{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001900149,0.0008023584,0.0007943536,0.0004128891,0.0007666381,0.0005175594,0.00206949,0.000641923,0.00003294092],"category_scores_gemma":[0.0001132631,0.0007331702,0.0003885674,0.002112204,0.0005137311,0.0009121424,0.0008321676,0.001735909,0.00001926169],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001161495,"about_ca_system_score_gemma":0.0006842729,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00104112,"about_ca_topic_score_gemma":0.004983075,"domain_scores_codex":[0.9934025,0.002434786,0.0005937935,0.002228973,0.0003519162,0.0009879697],"domain_scores_gemma":[0.9960257,0.0009792118,0.0008040732,0.001623632,0.0003712164,0.0001961104],"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":[0.0007505605,0.0002359922,0.01239716,0.00004661316,0.0001833265,0.0003952975,0.0008905045,0.9792906,0.0003893122,0.003008708,0.000002142662,0.002409753],"study_design_scores_gemma":[0.002158924,0.0004734332,0.008110951,0.000212223,0.0001834496,0.00006708455,0.0004484644,0.9848715,0.0003072073,0.002104697,0.0001328313,0.0009292577],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3249725,0.0001289948,0.6716166,0.00003859673,0.002144027,0.0007584774,0.00001106646,0.00005537073,0.0002743333],"genre_scores_gemma":[0.9950732,0.0001117543,0.003915663,0.0001212448,0.0006514918,0.000003003713,0.00002012188,0.00004203243,0.00006146678],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6701007,"threshold_uncertainty_score":0.999512,"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."}}