{"id":"W4387415240","doi":"10.1109/tgrs.2023.3322579","title":"MCTNet: Multiscale Cross-Attention-Based Transformer Network for Semantic Segmentation of Large-Scale Point Cloud","year":2023,"lang":"en","type":"article","venue":"IEEE Transactions on Geoscience and Remote Sensing","topic":"3D Shape Modeling and Analysis","field":"Engineering","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"National Natural Science Foundation of China","keywords":"Computer science; Artificial intelligence; Data mining; Encoder; Transformer; Segmentation; Feature extraction; Security token; Pattern recognition (psychology); Computer network","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":[0.0005075674,0.001477852,0.001159963,0.001547281,0.0005130427,0.0008095657,0.002508957,0.001085345,0.003518274],"category_scores_gemma":[0.001287782,0.0006172304,0.001189268,0.001513851,0.0006557673,0.002335354,0.001701814,0.001132533,0.001202432],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001715377,"about_ca_system_score_gemma":0.001417703,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01952506,"about_ca_topic_score_gemma":0.02800854,"domain_scores_codex":[0.9996336,0.0000314725,0.00001558923,0.0001559204,0.00009197574,0.00007141285],"domain_scores_gemma":[0.9997396,0.00005796011,0.00003053916,0.00005815212,0.00008013674,0.00003371761],"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":[0.0005099965,0.0002417716,0.003457775,0.0002680126,0.0002842348,0.0003312085,0.0002359452,0.3536019,0.03428852,0.01491338,0.01687657,0.5749907],"study_design_scores_gemma":[0.00001259585,0.00003946201,0.0004367045,0.000008370811,0.00002673705,0.00005583722,0.0000216116,0.9878322,0.004695618,0.005241636,0.001618984,0.00001025477],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03766095,0.0007461601,0.9487982,0.0002897604,0.0001302445,0.0001275766,0.000965121,0.007860481,0.003421442],"genre_scores_gemma":[0.6045222,0.0006347181,0.3789095,0.0005738826,0.0001364584,0.0002607868,0.005212996,0.0007137301,0.009035638],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01952506,"threshold_uncertainty_score":0.03882283,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01293129356734765,"score_gpt":0.2634567325341741,"score_spread":0.2505254389668265,"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."}}