{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001365824,0.00008987846,0.0001184943,0.00007309886,0.00004154635,0.00003960996,0.0003464412,0.00003941096,0.0000041995],"category_scores_gemma":[0.0001158057,0.0000681927,0.00003067226,0.0001616809,0.0001366969,0.000852159,0.0002553657,0.0001445479,0.000002249211],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000659752,"about_ca_system_score_gemma":0.00003175627,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001690784,"about_ca_topic_score_gemma":0.000006111796,"domain_scores_codex":[0.9993346,0.0000175923,0.0001341107,0.0002438019,0.0001400446,0.0001298648],"domain_scores_gemma":[0.9993322,0.0000707858,0.0000733262,0.0003455626,0.0001254536,0.00005264103],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00003985526,0.00008334815,0.0003256113,0.00002999136,0.000006238072,0.00002380407,0.00150808,0.000009789403,0.6832813,0.2074941,0.000862214,0.1063357],"study_design_scores_gemma":[0.0001485007,0.0001447384,0.0007985486,0.00001987376,0.000004070303,0.00002821265,0.00002851756,0.2161214,0.7136832,0.0687815,0.00008827934,0.0001531312],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02756313,0.0000218048,0.9693326,0.000239298,0.00004278328,0.0001173616,0.000001087473,0.0001238392,0.002558052],"genre_scores_gemma":[0.6740907,0.00004229603,0.3247894,0.00008042677,0.000005491616,0.000004767437,2.117325e-7,0.000003630954,0.0009830148],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.6465276,"threshold_uncertainty_score":0.2780817,"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."}}