{"id":"W4388535692","doi":"10.21203/rs.3.rs-3557409/v1","title":"Transductive Meta-Learning with Enhanced Feature Ensemble for Few-shot Semantic Segmentation","year":2023,"lang":"en","type":"preprint","venue":"Research Square","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Artificial intelligence; Pascal (unit); Segmentation; Benchmark (surveying); Pattern recognition (psychology); Matching (statistics); Machine learning; Shot (pellet); Extractor; Feature (linguistics); Class (philosophy)","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.002943981,0.0004334606,0.0006616857,0.0007462504,0.0007739073,0.0009293986,0.001171101,0.0003364172,0.00005674767],"category_scores_gemma":[0.000489019,0.000362051,0.0003823786,0.001228351,0.0001300517,0.0004328389,0.0005803739,0.002424577,0.000124668],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002581532,"about_ca_system_score_gemma":0.0005699024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008009514,"about_ca_topic_score_gemma":0.0001209491,"domain_scores_codex":[0.9943482,0.001068799,0.0003497615,0.001454851,0.001793461,0.000984945],"domain_scores_gemma":[0.9961717,0.001291249,0.0002353349,0.0008406958,0.001214366,0.0002466161],"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":[0.002025917,0.0009407802,0.0005049102,0.01377096,0.0119673,0.0005100435,0.1441992,0.520995,0.08327121,0.05470241,0.01461905,0.1524933],"study_design_scores_gemma":[0.01070852,0.007718295,0.008771082,0.005060983,0.002113858,0.00008368529,0.04475746,0.6644993,0.1414797,0.06156276,0.04679792,0.006446462],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004238296,0.0004641554,0.9859923,0.004163596,0.0003432723,0.002739228,0.0000267495,0.0006234217,0.001409048],"genre_scores_gemma":[0.867561,0.000280751,0.1101407,0.00008676594,0.0003032839,0.002901642,0.0004166953,0.0001530367,0.01815609],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8758515,"threshold_uncertainty_score":0.9998832,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1850342493475725,"score_gpt":0.4148479433312821,"score_spread":0.2298136939837095,"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."}}