{"id":"W2773584821","doi":"10.1609/aaai.v32i1.12323","title":"Co-Domain Embedding Using Deep Quadruplet Networks for Unseen Traffic Sign Recognition","year":2018,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Infrastructure Maintenance and Monitoring","field":"Engineering","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kootenay Association for Science & Technology","funders":"Samsung","keywords":"Computer science; Embedding; Artificial intelligence; Generalization; Traffic sign recognition; Pairwise comparison; Similarity (geometry); Feature (linguistics); Pattern recognition (psychology); Class (philosophy); Domain (mathematical analysis); Sign (mathematics); Synthetic data; Machine learning; Traffic sign; Image (mathematics); 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":[0.0005601716,0.0008627907,0.0007604593,0.0007007181,0.0003102104,0.0005997439,0.001233185,0.001009276,0.00183226],"category_scores_gemma":[0.001418701,0.0003084451,0.0005187584,0.0007845328,0.000507428,0.001933117,0.001113973,0.001574122,0.0009572005],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006522136,"about_ca_system_score_gemma":0.0004559813,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003296587,"about_ca_topic_score_gemma":0.004956003,"domain_scores_codex":[0.9996604,0.00006439142,0.00001461574,0.0001281878,0.00007720407,0.00005516217],"domain_scores_gemma":[0.9993612,0.0001893902,0.00007878996,0.0001837571,0.0001383477,0.00004853137],"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":[0.0004760084,0.0006045458,0.003299441,0.0001314761,0.0001302014,0.0003082046,0.0001799515,0.3264913,0.03138925,0.009321504,0.01179122,0.6158769],"study_design_scores_gemma":[0.00000326915,0.00002875142,0.0002693292,0.000003352735,0.000006036621,0.00003575784,0.00001235526,0.9934686,0.002912561,0.002615688,0.0006383433,0.000005879079],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.175642,0.0008749588,0.8150223,0.0004065767,0.0001627684,0.00008121506,0.0005645147,0.003433532,0.003811972],"genre_scores_gemma":[0.8265802,0.0003763474,0.1631895,0.000258364,0.00007820262,0.00009179788,0.002762383,0.0001373468,0.006525735],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003296587,"threshold_uncertainty_score":0.006554782,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05853266592608864,"score_gpt":0.2991962923570157,"score_spread":0.2406636264309271,"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."}}