,0.00008253412,0.00004903368,0.0008720696,0.0001367903,0.006497442],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004435171,"threshold_uncertainty_score":0.008818746,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.121544899163717,"score_gpt":0.2202736397004998,"score_spread":0.09872874053678278,"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."}}