{"id":"W2767286887","doi":"10.1007/s10846-017-0735-y","title":"Evaluation of Object Proposals and ConvNet Features for Landmark-based Visual Place Recognition","year":2017,"lang":"en","type":"article","venue":"Journal of Intelligent & Robotic Systems","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Hunan Provincial Innovation Foundation for Postgraduate; Natural Science Foundation of Hunan Province; National Natural Science Foundation of China","keywords":"Landmark; Artificial intelligence; Computer science; Object (grammar); Cognitive neuroscience of visual object recognition; Convolutional neural network; Matching (statistics); Strengths and weaknesses; Computer vision; Object detection; Pattern recognition (psychology); Psychology","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":[0.003268577,0.001824981,0.001599368,0.001557581,0.0004363224,0.001472642,0.002309828,0.001779424,0.003466113],"category_scores_gemma":[0.006926787,0.0005104337,0.0007606423,0.001228316,0.0004808258,0.0019198,0.001380022,0.0007582917,0.001176212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009544771,"about_ca_system_score_gemma":0.001466083,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01230392,"about_ca_topic_score_gemma":0.01270672,"domain_scores_codex":[0.9982918,0.0003297344,0.000112342,0.0004529796,0.0006170303,0.0001960512],"domain_scores_gemma":[0.9973269,0.001185593,0.0001567934,0.0003225039,0.0008394221,0.0001688935],"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":[0.00588402,0.000992866,0.01008773,0.000759168,0.0008686963,0.0002324598,0.00009087259,0.1699,0.02290472,0.001039222,0.00752736,0.7797129],"study_design_scores_gemma":[0.0001684221,0.0008073205,0.004893201,0.00003521264,0.0001801683,0.0001460643,0.00008261893,0.9812955,0.01098958,0.0005521773,0.0008255328,0.0000243271],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7884443,0.005913252,0.1885749,0.0004849014,0.0006772284,0.0004669181,0.002370974,0.006477632,0.0065899],"genre_scores_gemma":[0.9407727,0.0007762469,0.05053102,0.0001014474,0.00006807011,0.0001000258,0.004637784,0.0002800598,0.00273278],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01230392,"threshold_uncertainty_score":0.02446461,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05119879216315108,"score_gpt":0.309379694461196,"score_spread":0.2581809022980449,"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."}}