{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.001571152,0.001024509,0.001207538,0.00109478,0.001517601,0.000420424,0.001260468,0.0007805215,0.00007164367],"category_scores_gemma":[0.0008357033,0.001264609,0.0006979583,0.002478003,0.0007429224,0.00084769,0.002186768,0.001155487,0.00009091],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007172393,"about_ca_system_score_gemma":0.0002372018,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007764541,"about_ca_topic_score_gemma":0.0006393447,"domain_scores_codex":[0.992943,0.0009895057,0.00131629,0.003167375,0.0005318064,0.001051998],"domain_scores_gemma":[0.9937329,0.0007974697,0.001819362,0.001657971,0.001467645,0.000524679],"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":[0.003606334,0.00199912,0.02578408,0.006145503,0.002281072,0.002375619,0.01805652,0.5425784,0.1492144,0.008810034,0.00004496973,0.239104],"study_design_scores_gemma":[0.002051535,0.0009448557,0.002307387,0.001001483,0.0003621104,0.0001310224,0.001197784,0.9288189,0.05797349,0.003612099,0.0002415307,0.001357793],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5590026,0.0001501688,0.4374658,0.00001840706,0.00202419,0.0006835609,0.00001378554,0.0002616303,0.0003798947],"genre_scores_gemma":[0.9975721,0.0006662304,0.0006323978,0.0001027729,0.0005245316,0.000004658127,0.000008262226,0.00007375038,0.0004152839],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4385695,"threshold_uncertainty_score":0.9997823,"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."}}