{"id":"W4323668166","doi":"10.1016/j.patcog.2023.109516","title":"A uniform transformer-based structure for feature fusion and enhancement for RGB-D saliency detection","year":2023,"lang":"en","type":"article","venue":"Pattern Recognition","topic":"Visual Attention and Saliency Detection","field":"Computer Science","cited_by":20,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"National Postdoctoral Program for Innovative Talents; National Natural Science Foundation of China","keywords":"RGB color model; Artificial intelligence; Computer science; Feature (linguistics); Pattern recognition (psychology); Benchmark (surveying); Fusion; Transformer; Salient; Computer vision; 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":[0.00026469,0.0005270459,0.0004246491,0.0007143765,0.0002374876,0.0005670568,0.0009572237,0.0004240361,0.005084121],"category_scores_gemma":[0.0007196773,0.0002628741,0.0004708868,0.0008809718,0.0002933362,0.001143749,0.0007892709,0.0005517593,0.001533604],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000360986,"about_ca_system_score_gemma":0.000520212,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001133288,"about_ca_topic_score_gemma":0.001892868,"domain_scores_codex":[0.9998268,0.00001842169,0.00001128893,0.00004754589,0.00007257687,0.00002326941],"domain_scores_gemma":[0.9997746,0.00003404385,0.00002040434,0.00004035318,0.0001109469,0.00001963344],"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":[0.0003749698,0.0001405277,0.0004646445,0.0001857374,0.00005320864,0.00009986901,0.00007236347,0.008392485,0.4310799,0.01116982,0.004758032,0.5432085],"study_design_scores_gemma":[0.00005432375,0.0005546442,0.002116037,0.00003711978,0.0001171883,0.001027044,0.00005423415,0.561913,0.406749,0.00844419,0.01886988,0.00006345357],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005406511,0.0001131406,0.9927658,0.00003531661,0.00003578604,0.00003865824,0.00005954376,0.0005851793,0.0009600216],"genre_scores_gemma":[0.2316676,0.0003025111,0.7630002,0.0001911438,0.00006359407,0.0001043405,0.000356855,0.0001772307,0.004136538],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005084121,"threshold_uncertainty_score":0.01700813,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02990909441917539,"score_gpt":0.2811409532112253,"score_spread":0.2512318587920499,"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."}}