{"id":"W4319586573","doi":"10.1109/dsaa54385.2022.10032344","title":"Graph Attention Network for Camera Relocalization on Dynamic Scenes","year":2022,"lang":"en","type":"article","venue":"2022 IEEE 9th International Conference on Data Science and Advanced Analytics (DSAA)","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Computer science; Artificial intelligence; Computer vision; Embedding; Point cloud; RANSAC; RGB color model; Benchmark (surveying); Graph; Matching (statistics); Triangle mesh; Polygon mesh; Pose; Scene graph; Artificial neural network; Image (mathematics); Computer graphics (images); Theoretical computer science; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005431289,0.001509079,0.001054853,0.001423964,0.0005283852,0.0006541462,0.001973432,0.001292595,0.003755166],"category_scores_gemma":[0.002364286,0.0006142292,0.0009666042,0.001374444,0.0006867507,0.001785166,0.001313422,0.001671662,0.0008669721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001902157,"about_ca_system_score_gemma":0.00100887,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03048967,"about_ca_topic_score_gemma":0.03064211,"domain_scores_codex":[0.9994231,0.00009161133,0.00001705808,0.0002465306,0.0001220326,0.0000997062],"domain_scores_gemma":[0.9994288,0.0002095153,0.00008703639,0.00009961143,0.0001372249,0.00003771209],"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":[0.0001649401,0.00008793105,0.0009026175,0.00009278855,0.00007426389,0.0001217286,0.00008727523,0.7441095,0.008893651,0.006482808,0.00381274,0.2351697],"study_design_scores_gemma":[0.000003316865,0.00001483598,0.0001502459,0.00000318447,0.000006415364,0.00001833037,0.000007714966,0.9954907,0.001204746,0.002725252,0.0003712731,0.000003954713],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02111357,0.000319848,0.9736655,0.0001858525,0.00004677483,0.00004327426,0.0001573869,0.002577841,0.001890044],"genre_scores_gemma":[0.705963,0.0004807332,0.2841657,0.0004793321,0.0001173305,0.0001208976,0.001361256,0.0005665753,0.006745152],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03048967,"threshold_uncertainty_score":0.06062436,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04516029495801743,"score_gpt":0.3055356027970949,"score_spread":0.2603753078390775,"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."}}