{"id":"W4286970487","doi":"10.48550/arxiv.2109.09923","title":"AutoPhoto: Aesthetic Photo Capture using Reinforcement Learning","year":2021,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; National Science Foundation","keywords":"Reinforcement learning; Computer science; Heuristics; Motion capture; Process (computing); Estimator; Artificial intelligence; Pipeline (software); Metric (unit); Computer vision; Robot; Human–computer interaction; Motion (physics); Engineering; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000206132,0.0002832578,0.0002742577,0.0002332696,0.0002863408,0.0002699112,0.0008447813,0.0002791016,0.0001078662],"category_scores_gemma":[0.00001641805,0.000340019,0.0002773279,0.0006257184,0.00005214724,0.0003619574,0.001338968,0.0007790073,0.00004651071],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003366555,"about_ca_system_score_gemma":0.0002132887,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005033734,"about_ca_topic_score_gemma":0.00002999667,"domain_scores_codex":[0.998142,0.0001891406,0.000218672,0.0009728806,0.0001479776,0.000329318],"domain_scores_gemma":[0.9986562,0.00001392616,0.0002560535,0.000758083,0.0001812465,0.0001344606],"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":[0.000007948467,0.00004944351,0.0003749389,0.00005288425,0.00003294219,0.0003751531,0.0005799908,0.9884146,0.0008578814,0.008823193,0.00001952543,0.0004114897],"study_design_scores_gemma":[0.0002814715,0.00004882204,0.0001471777,0.0001258992,0.00003981267,0.00003917861,0.0003137379,0.9963932,0.0006868992,0.0008026618,0.0007239873,0.0003971681],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4416153,0.0000337745,0.5558221,0.00002034297,0.0006661596,0.0001448769,3.756947e-7,0.0002167446,0.001480288],"genre_scores_gemma":[0.9955945,0.00004971353,0.001000623,0.0001573122,0.0000272127,8.737371e-7,0.00001093641,0.0000160178,0.003142811],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5548215,"threshold_uncertainty_score":0.9999052,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0730158786417651,"score_gpt":0.2103330890919585,"score_spread":0.1373172104501934,"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."}}