{"id":"W4385805019","doi":"10.1109/cvprw59228.2023.00177","title":"SwinFSR: Stereo Image Super-Resolution using SwinIR and Frequency Domain Knowledge","year":2023,"lang":"en","type":"article","venue":"","topic":"Advanced Image Processing Techniques","field":"Computer Science","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Computer science; Artificial intelligence; Residual; Computer vision; Stereo image; Frequency domain; Feature extraction; Convolution (computer science); Transformer; Image (mathematics); Algorithm; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007097803,0.0008571325,0.0007784553,0.001014467,0.0002426466,0.0005254914,0.001188664,0.000754493,0.00289792],"category_scores_gemma":[0.001300141,0.000377416,0.0008085743,0.0007012816,0.0004402039,0.001432956,0.001425156,0.0008637037,0.001161082],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002995384,"about_ca_system_score_gemma":0.0007392386,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001756087,"about_ca_topic_score_gemma":0.003400951,"domain_scores_codex":[0.9995727,0.00005818956,0.00002161591,0.00008362157,0.0002142826,0.00004968798],"domain_scores_gemma":[0.9995427,0.0001061328,0.00006232219,0.0001297381,0.0001272223,0.00003182445],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0003109646,0.0001277675,0.000657591,0.0002765612,0.0001237429,0.0002021053,0.0001414097,0.04154802,0.1577229,0.009423905,0.005194391,0.7842706],"study_design_scores_gemma":[0.00003235774,0.0001576447,0.001068651,0.00002804836,0.00006955189,0.0007290644,0.00003872135,0.8950934,0.08698393,0.005717293,0.01003894,0.00004242185],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007130241,0.0002652536,0.9900895,0.00005695362,0.00002844908,0.00003889,0.0000584811,0.001014194,0.00131811],"genre_scores_gemma":[0.1175967,0.0004343595,0.8777239,0.000241739,0.00007028375,0.00007506895,0.0004374171,0.0002495266,0.003171114],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00289792,"threshold_uncertainty_score":0.009694517,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.031726417406401,"score_gpt":0.3209651585220337,"score_spread":0.2892387411156327,"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."}}