{"id":"W2156867888","doi":"","title":"A Discriminative Latent Model of Image Region and Object Tag Correspondence","year":2010,"lang":"en","type":"article","venue":"","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":28,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Discriminative model; Artificial intelligence; Computer science; Annotation; Object (grammar); Image (mathematics); Pattern recognition (psychology); Representation (politics); Probabilistic latent semantic analysis; Ground truth; Automatic image annotation; Image retrieval; Latent variable; Computer vision","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":[0.001404414,0.0006926536,0.001179316,0.001290391,0.0004106745,0.001766348,0.003024464,0.001691645,0.002394504],"category_scores_gemma":[0.004991184,0.0006428344,0.001173282,0.002173201,0.001021671,0.003844808,0.001393915,0.00198362,0.001919346],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001107609,"about_ca_system_score_gemma":0.001062415,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004213689,"about_ca_topic_score_gemma":0.006738856,"domain_scores_codex":[0.9985546,0.0004235333,0.00005453612,0.0005008639,0.0002987653,0.000167703],"domain_scores_gemma":[0.9979268,0.0006423803,0.0003877985,0.0006389655,0.0003041515,0.00009979694],"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":[0.0009730217,0.0007428872,0.01056224,0.0005247081,0.0003028645,0.000434859,0.0007620404,0.3257387,0.0419099,0.1059303,0.01728082,0.4948378],"study_design_scores_gemma":[0.00002161193,0.00005085175,0.0008954475,0.00001639752,0.00003263307,0.0001187789,0.00003027694,0.9746842,0.002513427,0.01986405,0.001750036,0.00002214631],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01226916,0.0002409179,0.985382,0.000194479,0.000036653,0.00002774714,0.0003811322,0.0007473379,0.0007204918],"genre_scores_gemma":[0.6830544,0.0006275017,0.3024974,0.0003942244,0.0002515029,0.0003215141,0.003136337,0.0003436403,0.009373493],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004213689,"threshold_uncertainty_score":0.008378327,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02403900720080977,"score_gpt":0.2879254838052898,"score_spread":0.2638864766044801,"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."}}