{"id":"W4415214196","doi":"10.1016/j.neunet.2025.108206","title":"Stochastic style perturbation modelling for visible-Infrared person re-Identification with severely modality imbalance","year":2025,"lang":"en","type":"article","venue":"Neural Networks","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Windsor","funders":"Natural Science Foundation of Zhejiang Province; National Natural Science Foundation of China","keywords":"Modality (human–computer interaction); Discriminative model; Feature learning; Consistency (knowledge bases); Modalities; Feature vector; Contrast (vision); Perturbation (astronomy); Synthetic data","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":[0.0005901746,0.0001751933,0.0002145127,0.0000852529,0.000278057,0.000280213,0.0004659386,0.0001011946,0.000001533912],"category_scores_gemma":[0.00005363582,0.0001554239,0.00007806161,0.0005503481,0.00003196886,0.0005232276,0.00004452018,0.0002003255,0.000001201067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006073641,"about_ca_system_score_gemma":0.00004412906,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001903447,"about_ca_topic_score_gemma":0.00001186881,"domain_scores_codex":[0.9985218,0.000132893,0.0002530624,0.0005589765,0.0002012909,0.0003319972],"domain_scores_gemma":[0.9986154,0.0004111094,0.0001581053,0.0005548467,0.0002084141,0.00005206098],"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":[0.00009272211,0.00002235006,0.0003110144,0.00002868576,0.00001893811,9.783765e-7,0.0001943413,0.9787712,0.00002524228,0.004210374,0.0002959354,0.01602824],"study_design_scores_gemma":[0.000444223,0.00006225808,0.00457549,0.00005747747,0.00001427006,0.00000233703,0.00002190748,0.9888079,0.00006533365,0.005723926,0.0000548641,0.0001700258],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01191005,0.0002748235,0.9851398,0.001117789,0.0005817073,0.0004458493,0.000003325171,0.0002183777,0.0003082533],"genre_scores_gemma":[0.95669,0.000008995479,0.04174191,0.0003599348,0.0001252168,0.00008012603,0.00002066358,0.00001261933,0.0009605691],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9447799,"threshold_uncertainty_score":0.6338002,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03100036715089219,"score_gpt":0.28358394630298,"score_spread":0.2525835791520878,"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."}}