{"id":"W1970842225","doi":"10.1007/s10994-013-5336-9","title":"Learning semantic representations of objects and their parts","year":2013,"lang":"en","type":"article","venue":"Machine Learning","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; Agence Nationale de la Recherche; Compute Canada; Defense Advanced Research Projects Agency; Canadian Institute for Advanced Research","keywords":"Computer science; WordNet; Object (grammar); Embedding; Artificial intelligence; Image retrieval; Annotation; Set (abstract data type); Image (mathematics); Information retrieval; Automatic image annotation; Learning object; Cognitive neuroscience of visual object recognition; Machine learning; Pattern recognition (psychology)","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":[0.0006592181,0.001194435,0.00103572,0.002479229,0.0004025205,0.001705555,0.001342609,0.00176,0.002284115],"category_scores_gemma":[0.002842651,0.0004648351,0.001440131,0.002730231,0.0008280452,0.004841204,0.001293883,0.001498839,0.001050153],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007351899,"about_ca_system_score_gemma":0.001005431,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003439923,"about_ca_topic_score_gemma":0.003829201,"domain_scores_codex":[0.9994468,0.0000886417,0.00004285909,0.0002117134,0.0001448637,0.00006510579],"domain_scores_gemma":[0.9993826,0.0002018261,0.0000875429,0.0001614444,0.0001282936,0.00003823459],"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":[0.0007361026,0.0005392164,0.005690822,0.0006019238,0.0002891729,0.0003773675,0.0003862725,0.03827069,0.03676052,0.03932413,0.01768268,0.859341],"study_design_scores_gemma":[0.00009819605,0.0003346434,0.004449733,0.0002237069,0.0003651305,0.0005875715,0.0006657404,0.8043466,0.01742343,0.1593697,0.01207392,0.00006166514],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0902808,0.001822691,0.898594,0.000780955,0.0002476584,0.0001722867,0.001813114,0.002811997,0.003476427],"genre_scores_gemma":[0.6444193,0.002628318,0.3397652,0.0004077421,0.0002468404,0.0002551999,0.007942435,0.0002395967,0.004095428],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003439923,"threshold_uncertainty_score":0.007641137,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01081827026464842,"score_gpt":0.2673789469826173,"score_spread":0.2565606767179689,"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."}}