{"id":"W2951329458","doi":"10.48550/arxiv.1406.2031","title":"Detect What You Can: Detecting and Representing Objects using Holistic Models and Body Parts","year":2014,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":92,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Pascal (unit); Torso; Computer science; Artificial intelligence; Computer vision; Low resolution; Object (grammar); Human body; Representation (politics); Pattern recognition (psychology); High resolution","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":[0.0006125916,0.002061617,0.001420108,0.001737103,0.0003936564,0.001652637,0.002102815,0.002039386,0.002652648],"category_scores_gemma":[0.001446307,0.0008271376,0.00161476,0.001040677,0.0008924328,0.002885967,0.002227145,0.00111489,0.002552452],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007040335,"about_ca_system_score_gemma":0.0005903657,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00556365,"about_ca_topic_score_gemma":0.009731843,"domain_scores_codex":[0.9993774,0.00004969671,0.00001868956,0.0003177444,0.0001451955,0.00009118996],"domain_scores_gemma":[0.9994963,0.0001009855,0.00006149319,0.0001880411,0.0000995532,0.00005348091],"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":[0.0003809965,0.0001647436,0.006699313,0.0004082467,0.0002576194,0.0003605424,0.0002322548,0.04122886,0.09445272,0.004012822,0.02078234,0.8310195],"study_design_scores_gemma":[0.00004099843,0.0004445292,0.01900862,0.0002728157,0.0004559999,0.001707397,0.0004591534,0.8351454,0.07786406,0.02727988,0.03716682,0.0001544063],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07455079,0.002571913,0.905289,0.0005736353,0.0002440478,0.0002084166,0.002140671,0.008316989,0.006104444],"genre_scores_gemma":[0.3629149,0.001898777,0.618215,0.001011363,0.0001372832,0.0002234272,0.006476545,0.0009724425,0.008150187],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00556365,"threshold_uncertainty_score":0.0110625,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1313291188635368,"score_gpt":0.2465769638115365,"score_spread":0.1152478449479997,"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."}}