{"id":"W4411987910","doi":"10.1016/j.cviu.2025.104438","title":"Distribution-aware contrastive learning for domain adaptation in 3D LiDAR segmentation","year":2025,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada; Mitacs","keywords":"Domain adaptation; Computer science; Segmentation; Lidar; Artificial intelligence; Domain (mathematical analysis); Adaptation (eye); Computer vision; Distribution (mathematics); Pattern recognition (psychology); Remote sensing; Geography; Mathematics; Psychology","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00168866,0.0008679376,0.001360237,0.00119128,0.0005759679,0.001110275,0.002216795,0.00154815,0.001279077],"category_scores_gemma":[0.00437066,0.0005911455,0.001252132,0.001190281,0.001309585,0.002107517,0.002224742,0.002398353,0.0006458305],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001217316,"about_ca_system_score_gemma":0.001064591,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003668837,"about_ca_topic_score_gemma":0.004252721,"domain_scores_codex":[0.9992003,0.0002094942,0.00003612008,0.0003116127,0.0001480747,0.00009447253],"domain_scores_gemma":[0.9985036,0.0007734136,0.0001276595,0.0002748419,0.0002233347,0.00009729502],"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":[0.0002938911,0.0002569738,0.00268594,0.000173094,0.0001320966,0.000147459,0.0002580349,0.5850911,0.01881565,0.01125228,0.0054433,0.3754503],"study_design_scores_gemma":[0.000005124646,0.00001471943,0.0001592926,0.000004123026,0.000004079062,0.00002228551,0.00001059748,0.9934041,0.001690745,0.004332898,0.0003467356,0.000005349858],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02509283,0.0003466595,0.972504,0.0001698976,0.00003760036,0.00003870681,0.00008193889,0.001149329,0.0005790884],"genre_scores_gemma":[0.6497865,0.0003231719,0.3456346,0.0005820003,0.0001207906,0.0001832442,0.0009680068,0.0004643891,0.001937267],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003668837,"threshold_uncertainty_score":0.008930564,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02485414115186745,"score_gpt":0.289053672136619,"score_spread":0.2641995309847515,"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."}}