{"id":"W4293868279","doi":"10.1109/crv55824.2022.00038","title":"3DVQA: Visual Question Answering for 3D Environments","year":2022,"lang":"en","type":"article","venue":"","topic":"Multimodal Machine Learning Applications","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"Natural Sciences and Engineering Research Council of Canada; Western Canada Research Grid; Compute Canada","keywords":"Question answering; Computer science; Task (project management); Artificial intelligence; Domain (mathematical analysis); Modality (human–computer interaction); Relation (database); Point (geometry); Baseline (sea); Natural language processing; Information retrieval; Computer vision; Data mining","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":[0.00256378,0.003990883,0.002086913,0.004225203,0.001491448,0.003097689,0.006325684,0.006019084,0.01826142],"category_scores_gemma":[0.01481161,0.0008692858,0.003245186,0.002634128,0.001392163,0.007340856,0.007800746,0.003621586,0.01082885],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002273995,"about_ca_system_score_gemma":0.002162899,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02823586,"about_ca_topic_score_gemma":0.03649778,"domain_scores_codex":[0.9957637,0.001186119,0.0003267197,0.001252186,0.00118404,0.0002872043],"domain_scores_gemma":[0.9945655,0.002395447,0.0002668263,0.001730649,0.0007045093,0.000337002],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001061761,0.0007180875,0.005817605,0.00513973,0.000455123,0.000746377,0.0009856868,0.02836282,0.01419544,0.01522226,0.5953808,0.3319141],"study_design_scores_gemma":[0.0007531581,0.0007537727,0.01275694,0.0009508587,0.0001952663,0.002147041,0.001665832,0.374928,0.02016611,0.07095529,0.514486,0.0002416991],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.08280785,0.01589789,0.2805194,0.004254646,0.001363926,0.004747027,0.3740657,0.1931835,0.04315998],"genre_scores_gemma":[0.1345532,0.001822885,0.2584551,0.002252154,0.0002187999,0.002259798,0.5915077,0.002208509,0.006721979],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02823586,"threshold_uncertainty_score":0.06109047,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009262272345025406,"score_gpt":0.2886512940798796,"score_spread":0.2793890217348541,"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."}}