{"id":"W2789177853","doi":"10.1609/aaai.v32i1.12274","title":"Visual Relationship Detection With Deep Structural Ranking","year":2018,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Multimodal Machine Learning Applications","field":"Computer Science","cited_by":96,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"China Scholarship Council; Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China; Canada Research Chairs","keywords":"Ranking (information retrieval); Computer science; Artificial intelligence; Complement (music); Representation (politics); Relevance (law); Object (grammar); Machine learning; Pattern recognition (psychology); Function (biology); Image (mathematics)","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":[0.0008348082,0.001896025,0.001400695,0.004016099,0.0006654232,0.001542278,0.003178721,0.001658892,0.003777467],"category_scores_gemma":[0.002835093,0.0005941763,0.001211345,0.002575917,0.0004843308,0.003153138,0.002015155,0.001586217,0.002213692],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008883859,"about_ca_system_score_gemma":0.001124591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006216206,"about_ca_topic_score_gemma":0.01481269,"domain_scores_codex":[0.9987325,0.0001873538,0.00005841866,0.0004275013,0.0003964423,0.0001977103],"domain_scores_gemma":[0.99875,0.000316013,0.0002397595,0.000304376,0.0002757334,0.0001140447],"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":[0.0004447433,0.0005605774,0.004214717,0.0003911644,0.0001877078,0.0003491963,0.0001617902,0.04233181,0.03378962,0.008497369,0.02006361,0.8890077],"study_design_scores_gemma":[0.00003731055,0.0001873806,0.002020791,0.00003628083,0.00007871218,0.0003588426,0.00008628179,0.9649402,0.01340062,0.0141663,0.004647304,0.00003995187],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07075121,0.002400609,0.909874,0.000328491,0.0001357364,0.0002395018,0.001296684,0.009860622,0.005113162],"genre_scores_gemma":[0.6267779,0.0009357023,0.356066,0.0004190536,0.0001861021,0.0002171152,0.005761386,0.0005542428,0.009082506],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006216206,"threshold_uncertainty_score":0.0126369,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04774393774670666,"score_gpt":0.3167447715831568,"score_spread":0.2690008338364502,"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."}}