{"id":"W4402915270","doi":"10.1109/icip51287.2024.10647866","title":"Learn By An Example Transformer For Domain Generalization In Video Object Segmentation","year":2024,"lang":"en","type":"article","venue":"","topic":"Image Processing Techniques and Applications","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"","keywords":"Computer science; Transformer; Computer vision; Segmentation; Artificial intelligence; Generalization; Mathematics; Engineering; Electrical engineering","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":[0.00008783664,0.00005829828,0.00004413638,0.0000543628,0.00002808366,0.00007028031,0.0000379557,0.00003415642,0.00003535687],"category_scores_gemma":[6.831083e-7,0.00005665974,0.00001602432,0.0001515413,0.000005563063,0.0002328497,0.000001175759,0.00003582826,0.000003810213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004000181,"about_ca_system_score_gemma":0.000007155552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001019429,"about_ca_topic_score_gemma":0.00008120594,"domain_scores_codex":[0.9996492,0.000004300863,0.0001057545,0.0001088272,0.00003810457,0.00009383841],"domain_scores_gemma":[0.9999049,0.00001017745,0.000003539073,0.00005605477,0.000009224083,0.0000160559],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000003461909,0.00002879305,0.00002318795,0.000263255,0.000008980101,3.269379e-7,0.001424819,0.004738383,0.8270937,0.008869379,0.02597324,0.1315724],"study_design_scores_gemma":[0.0002191931,0.00003909579,0.00002106394,0.00003250227,0.000009775109,0.000001994134,0.0001775477,0.5164614,0.3816913,0.01807831,0.08305024,0.0002175623],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01680928,0.0002246429,0.9804482,0.000109378,0.00002775449,0.0002624743,0.000009646083,0.0006331332,0.001475467],"genre_scores_gemma":[0.8359211,0.00009834728,0.1622157,0.00009128747,0.00004593278,0.0006140028,0.0003884366,0.00004763186,0.0005775173],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8191118,"threshold_uncertainty_score":0.2310517,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01876607957222535,"score_gpt":0.2783013560109775,"score_spread":0.2595352764387521,"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."}}