{"id":"W3191907322","doi":"10.1109/iccv48922.2021.01531","title":"Bifold and Semantic Reasoning for Pedestrian Behavior Prediction","year":2021,"lang":"en","type":"article","venue":"2021 IEEE/CVF International Conference on Computer Vision (ICCV)","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":81,"is_retracted":false,"has_abstract":true,"ca_institutions":"Huawei Technologies (Canada)","funders":"","keywords":"Computer science; Pedestrian; Artificial intelligence; Modalities; Machine learning; Benchmark (surveying); Categorical variable; Trajectory; Representation (politics); Encoding (memory); Decoding methods; Parsing; Human–computer interaction","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":[0.0001365042,0.0001929691,0.0002033037,0.000142306,0.0001168541,0.0001247461,0.0002024396,0.0001875373,0.0003018306],"category_scores_gemma":[0.0000205398,0.0002057987,0.00007727367,0.0001041206,0.00003828004,0.0001685775,0.00007437328,0.0002671535,0.00004814083],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009146622,"about_ca_system_score_gemma":0.00005078973,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006392918,"about_ca_topic_score_gemma":0.00002000015,"domain_scores_codex":[0.9988861,0.00002669116,0.0003034066,0.00038293,0.0001941117,0.0002067297],"domain_scores_gemma":[0.9993626,0.00007916035,0.0000549543,0.0002221697,0.0002073155,0.00007379993],"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":[0.0002400829,0.0006631567,0.009706334,0.0002302772,0.0006066932,0.0004270674,0.0006456542,0.01085352,0.05154141,0.1066627,0.0187429,0.7996802],"study_design_scores_gemma":[0.00096147,0.0002392487,0.01266001,0.0002658742,0.000040153,0.0001073538,0.00005571801,0.9707589,0.007559524,0.001024201,0.006026237,0.0003013511],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6803196,0.0001474637,0.3088081,0.001938653,0.004334182,0.0004373796,0.0001692567,0.0005894199,0.003255902],"genre_scores_gemma":[0.9922429,0.000237096,0.006322213,0.0001030547,0.0003441619,0.00005844114,0.0001218684,0.00002656322,0.0005436994],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9599053,"threshold_uncertainty_score":0.8392226,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02191209137745254,"score_gpt":0.269649960127515,"score_spread":0.2477378687500624,"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."}}