{"id":"W4409367117","doi":"10.1609/aaai.v39i7.32838","title":"Alignment-Free RGB-T Salient Object Detection: A Large-Scale Dataset and Progressive Correlation Network","year":2025,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Natural Science Foundation of Anhui Province; National Natural Science Foundation of China","keywords":"Scale (ratio); Correlation; Artificial intelligence; Salient; Computer science; RGB color model; Pattern recognition (psychology); Object (grammar); Computer vision; Cartography; Geography; Mathematics; Geometry","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":[0.0005590448,0.0001909174,0.000198954,0.000128488,0.0004346806,0.0002967659,0.001085413,0.00009682614,0.00002843063],"category_scores_gemma":[0.0002136868,0.0001508143,0.00007145962,0.0009050664,0.0001679382,0.0004280089,0.0006749327,0.0002369917,0.00002453286],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005189577,"about_ca_system_score_gemma":0.00005333732,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000176696,"about_ca_topic_score_gemma":0.00003661218,"domain_scores_codex":[0.998261,0.00003442276,0.0004658847,0.0005266668,0.0003954856,0.00031655],"domain_scores_gemma":[0.9988497,0.00005446609,0.0003221429,0.0003806585,0.0003257512,0.00006732034],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001350426,0.0003507272,0.00165397,0.00008403954,0.00004839903,9.138094e-7,0.001405088,0.0002266505,0.01097897,0.8664436,0.003405302,0.1152673],"study_design_scores_gemma":[0.0001769945,0.0005282608,0.002590056,0.000544425,0.00005356229,0.00001223455,0.001237194,0.3960664,0.2448977,0.3518721,0.001620545,0.0004005484],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1688193,0.000219782,0.8062614,0.008211203,0.003843274,0.002078709,0.00008421635,0.0003878036,0.01009435],"genre_scores_gemma":[0.9977703,0.00002789392,0.001439356,0.0004213039,0.00006008329,0.00005545181,0.000004217259,0.000006179355,0.0002152007],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.828951,"threshold_uncertainty_score":0.615003,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03036189820820484,"score_gpt":0.2965295280216453,"score_spread":0.2661676298134405,"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."}}