{"id":"W3113078706","doi":"10.1007/s11263-022-01706-5","title":"Robots Understanding Contextual Information in Human-Centered Environments Using Weakly Supervised Mask Data Distillation","year":2022,"lang":"en","type":"article","venue":"International Journal of Computer Vision","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"Vector Institute; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs; AGE-WELL; Nvidia","keywords":"Computer science; Artificial intelligence; Segmentation; Convolutional neural network; Pattern recognition (psychology); Robot; Heuristic; Categorization; Object (grammar); Image segmentation; Machine learning; Computer vision","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":[0.0003565747,0.0009539857,0.001045823,0.0004252855,0.0005289178,0.0008532993,0.001154493,0.001149662,0.001349288],"category_scores_gemma":[0.00195499,0.0005254749,0.0006499695,0.0004875796,0.0009833566,0.0015596,0.00241245,0.001448998,0.000453036],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002960121,"about_ca_system_score_gemma":0.0009908128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004406204,"about_ca_topic_score_gemma":0.00732508,"domain_scores_codex":[0.9996163,0.00008489645,0.00001152336,0.0001413004,0.00008210409,0.00006386545],"domain_scores_gemma":[0.9994791,0.0002284289,0.00006226132,0.0001125808,0.00006807064,0.0000496082],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001457578,0.0003760289,0.003331794,0.0002765474,0.0001383616,0.0003661979,0.0007219479,0.5077726,0.08953805,0.01316277,0.003892132,0.378966],"study_design_scores_gemma":[0.00001229522,0.00008587008,0.0007773879,0.00001071988,0.000013227,0.00004103532,0.00005291932,0.9797409,0.009694403,0.008807976,0.0007476148,0.00001569241],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1194773,0.0003816227,0.8765787,0.0003015503,0.0000680653,0.0000422068,0.0002425713,0.00132472,0.001583348],"genre_scores_gemma":[0.837136,0.0002024709,0.1599107,0.0001748232,0.0000610861,0.00006655262,0.0004960313,0.000146555,0.001805914],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004406204,"threshold_uncertainty_score":0.008761108,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09627518311913438,"score_gpt":0.3382836249925456,"score_spread":0.2420084418734112,"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."}}