{"id":"W4409367093","doi":"10.1609/aaai.v39i5.32535","title":"RCTrans: Radar-Camera Transformer via Radar Densifier and Sequential Decoder for 3D Object Detection","year":2025,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Advanced SAR Imaging Techniques","field":"Engineering","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"","keywords":"Radar; Computer science; Transformer; Computer vision; Artificial intelligence; Engineering; Telecommunications; Electrical engineering; Voltage","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":[0.0002362432,0.0002466032,0.0002645562,0.0001596182,0.0001609787,0.00008207394,0.000338169,0.0001178913,0.00001981023],"category_scores_gemma":[0.0001008325,0.0002143359,0.0001052921,0.0003048324,0.0002277482,0.0002693271,0.00002648625,0.0002827453,0.000003705653],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007094975,"about_ca_system_score_gemma":0.0000332714,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002302873,"about_ca_topic_score_gemma":0.00003359306,"domain_scores_codex":[0.9987687,0.000006796799,0.0004262874,0.0003138049,0.0001771103,0.0003073429],"domain_scores_gemma":[0.9993839,0.00007210964,0.00007673106,0.0001384583,0.0002843786,0.00004441716],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001277439,0.00002481873,0.00002054395,0.0001873035,0.00004157315,1.286211e-7,0.0004042867,0.00006403903,0.6778261,0.01920943,0.00006820392,0.3020258],"study_design_scores_gemma":[0.00004408045,0.00006319664,0.00001694649,0.0001789415,0.00004516618,0.000003676209,0.0001435689,0.0520152,0.8495762,0.09709673,0.0006302252,0.0001860563],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.04847759,0.0001265961,0.9462914,0.0005366247,0.0004299885,0.0008678142,0.00001250248,0.0003360739,0.002921362],"genre_scores_gemma":[0.9732584,0.0001607966,0.02623154,0.0001057105,0.00004354596,0.00008562751,8.5653e-7,0.00003112531,0.00008240535],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9247808,"threshold_uncertainty_score":0.8740363,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03219348471573048,"score_gpt":0.2889725305831896,"score_spread":0.2567790458674591,"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."}}