{"id":"W4300649690","doi":"10.48550/arxiv.1803.05082","title":"Revisiting Salient Object Detection: Simultaneous Detection, Ranking,\\n and Subitizing of Multiple Salient Objects","year":2018,"lang":"","type":"preprint","venue":"arXiv (Cornell University)","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Salient; Representation (politics); Computer science; Object (grammar); Artificial intelligence; Rank (graph theory); Ranking (information retrieval); Object detection; Machine learning; Pattern recognition (psychology); Mathematics","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":[0.001545748,0.001233803,0.001497401,0.001695112,0.0007672972,0.001748834,0.00270328,0.001385681,0.002184425],"category_scores_gemma":[0.006476894,0.0005848054,0.0007128348,0.001119092,0.001136421,0.004176951,0.002603505,0.00167475,0.0006630546],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001038285,"about_ca_system_score_gemma":0.00148894,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01074401,"about_ca_topic_score_gemma":0.0161281,"domain_scores_codex":[0.9990566,0.0001321931,0.00004547816,0.0003806001,0.000224194,0.0001608879],"domain_scores_gemma":[0.9980368,0.0007352043,0.0002250221,0.0004139229,0.0003926433,0.0001964858],"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":[0.0005224551,0.0003441914,0.007410548,0.000417287,0.0001894274,0.0003014349,0.0007410763,0.06874312,0.06935613,0.02410509,0.008452465,0.8194169],"study_design_scores_gemma":[0.00003406944,0.0003560774,0.004788057,0.00004061669,0.00008560768,0.0003546547,0.0002201692,0.9381548,0.02340433,0.02792378,0.004598771,0.00003911325],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1057304,0.001098675,0.8856115,0.0008388091,0.000111579,0.0001751213,0.0002158861,0.002033529,0.004184439],"genre_scores_gemma":[0.6619774,0.0004987115,0.3301207,0.0003440333,0.0001958904,0.00008020421,0.0005595313,0.0003052868,0.005918419],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01074401,"threshold_uncertainty_score":0.02136296,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04591597025099352,"score_gpt":0.2040451411117599,"score_spread":0.1581291708607664,"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."}}