{"id":"W4414458632","doi":"10.1109/tcsvt.2025.3613262","title":"Leveraging Multi-View Images to Learn Domain-Invariant Discriminative Embeddings for Cross-View Geo-Localization","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Circuits and Systems for Video Technology","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Fundamental Research Funds for Central Universities of the Central South University; Natural Science Foundation of Fujian Province; National Natural Science Foundation of China","keywords":"Discriminative model; Robustness (evolution); Drone; Feature learning; Feature extraction; Pattern recognition (psychology); Task analysis; Feature (linguistics); Task (project management)","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006505414,0.0002556811,0.0004312292,0.0007599302,0.0008883743,0.0003808833,0.0004607631,0.0001946847,0.000003082513],"category_scores_gemma":[0.00008723661,0.000245634,0.0001207013,0.0008527225,0.0001220808,0.0003477847,0.0000103348,0.0002225977,0.000008897532],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001282249,"about_ca_system_score_gemma":0.00008865274,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004281569,"about_ca_topic_score_gemma":0.00001874659,"domain_scores_codex":[0.9980759,0.00008100303,0.0005193789,0.0007318853,0.000156371,0.0004354756],"domain_scores_gemma":[0.9986961,0.0003117392,0.0001549981,0.0004116162,0.0003324051,0.00009308628],"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":[0.00005570984,0.0003417279,0.0001243642,0.001693662,0.0003089525,0.00001181874,0.005173328,0.04338073,0.008134141,0.2942122,0.0006925854,0.6458708],"study_design_scores_gemma":[0.006465835,0.001103157,0.0003608349,0.002207141,0.0001853098,0.0001342691,0.00548155,0.6899961,0.01612284,0.0152561,0.2612202,0.001466732],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001090307,0.0007544861,0.9929807,0.001787379,0.001019669,0.001748194,0.00003274798,0.0004371545,0.0001494253],"genre_scores_gemma":[0.9823415,0.0001028601,0.01369414,0.0006641771,0.00002256543,0.001232905,0.000003838149,0.00002923636,0.001908752],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9812512,"threshold_uncertainty_score":0.9999996,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03417772069864473,"score_gpt":0.3144990413032279,"score_spread":0.2803213206045832,"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."}}