{"id":"W4401748255","doi":"10.1109/access.2024.3448304","title":"SPT-Swin: A Shifted Patch Tokenization Swin Transformer for Image Classification","year":2024,"lang":"en","type":"article","venue":"IEEE Access","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"Athabasca University","funders":"","keywords":"Computer science; Lexical analysis; Artificial intelligence; Transformer; Computer vision; Pattern recognition (psychology); Electrical engineering; Engineering; Voltage","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.000607912,0.0008702173,0.0009200041,0.0006548024,0.0003870467,0.0007534562,0.002467706,0.0005852267,0.005874936],"category_scores_gemma":[0.001724091,0.0003756949,0.0008394783,0.0009668174,0.0006723288,0.002551973,0.001458474,0.00155084,0.00252337],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009330466,"about_ca_system_score_gemma":0.001263658,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005717398,"about_ca_topic_score_gemma":0.007312733,"domain_scores_codex":[0.9996489,0.00006108655,0.00002358774,0.0001227286,0.00008922479,0.00005444085],"domain_scores_gemma":[0.9995543,0.0001113256,0.00004007373,0.0001481565,0.0001022391,0.00004385601],"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":[0.001048396,0.000411524,0.002360208,0.0002817431,0.0001333199,0.0002567121,0.00018645,0.1099316,0.04417561,0.0253751,0.024428,0.7914113],"study_design_scores_gemma":[0.00003326661,0.0001726199,0.0003666163,0.00000984178,0.0000315457,0.000146598,0.00002921565,0.9645137,0.01768669,0.01032076,0.006671083,0.00001811794],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02104182,0.0004373284,0.969963,0.0001907353,0.0001766232,0.0001448696,0.0004456489,0.005358751,0.002241286],"genre_scores_gemma":[0.5961037,0.0007592319,0.3816656,0.0005473898,0.0001651292,0.0004467581,0.003585459,0.001008903,0.01571764],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005874936,"threshold_uncertainty_score":0.01965362,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05227880889700339,"score_gpt":0.3573828564111808,"score_spread":0.3051040475141774,"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."}}