{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007463842,0.00108237,0.0006390194,0.001224115,0.0005144393,0.0008065127,0.001427684,0.0009153517,0.001944922],"category_scores_gemma":[0.002481738,0.0002943829,0.0009320355,0.0009497002,0.0005497382,0.002417947,0.001157874,0.001397379,0.0008152541],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009453024,"about_ca_system_score_gemma":0.001092859,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01349252,"about_ca_topic_score_gemma":0.02144602,"domain_scores_codex":[0.9995888,0.00006643884,0.00002243675,0.0001666273,0.0001007058,0.0000551048],"domain_scores_gemma":[0.9992778,0.0002538114,0.00007019901,0.0001694287,0.0001596919,0.0000690531],"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":[0.0007906062,0.0006903574,0.01881977,0.0002471156,0.0001591966,0.0006122104,0.000355629,0.3707472,0.02110128,0.02234348,0.01493211,0.5492011],"study_design_scores_gemma":[0.000005978604,0.00003588144,0.0009056717,0.0000161458,0.00001632803,0.00005516062,0.00003529057,0.9791158,0.003620093,0.01496316,0.001216577,0.00001395415],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06591334,0.0004991313,0.9239205,0.0003515326,0.00008726121,0.00005983943,0.001731959,0.005226486,0.002210071],"genre_scores_gemma":[0.7755924,0.0002602765,0.2170874,0.0002269023,0.0000373887,0.00009129589,0.004152543,0.0001571266,0.002394733],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01349252,"threshold_uncertainty_score":0.02682799,"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."}}