{"id":"https://openalex.org/W4413359197","doi":"https://doi.org/10.1109/tim.2025.3600718","title":"Cognition-Inspired Dynamic Feature Integration Network for RGB-D and RGB-T Salient Object Detection","display_name":"Cognition-Inspired Dynamic Feature Integration Network for RGB-D and RGB-T Salient Object Detection","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4413359197","doi":"https://doi.org/10.1109/tim.2025.3600718"},"language":"en","primary_location":{"id":"doi:10.1109/tim.2025.3600718","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2025.3600718","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Instrumentation and Measurement","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5102005199","display_name":"Huizhi Wang","orcid":"https://orcid.org/0009-0004-5339-2690"},"institutions":[{"id":"https://openalex.org/I109935558","display_name":"Ningbo University","ror":"https://ror.org/03et85d35","country_code":"CN","type":"education","lineage":["https://openalex.org/I109935558"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Huizhi Wang","raw_affiliation_strings":["Faculty of Information Science and Engineering, Ningbo University, Ningbo, China"],"raw_orcid":"https://orcid.org/0009-0004-5339-2690","affiliations":[{"raw_affiliation_string":"Faculty of Information Science and Engineering, Ningbo University, Ningbo, China","institution_ids":["https://openalex.org/I109935558"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007883088","display_name":"Hui Guo","orcid":"https://orcid.org/0000-0003-2126-9757"},"institutions":[{"id":"https://openalex.org/I4210107865","display_name":"Wuzhou University","ror":"https://ror.org/01vv37n49","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210107865"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hui Guo","raw_affiliation_strings":["Guangxi Key Laboratory of Machine Vision and Intelligent Control, Wuzhou University, Wuzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-2126-9757","affiliations":[{"raw_affiliation_string":"Guangxi Key Laboratory of Machine Vision and Intelligent Control, Wuzhou University, Wuzhou, China","institution_ids":["https://openalex.org/I4210107865"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030554236","display_name":"Xiongli Chai","orcid":"https://orcid.org/0000-0002-4245-5391"},"institutions":[{"id":"https://openalex.org/I109935558","display_name":"Ningbo University","ror":"https://ror.org/03et85d35","country_code":"CN","type":"education","lineage":["https://openalex.org/I109935558"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiongli Chai","raw_affiliation_strings":["Faculty of Information Science and Engineering, Ningbo University, Ningbo, China"],"raw_orcid":"https://orcid.org/0000-0002-4245-5391","affiliations":[{"raw_affiliation_string":"Faculty of Information Science and Engineering, Ningbo University, Ningbo, China","institution_ids":["https://openalex.org/I109935558"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021988709","display_name":"Baoyang Mu","orcid":"https://orcid.org/0000-0003-2898-0461"},"institutions":[{"id":"https://openalex.org/I109935558","display_name":"Ningbo University","ror":"https://ror.org/03et85d35","country_code":"CN","type":"education","lineage":["https://openalex.org/I109935558"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Baoyang Mu","raw_affiliation_strings":["Faculty of Information Science and Engineering, Ningbo University, Ningbo, China"],"raw_orcid":"https://orcid.org/0000-0003-2898-0461","affiliations":[{"raw_affiliation_string":"Faculty of Information Science and Engineering, Ningbo University, Ningbo, China","institution_ids":["https://openalex.org/I109935558"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5049898953","display_name":"Feng Shao","orcid":"https://orcid.org/0000-0002-2495-9924"},"institutions":[{"id":"https://openalex.org/I109935558","display_name":"Ningbo University","ror":"https://ror.org/03et85d35","country_code":"CN","type":"education","lineage":["https://openalex.org/I109935558"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feng Shao","raw_affiliation_strings":["Faculty of Information Science and Engineering, Ningbo University, Ningbo, China"],"raw_orcid":"https://orcid.org/0000-0002-2495-9924","affiliations":[{"raw_affiliation_string":"Faculty of Information Science and Engineering, Ningbo University, Ningbo, China","institution_ids":["https://openalex.org/I109935558"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.17204237,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"74","issue":null,"first_page":"1","last_page":"16"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11605","display_name":"Visual Attention and Saliency Detection","score":0.996999979019165,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11605","display_name":"Visual Attention and Saliency Detection","score":0.996999979019165,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12389","display_name":"Infrared Target Detection Methodologies","score":0.9855999946594238,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9575999975204468,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.703254222869873},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.6960323452949524},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6421927809715271},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6293177604675293},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6256635189056396},{"id":"https://openalex.org/keywords/salient","display_name":"Salient","score":0.6195369362831116},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5581562519073486},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5243606567382812},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4904744327068329},{"id":"https://openalex.org/keywords/cognition","display_name":"Cognition","score":0.4534171223640442},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4412558674812317},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.11931028962135315}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.703254222869873},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.6960323452949524},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6421927809715271},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6293177604675293},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6256635189056396},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.6195369362831116},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5581562519073486},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5243606567382812},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4904744327068329},{"id":"https://openalex.org/C169900460","wikidata":"https://www.wikidata.org/wiki/Q2200417","display_name":"Cognition","level":2,"score":0.4534171223640442},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4412558674812317},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.11931028962135315},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C169760540","wikidata":"https://www.wikidata.org/wiki/Q207011","display_name":"Neuroscience","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tim.2025.3600718","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2025.3600718","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Transactions on Instrumentation and Measurement","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.5099999904632568,"id":"https://metadata.un.org/sdg/10"}],"awards":[{"id":"https://openalex.org/G5902057442","display_name":null,"funder_award_id":"2024Z005","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7037312785","display_name":null,"funder_award_id":"62471263","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":71,"referenced_works":["https://openalex.org/W20683899","https://openalex.org/W1772076007","https://openalex.org/W1976409045","https://openalex.org/W1982075130","https://openalex.org/W1993713494","https://openalex.org/W2038913936","https://openalex.org/W2039298799","https://openalex.org/W2100470808","https://openalex.org/W2133059825","https://openalex.org/W2194775991","https://openalex.org/W2295107390","https://openalex.org/W2415053570","https://openalex.org/W2607011617","https://openalex.org/W2752782242","https://openalex.org/W2766315367","https://openalex.org/W2887486131","https://openalex.org/W2945809413","https://openalex.org/W2957414648","https://openalex.org/W2961348656","https://openalex.org/W2963529609","https://openalex.org/W2963749936","https://openalex.org/W3002301267","https://openalex.org/W3039479109","https://openalex.org/W3083388823","https://openalex.org/W3093213431","https://openalex.org/W3126725132","https://openalex.org/W3135874576","https://openalex.org/W3138516171","https://openalex.org/W3151956566","https://openalex.org/W3159018159","https://openalex.org/W3163132162","https://openalex.org/W3164802490","https://openalex.org/W3166092877","https://openalex.org/W3170173308","https://openalex.org/W3176378302","https://openalex.org/W3181580843","https://openalex.org/W3185043317","https://openalex.org/W3203040502","https://openalex.org/W3204197760","https://openalex.org/W3206198586","https://openalex.org/W3207668590","https://openalex.org/W3209732904","https://openalex.org/W3212645988","https://openalex.org/W4205688290","https://openalex.org/W4206420686","https://openalex.org/W4206873376","https://openalex.org/W4214696292","https://openalex.org/W4285058230","https://openalex.org/W4289752563","https://openalex.org/W4290186514","https://openalex.org/W4293731746","https://openalex.org/W4311972917","https://openalex.org/W4312612915","https://openalex.org/W4312699294","https://openalex.org/W4313270788","https://openalex.org/W4315606099","https://openalex.org/W4362496243","https://openalex.org/W4376872192","https://openalex.org/W4382677718","https://openalex.org/W4383890466","https://openalex.org/W4386902928","https://openalex.org/W4387968414","https://openalex.org/W4387969113","https://openalex.org/W4388407729","https://openalex.org/W4391070208","https://openalex.org/W4392188075","https://openalex.org/W4396505586","https://openalex.org/W4399563339","https://openalex.org/W4399988332","https://openalex.org/W4401635021","https://openalex.org/W4406857697"],"related_works":["https://openalex.org/W2329500892","https://openalex.org/W28991112","https://openalex.org/W2370726991","https://openalex.org/W2369710579","https://openalex.org/W4327728159","https://openalex.org/W4394266730","https://openalex.org/W1990856605","https://openalex.org/W4388913932","https://openalex.org/W4309130263","https://openalex.org/W2138022083"],"abstract_inverted_index":{"Salient":[0],"Object":[1],"Detection":[2],"(SOD)":[3],"are":[4,194],"widely":[5],"used":[6],"in":[7,43,72,118,187],"quality":[8],"inspection":[9],"scenarios,":[10],"such":[11],"as":[12],"rail":[13],"surface":[14],"detection.Recent":[15],"studies":[16,82],"have":[17],"proven":[18],"that":[19,96,218],"incorporating":[20],"complementary":[21],"information":[22,205],"like":[23],"depth":[24],"and":[25,56,104,108,135,179,213,225,231],"thermal":[26],"images":[27],"is":[28,154],"conducive":[29],"to":[30,69,112,133,156,206],"SOD.":[31,110],"Effectively":[32],"leveraging":[33],"the":[34,98,115,128,137,144,158,172,181,188],"advantages":[35],"of":[36,143,160],"each":[37,184],"modality":[38,60],"while":[39],"eliminating":[40],"inter-modality":[41],"noise":[42],"multi-level":[44],"fusion":[45],"has":[46,223],"been":[47],"a":[48,90,123,148,197],"research":[49],"hotspot.":[50],"Most":[51],"existing":[52],"works":[53],"use":[54],"convolution":[55],"attention":[57,103],"mechanisms":[58],"for":[59,106,183],"interaction":[61],"but":[62],"overlook":[63],"semantic":[64,164,173,204],"similarity":[65,174],"during":[66],"fusion,":[67],"leading":[68],"poor":[70],"performance":[71,227],"some":[73],"challenging":[74],"scenarios.":[75],"In":[76],"this":[77],"paper,":[78],"inspired":[79],"by":[80],"psychology":[81],"on":[83,211],"human":[84,102],"vision":[85],"system":[86],"(HVS),":[87],"we":[88,120],"propose":[89],"Dynamic":[91,149],"Feature":[92,150],"Integration":[93],"Network":[94],"(DFINet)":[95],"simulates":[97],"correlation":[99],"mechanism":[100,159],"between":[101,175],"semantics":[105],"RGB-D":[107,212],"RGB-T":[109,214],"Specifically,":[111],"better":[113],"capture":[114],"modal-specific":[116,138],"features":[117,139,178,193],"semantics,":[119],"first":[121],"employ":[122],"multi-granularity-based":[124],"pre-segmentation":[125],"method,":[126],"namely":[127],"Pre-segmentation":[129],"Injection":[130],"Module":[131,152],"(PIM),":[132],"enhance":[134],"preserve":[136],"at":[140],"different":[141,176],"layers":[142],"backbone":[145],"network.":[146],"Then,":[147],"Fusion":[151],"(DFFM)":[153],"devised":[155],"simulate":[157],"HVS":[161],"where":[162],"specific":[163],"regions":[165],"gain":[166],"more":[167],"attention.":[168],"This":[169],"module":[170],"evaluates":[171],"modal":[177,185],"determines":[180],"weights":[182],"feature":[186],"fusion.":[189],"The":[190],"encoded":[191],"multi-modal":[192],"fed":[195],"into":[196],"staircase":[198],"decoder":[199],"which":[200],"can":[201],"retain":[202],"deep":[203],"boost":[207],"accuracy.":[208],"Extensive":[209],"experiments":[210],"SOD":[215],"datasets":[216],"validate":[217],"our":[219],"proposed":[220],"cognition-inspired":[221],"framework":[222],"excellent":[224],"competitive":[226],"with":[228],"good":[229],"generalization":[230],"robustness.":[232]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
