{"id":"W3109534421","doi":"","title":"Action Concept Grounding Network for Semantically-Consistent Video Generation","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Human Pose and Action Recognition","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Task (project management); Action (physics); Bridge (graph theory); Artificial intelligence; Object (grammar); Bounding overwatch; Exploit; Minimum bounding box; Machine learning; Ground; Image (mathematics)","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00021961,0.0002221193,0.0002603099,0.0001193803,0.0003664547,0.0004094691,0.0004282428,0.000260308,0.00005057603],"category_scores_gemma":[0.00003273954,0.0002767131,0.0002766309,0.0002852344,0.0000444793,0.0006264337,0.0004271003,0.0003173981,0.00002450456],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002281253,"about_ca_system_score_gemma":0.0001758286,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003387964,"about_ca_topic_score_gemma":0.00007890162,"domain_scores_codex":[0.9983413,0.0001411375,0.0002230572,0.0009111552,0.00008190892,0.0003014121],"domain_scores_gemma":[0.9987132,0.0001137844,0.0002414173,0.0005334416,0.0002848144,0.000113361],"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":[0.00005057572,0.0002859721,0.0004241279,0.0002888209,0.000504245,0.000241979,0.0006403228,0.6517558,0.001593708,0.3136021,0.009637583,0.02097478],"study_design_scores_gemma":[0.0005322401,0.00006021844,0.0003996354,0.0001489472,0.0001417551,0.00001076986,0.0001293585,0.976736,0.00144644,0.01820007,0.001720317,0.0004742274],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1568524,0.00004568378,0.8399587,0.000131843,0.002115926,0.0003289987,0.000006002858,0.0001714468,0.0003890248],"genre_scores_gemma":[0.9906703,0.0001190705,0.006841086,0.0002736061,0.0008249609,0.000004984043,0.0002083327,0.00001456135,0.001043118],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8338179,"threshold_uncertainty_score":0.9999685,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1744843238970816,"score_gpt":0.2251907206734084,"score_spread":0.05070639677632677,"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."}}