{"id":"https://openalex.org/W4405784956","doi":"https://doi.org/10.1109/iros58592.2024.10802230","title":"DCSANet: Dual Cross-channel and Spatial Attention Make RGB-T Object Detection Better","display_name":"DCSANet: Dual Cross-channel and Spatial Attention Make RGB-T Object Detection Better","publication_year":2024,"publication_date":"2024-10-14","ids":{"openalex":"https://openalex.org/W4405784956","doi":"https://doi.org/10.1109/iros58592.2024.10802230"},"language":"en","primary_location":{"id":"doi:10.1109/iros58592.2024.10802230","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros58592.2024.10802230","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","raw_type":"proceedings-article"},"type":"conference-paper","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/A5109762317","display_name":"Xiaoxiong Lan","orcid":null},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoxiong Lan","raw_affiliation_strings":["Sun Yat-Sen University,School of Electronics and Communication Engineering,Shenzhen,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sun Yat-Sen University,School of Electronics and Communication Engineering,Shenzhen,China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114246622","display_name":"Shenghao Liu","orcid":"https://orcid.org/0009-0005-8058-605X"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Shenghao Liu","raw_affiliation_strings":["Sun Yat-Sen University,School of Electronics and Communication Engineering,Shenzhen,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sun Yat-Sen University,School of Electronics and Communication Engineering,Shenzhen,China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5032083248","display_name":"Zhiyong Zhang","orcid":"https://orcid.org/0000-0001-9039-5170"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhiyong Zhang","raw_affiliation_strings":["Sun Yat-Sen University,School of Electronics and Communication Engineering,Shenzhen,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sun Yat-Sen University,School of Electronics and Communication Engineering,Shenzhen,China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5051522890","display_name":"Changzhen Qiu","orcid":"https://orcid.org/0000-0003-4649-1178"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Changzhen Qiu","raw_affiliation_strings":["Sun Yat-Sen University,School of Electronics and Communication Engineering,Shenzhen,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Sun Yat-Sen University,School of Electronics and Communication Engineering,Shenzhen,China","institution_ids":["https://openalex.org/I157773358"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I157773358"],"apc_list":null,"apc_paid":null,"fwci":0.2629,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.59625328,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"12552","last_page":"12558"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9977999925613403,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9977999925613403,"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/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9937999844551086,"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/T12702","display_name":"Brain Tumor Detection and Classification","score":0.9879000186920166,"subfield":{"id":"https://openalex.org/subfields/2808","display_name":"Neurology"},"field":{"id":"https://openalex.org/fields/28","display_name":"Neuroscience"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7245186567306519},{"id":"https://openalex.org/keywords/dual","display_name":"Dual (grammatical number)","score":0.638391375541687},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.5939278602600098},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5622779130935669},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.5571684241294861},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.552248477935791},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5341006517410278},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.5301079750061035},{"id":"https://openalex.org/keywords/computer-graphics","display_name":"Computer graphics (images)","score":0.3310481309890747},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.22551870346069336},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.16196861863136292},{"id":"https://openalex.org/keywords/art","display_name":"Art","score":0.06791803240776062}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7245186567306519},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.638391375541687},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.5939278602600098},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5622779130935669},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.5571684241294861},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.552248477935791},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5341006517410278},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.5301079750061035},{"id":"https://openalex.org/C121684516","wikidata":"https://www.wikidata.org/wiki/Q7600677","display_name":"Computer graphics (images)","level":1,"score":0.3310481309890747},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.22551870346069336},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.16196861863136292},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.06791803240776062},{"id":"https://openalex.org/C124952713","wikidata":"https://www.wikidata.org/wiki/Q8242","display_name":"Literature","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iros58592.2024.10802230","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros58592.2024.10802230","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1536680647","https://openalex.org/W1910108985","https://openalex.org/W2102605133","https://openalex.org/W2120419212","https://openalex.org/W2161969291","https://openalex.org/W2164598857","https://openalex.org/W2193145675","https://openalex.org/W2741620214","https://openalex.org/W2752782242","https://openalex.org/W2913585847","https://openalex.org/W2925287836","https://openalex.org/W2963037989","https://openalex.org/W2963188557","https://openalex.org/W2963351448","https://openalex.org/W2969583230","https://openalex.org/W2984111315","https://openalex.org/W2987131085","https://openalex.org/W3034696777","https://openalex.org/W3036931590","https://openalex.org/W3096831136","https://openalex.org/W3097096317","https://openalex.org/W3116967329","https://openalex.org/W3118570274","https://openalex.org/W3152390103","https://openalex.org/W3213472242","https://openalex.org/W4200631567","https://openalex.org/W4206608625","https://openalex.org/W4289752563","https://openalex.org/W4312594135","https://openalex.org/W4313007055","https://openalex.org/W4320002812","https://openalex.org/W4327785494","https://openalex.org/W4385801610"],"related_works":["https://openalex.org/W2486460843","https://openalex.org/W2168109476","https://openalex.org/W2317351040","https://openalex.org/W1968121071","https://openalex.org/W2061647633","https://openalex.org/W2020254986","https://openalex.org/W2686985752","https://openalex.org/W2952912015","https://openalex.org/W4292830139","https://openalex.org/W4319309705"],"abstract_inverted_index":{"Multimodal":[0],"image":[1],"pairs":[2],"can":[3],"make":[4],"object":[5,14],"detection":[6,15],"more":[7,96],"reliable":[8],"in":[9,146],"challenging":[10],"environments,":[11],"so":[12],"RGB-T":[13],"has":[16],"gained":[17],"extensive":[18],"attention":[19,60,97],"over":[20],"the":[21,26,29,47,59,72,76,92,99,155,158],"past":[22],"decade.":[23],"To":[24],"alleviate":[25,75],"complementarity":[27,73],"of":[28,46,101,120,157],"visible":[30],"and":[31,52,74,82,107,118,128,142],"thermal":[32],"modality,":[33],"we":[34,149],"propose":[35],"a":[36],"novel":[37],"lightweight":[38],"Feature":[39],"Enhancement-fusion":[40,49,54],"Module":[41],"(FEM),":[42],"which":[43],"is":[44,68,88],"composed":[45],"Channel":[48],"Unit":[50,55],"(CEU)":[51],"Spatial":[53],"(SEU)":[56],"by":[57,79,124],"extending":[58],"mechanism":[61],"to":[62,70,90,94,98,113,153],"operate":[63],"on":[64,140,144],"two":[65,125],"modalities.":[66],"CEU":[67],"used":[69],"exploit":[71],"data":[77],"imbalance":[78],"combining":[80],"internal":[81],"global":[83],"channel":[84],"attention.":[85],"Additionally,":[86],"SEU":[87],"utilized":[89],"guide":[91],"model":[93],"pay":[95],"regions":[100],"interest.":[102],"By":[103],"incorporating":[104],"FEM,":[105],"enhanced":[106],"fused":[108],"features":[109],"are":[110,122],"obtained,":[111],"leading":[112],"improved":[114],"performance.":[115],"The":[116],"effectiveness":[117,156],"generalizability":[119],"FEM":[121],"validated":[123],"public":[126],"datasets":[127],"our":[129],"proposed":[130,159],"DCSANet":[131],"achieves":[132],"competitive":[133],"performance":[134],"while":[135],"maintaining":[136],"high":[137],"speed":[138],"(+%7.0":[139],"LLVIP":[141],"+1.2%":[143],"FLIR":[145],"mAP).":[147],"Moreover,":[148],"conducted":[150],"ablation":[151],"experiments":[152],"verify":[154],"operators.":[160]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
