{"id":"W3176028257","doi":"10.1609/aaai.v35i9.16995","title":"Visual Concept Reasoning Networks","year":2021,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Advanced Image and Video Retrieval Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"","keywords":"Computer science; Artificial intelligence; Merge (version control); Exploit; Visual reasoning; Cognitive neuroscience of visual object recognition; Convolutional neural network; Graph; Segmentation; Spatial intelligence; Pattern recognition (psychology); Machine learning; Theoretical computer science; Object (grammar); Information retrieval","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":[0.000314461,0.0002151275,0.0002726987,0.00006428992,0.0002142561,0.0003060281,0.001494797,0.0001030279,0.00006550039],"category_scores_gemma":[0.0007234496,0.0001696252,0.0001418534,0.001010007,0.0002464567,0.0006317938,0.0006466276,0.0003918204,0.00002488625],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004138656,"about_ca_system_score_gemma":0.0001221292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008554967,"about_ca_topic_score_gemma":0.000001909128,"domain_scores_codex":[0.9981524,0.00002348125,0.0004714645,0.0005428544,0.000430423,0.0003793671],"domain_scores_gemma":[0.9981318,0.0001043925,0.0003171619,0.0003250435,0.001027824,0.00009378414],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.00001670236,0.0000818997,0.00006729596,0.000009372002,0.00000927158,0.000002823873,0.0003096723,0.00005625496,0.01873501,0.7776561,0.000206461,0.2028492],"study_design_scores_gemma":[0.00001612135,0.0001247477,0.00006684571,0.0002063919,0.000006486905,0.00001260804,0.0002255178,0.05647282,0.851179,0.09119037,0.0003115448,0.0001875221],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006939864,0.0001866198,0.9792245,0.001686191,0.0003968963,0.0002291315,0.000001641696,0.0002329764,0.01110216],"genre_scores_gemma":[0.9795412,0.0001393662,0.01929164,0.0004708723,0.000102474,0.00001355137,6.135079e-7,0.00001311239,0.0004271831],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9726014,"threshold_uncertainty_score":0.6917114,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04798050538982407,"score_gpt":0.3244538886081463,"score_spread":0.2764733832183223,"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."}}