{"id":"W2116721202","doi":"10.1109/crv.2014.23","title":"Visual Saliency Improves Autonomous Visual Search","year":2014,"lang":"en","type":"article","venue":"","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Visual search; Computer science; Artificial intelligence; Object (grammar); Process (computing); Exploit; Mobile robot; Key (lock); Task (project management); Robot; Computer vision; Visualization","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0003312152,0.0005171514,0.0006217032,0.0008302372,0.0003029124,0.000591509,0.0007622935,0.0005400662,0.00181212],"category_scores_gemma":[0.002359912,0.0002854148,0.0003464958,0.0003746479,0.0003770751,0.001023796,0.001126288,0.00040229,0.000384944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005158588,"about_ca_system_score_gemma":0.0004531319,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003922469,"about_ca_topic_score_gemma":0.003320748,"domain_scores_codex":[0.9997051,0.00003933613,0.000009420363,0.00007931626,0.0001268255,0.00003993745],"domain_scores_gemma":[0.999231,0.000364278,0.00008574689,0.0001161206,0.000143324,0.00005959656],"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":[0.0005085733,0.0003441128,0.002904904,0.0002268194,0.00008700552,0.0002546429,0.0003933807,0.1281645,0.2193874,0.009914652,0.00454474,0.6332692],"study_design_scores_gemma":[0.00006835035,0.0002408435,0.004241441,0.000009407083,0.0000363026,0.000215399,0.00004642572,0.9573335,0.02586357,0.008772206,0.003150466,0.00002207638],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3726869,0.001870902,0.6098678,0.0002646803,0.0001127893,0.00009265007,0.00009045404,0.0044267,0.01058725],"genre_scores_gemma":[0.9029846,0.0002101559,0.0943562,0.00006582151,0.0000455234,0.00003375642,0.00008481038,0.0001635528,0.002055586],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003922469,"threshold_uncertainty_score":0.007799268,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01184768938493051,"score_gpt":0.2914767953386052,"score_spread":0.2796291059536747,"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."}}