{"id":"W4415708125","doi":"10.1109/icme59968.2025.11209218","title":"Exploring Flexibility in Incremental Few-Shot Object Detection","year":2025,"lang":"","type":"article","venue":"","topic":"Domain Adaptation and Few-Shot Learning","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"National Natural Science Foundation of China","keywords":"Flexibility (engineering); Feature (linguistics); Object detection; Classifier (UML); Incremental learning; Object (grammar); Class (philosophy); Adaptation (eye)","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":[0.001996308,0.0003535618,0.0003777424,0.0007688773,0.0004334622,0.0005565352,0.0007405714,0.0001309711,0.0003917951],"category_scores_gemma":[0.0003354416,0.0003948765,0.0001683431,0.002675252,0.0001173111,0.002092766,0.000638221,0.000687307,0.0002063688],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007153302,"about_ca_system_score_gemma":0.0003014781,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00104312,"about_ca_topic_score_gemma":0.001502808,"domain_scores_codex":[0.9962029,0.0005269013,0.0009145464,0.001144352,0.0005116815,0.0006995727],"domain_scores_gemma":[0.9985456,0.0002566701,0.0001518378,0.0007802573,0.0001140242,0.0001516097],"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":[0.0001829536,0.0004322649,0.009578578,0.0001404623,0.00005951808,0.00002887792,0.005632254,0.004096463,0.01481954,0.02296285,0.00004504056,0.9420212],"study_design_scores_gemma":[0.002712255,0.0003049079,0.167011,0.0003592651,0.00002499055,0.000009771736,0.005098508,0.7490606,0.06584316,0.002167359,0.006560464,0.0008476565],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3977652,0.0002064134,0.51999,0.0005970735,0.002904716,0.0005415178,8.363542e-7,0.0002674429,0.0777268],"genre_scores_gemma":[0.9916847,0.0001108862,0.004732389,0.0006748212,0.00007157811,0.000079753,0.000001744544,0.00001300228,0.00263118],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9411736,"threshold_uncertainty_score":0.9998503,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1718617007588261,"score_gpt":0.3208801751713076,"score_spread":0.1490184744124815,"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."}}