{"id":"https://openalex.org/W4404565361","doi":"https://doi.org/10.1109/tce.2024.3502692","title":"A Dual-Stream Cross-Domain Integration Network for RGB-T Salient Object Detection","display_name":"A Dual-Stream Cross-Domain Integration Network for RGB-T Salient Object Detection","publication_year":2024,"publication_date":"2024-11-20","ids":{"openalex":"https://openalex.org/W4404565361","doi":"https://doi.org/10.1109/tce.2024.3502692"},"language":"en","primary_location":{"id":"doi:10.1109/tce.2024.3502692","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tce.2024.3502692","pdf_url":null,"source":{"id":"https://openalex.org/S126824455","display_name":"IEEE Transactions on Consumer Electronics","issn_l":"0098-3063","issn":["0098-3063","1558-4127"],"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 Consumer Electronics","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/A5073414420","display_name":"Xiaosheng Yu","orcid":"https://orcid.org/0000-0003-3218-1486"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xiaosheng Yu","raw_affiliation_strings":["Faculty of Robot Science and Engineering, Northeastern University, Shenyang, China"],"affiliations":[{"raw_affiliation_string":"Faculty of Robot Science and Engineering, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050985185","display_name":"Xiaowei Cheng","orcid":"https://orcid.org/0000-0001-6544-3556"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiufei Cheng","raw_affiliation_strings":["Faculty of Robot Science and Engineering, Northeastern University, Shenyang, China"],"affiliations":[{"raw_affiliation_string":"Faculty of Robot Science and Engineering, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018818552","display_name":"Yixiu Liu","orcid":"https://orcid.org/0000-0002-4630-3774"},"institutions":[{"id":"https://openalex.org/I50760025","display_name":"Hangzhou Dianzi University","ror":"https://ror.org/0576gt767","country_code":"CN","type":"education","lineage":["https://openalex.org/I50760025"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yixiu Liu","raw_affiliation_strings":["School of Cyberspace, Hangzhou Dianzi University, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"School of Cyberspace, Hangzhou Dianzi University, Hangzhou, China","institution_ids":["https://openalex.org/I50760025"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102521298","display_name":"Zhigao Zheng","orcid":null},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhigao Zheng","raw_affiliation_strings":["School of Computer Science, Wuhan University, Wuhan, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Wuhan University, Wuhan, China","institution_ids":["https://openalex.org/I37461747"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5073414420"],"corresponding_institution_ids":["https://openalex.org/I9224756"],"apc_list":null,"apc_paid":null,"fwci":0.7799,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.74316887,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":96,"max":97},"biblio":{"volume":"71","issue":"1","first_page":"883","last_page":"894"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9983000159263611,"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":0.9983000159263611,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9488999843597412,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T12389","display_name":"Infrared Target Detection Methodologies","score":0.947700023651123,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5864918231964111},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.4420657753944397},{"id":"https://openalex.org/keywords/dual","display_name":"Dual (grammatical number)","score":0.43852710723876953},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.42961937189102173},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4248691499233246},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.42238643765449524},{"id":"https://openalex.org/keywords/computer-graphics","display_name":"Computer graphics (images)","score":0.333426296710968},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.20666208863258362}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5864918231964111},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.4420657753944397},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.43852710723876953},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.42961937189102173},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4248691499233246},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.42238643765449524},{"id":"https://openalex.org/C121684516","wikidata":"https://www.wikidata.org/wiki/Q7600677","display_name":"Computer graphics (images)","level":1,"score":0.333426296710968},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.20666208863258362},{"id":"https://openalex.org/C124952713","wikidata":"https://www.wikidata.org/wiki/Q8242","display_name":"Literature","level":1,"score":0.0},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tce.2024.3502692","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tce.2024.3502692","pdf_url":null,"source":{"id":"https://openalex.org/S126824455","display_name":"IEEE Transactions on Consumer Electronics","issn_l":"0098-3063","issn":["0098-3063","1558-4127"],"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 Consumer Electronics","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1437585164","display_name":null,"funder_award_id":"62206083","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":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":59,"referenced_works":["https://openalex.org/W1982075130","https://openalex.org/W1994922096","https://openalex.org/W2098702446","https://openalex.org/W2100470808","https://openalex.org/W2132083787","https://openalex.org/W2194775991","https://openalex.org/W2780861787","https://openalex.org/W2798807298","https://openalex.org/W2887486131","https://openalex.org/W2942049721","https://openalex.org/W2943563866","https://openalex.org/W2945809413","https://openalex.org/W2963529609","https://openalex.org/W2963868681","https://openalex.org/W2996575194","https://openalex.org/W3002301267","https://openalex.org/W3034185160","https://openalex.org/W3039479109","https://openalex.org/W3092564080","https://openalex.org/W3138516171","https://openalex.org/W3159018159","https://openalex.org/W3177040887","https://openalex.org/W3185043317","https://openalex.org/W3196444763","https://openalex.org/W3198062544","https://openalex.org/W3203605797","https://openalex.org/W3212645988","https://openalex.org/W4206282044","https://openalex.org/W4206715824","https://openalex.org/W4285058230","https://openalex.org/W4293680532","https://openalex.org/W4293731746","https://openalex.org/W4295046720","https://openalex.org/W4312258849","https://openalex.org/W4312443924","https://openalex.org/W4312560303","https://openalex.org/W4312699294","https://openalex.org/W4312828305","https://openalex.org/W4315606099","https://openalex.org/W4316661260","https://openalex.org/W4319879007","https://openalex.org/W4322707121","https://openalex.org/W4376872192","https://openalex.org/W4385195123","https://openalex.org/W4386075642","https://openalex.org/W4386158864","https://openalex.org/W4386319016","https://openalex.org/W4390874114","https://openalex.org/W4391992568","https://openalex.org/W4392346680","https://openalex.org/W4393156599","https://openalex.org/W4394593189","https://openalex.org/W4399697096","https://openalex.org/W4400579361","https://openalex.org/W6637373629","https://openalex.org/W6681583683","https://openalex.org/W6780688020","https://openalex.org/W6784333009","https://openalex.org/W6797578546"],"related_works":["https://openalex.org/W2317351040","https://openalex.org/W2952466936","https://openalex.org/W2737719445","https://openalex.org/W1988622314","https://openalex.org/W2393949104","https://openalex.org/W3046201198","https://openalex.org/W4293061921","https://openalex.org/W4239098401","https://openalex.org/W4292830139","https://openalex.org/W4319309705"],"abstract_inverted_index":{"RGB-T":[0],"salient":[1],"object":[2],"detection":[3,8],"enhances":[4],"the":[5,36,52,67,74,85,104,122,130,142,160,164,169,183],"performance":[6],"of":[7,39,88,124],"in":[9,51,136],"complex":[10,137],"scenes":[11],"by":[12],"integrating":[13],"RGB":[14,89],"and":[15,41,48,73,90,114,126,134,147,175],"thermal":[16,91],"data,":[17],"but":[18],"effective":[19,37],"fusion":[20,60],"remains":[21],"challenging.":[22],"To":[23],"this":[24],"end,":[25],"we":[26,65,150],"propose":[27],"a":[28,152,172],"dual-stream":[29],"cross-domain":[30],"integration":[31,38,54,71,78,82,87],"network":[32],"(DSCDNet),":[33],"which":[34,117],"explores":[35],"spatial":[40,68,105,119,146],"frequency":[42,75,148],"domain":[43,69,76,154],"features,":[44],"demonstrating":[45],"remarkable":[46],"accuracy":[47],"stability.":[49],"Specifically,":[50],"bimodal":[53,70,77],"stage,":[55],"to":[56,140,158],"deeply":[57],"extract":[58],"high-level":[59],"information":[61],"from":[62,93,121],"multiple":[63,94],"modalities,":[64],"introduce":[66],"module":[72,157],"module.":[79],"This":[80],"parallel":[81],"strategy":[83,102],"facilitates":[84],"deep":[86],"image":[92],"dimensions.":[95],"After":[96],"that,":[97],"our":[98],"proposed":[99,184],"feature":[100,155],"decomposition":[101],"decomposes":[103],"fused":[106],"features":[107,120],"into":[108],"two":[109,165],"streams:":[110],"region":[111],"perception":[112],"stream":[113],"detail-aware":[115],"stream,":[116],"interpret":[118],"perspectives":[123],"regional":[125],"detail":[127],"understanding,":[128],"making":[129],"model":[131,170],"more":[132,173],"flexible":[133],"efficient":[135],"scenes.":[138],"Further,":[139],"maximize":[141],"complementary":[143],"advantages":[144],"between":[145,163],"domain,":[149],"design":[151],"cross":[153],"alignment":[156],"facilitate":[159],"interactive":[161],"learning":[162],"domains,":[166],"thereby":[167],"providing":[168],"with":[171],"comprehensive":[174],"enriched":[176],"representation":[177],"perspective.":[178],"Experimental":[179],"results":[180],"demonstrate":[181],"that":[182],"DSCDNet":[185],"outperforms":[186],"11":[187],"state-of-the-art":[188],"(SOTA)":[189],"methods.":[190]},"counts_by_year":[{"year":2025,"cited_by_count":3}],"updated_date":"2026-03-09T08:58:05.943551","created_date":"2025-10-10T00:00:00"}