{"id":"W4206077751","doi":"10.1016/j.cviu.2021.103352","title":"Cross-modal distillation for RGB-depth person re-identification","year":2022,"lang":"en","type":"article","venue":"Computer Vision and Image Understanding","topic":"Video Surveillance and Tracking Methods","field":"Computer Science","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Artificial intelligence; Computer science; RGB color model; Computer vision; Pattern recognition (psychology); Identification (biology); Modality (human–computer interaction); Deep learning; Modal; Feature (linguistics); Distillation; Machine learning","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":[0.00116971,0.0001409619,0.0001653785,0.0001706171,0.00112182,0.0008910493,0.0003455616,0.00003286057,0.00001977521],"category_scores_gemma":[0.00003393616,0.0001406901,0.00008556651,0.0002633108,0.00005122659,0.0007607138,0.0002725435,0.0001353572,0.000002939559],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002135037,"about_ca_system_score_gemma":0.00002594878,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005577234,"about_ca_topic_score_gemma":0.000002077277,"domain_scores_codex":[0.9985378,0.000160271,0.0002384755,0.0005366392,0.0002833653,0.0002433818],"domain_scores_gemma":[0.9991214,0.0002720289,0.0001447537,0.0003279954,0.00006181303,0.00007203683],"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":[0.0003150415,0.0003161041,0.01193953,0.0003189046,0.0001151663,0.00005560843,0.009984448,0.004440296,0.01776477,0.3780967,0.01149708,0.5651563],"study_design_scores_gemma":[0.001096004,0.0003609277,0.02966607,0.00002331799,0.000008123207,0.00003961536,0.0004170127,0.942117,0.0004075734,0.02187674,0.003649203,0.000338468],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0106507,0.00008342216,0.9864667,0.000926545,0.0009820845,0.0002517876,0.000008577736,0.0001695392,0.0004606394],"genre_scores_gemma":[0.8854563,0.000008483382,0.1140293,0.0001995006,0.0001186289,0.00001649296,0.00002140827,0.00001375514,0.000136186],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9376767,"threshold_uncertainty_score":0.8628244,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09468000872548839,"score_gpt":0.3613157230118719,"score_spread":0.2666357142863836,"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."}}