{"id":"https://openalex.org/W4402916146","doi":"https://doi.org/10.1109/cvprw63382.2024.00305","title":"DaFF: Dual Attentive Feature Fusion for Multispectral Pedestrian Detection","display_name":"DaFF: Dual Attentive Feature Fusion for Multispectral Pedestrian Detection","publication_year":2024,"publication_date":"2024-06-17","ids":{"openalex":"https://openalex.org/W4402916146","doi":"https://doi.org/10.1109/cvprw63382.2024.00305"},"language":"en","primary_location":{"id":"doi:10.1109/cvprw63382.2024.00305","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvprw63382.2024.00305","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","raw_type":"proceedings-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/A5007109508","display_name":"Afnan Althoupety","orcid":null},"institutions":[{"id":"https://openalex.org/I126345244","display_name":"Portland State University","ror":"https://ror.org/00yn2fy02","country_code":"US","type":"education","lineage":["https://openalex.org/I126345244"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Afnan Althoupety","raw_affiliation_strings":["Portland State University,USA"],"affiliations":[{"raw_affiliation_string":"Portland State University,USA","institution_ids":["https://openalex.org/I126345244"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100760596","display_name":"Liyun Wang","orcid":"https://orcid.org/0000-0003-4288-2569"},"institutions":[{"id":"https://openalex.org/I126345244","display_name":"Portland State University","ror":"https://ror.org/00yn2fy02","country_code":"US","type":"education","lineage":["https://openalex.org/I126345244"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Li-Yun Wang","raw_affiliation_strings":["Portland State University,USA"],"affiliations":[{"raw_affiliation_string":"Portland State University,USA","institution_ids":["https://openalex.org/I126345244"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100859961","display_name":"Wu\u2010chi Feng","orcid":"https://orcid.org/0000-0002-5267-8281"},"institutions":[{"id":"https://openalex.org/I126345244","display_name":"Portland State University","ror":"https://ror.org/00yn2fy02","country_code":"US","type":"education","lineage":["https://openalex.org/I126345244"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Wu-Chi Feng","raw_affiliation_strings":["Portland State University,USA"],"affiliations":[{"raw_affiliation_string":"Portland State University,USA","institution_ids":["https://openalex.org/I126345244"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5083774234","display_name":"Banafsheh Rekabdar","orcid":null},"institutions":[{"id":"https://openalex.org/I126345244","display_name":"Portland State University","ror":"https://ror.org/00yn2fy02","country_code":"US","type":"education","lineage":["https://openalex.org/I126345244"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Banafsheh Rekabdar","raw_affiliation_strings":["Portland State University,USA"],"affiliations":[{"raw_affiliation_string":"Portland State University,USA","institution_ids":["https://openalex.org/I126345244"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5007109508"],"corresponding_institution_ids":["https://openalex.org/I126345244"],"apc_list":null,"apc_paid":null,"fwci":3.5012,"has_fulltext":false,"cited_by_count":14,"citation_normalized_percentile":{"value":0.94026053,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":99,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"2997","last_page":"3006"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9922000169754028,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.9922000169754028,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9681000113487244,"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/T13282","display_name":"Automated Road and Building Extraction","score":0.9609000086784363,"subfield":{"id":"https://openalex.org/subfields/2212","display_name":"Ocean 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/multispectral-image","display_name":"Multispectral image","score":0.8633826375007629},{"id":"https://openalex.org/keywords/pedestrian-detection","display_name":"Pedestrian detection","score":0.8353794813156128},{"id":"https://openalex.org/keywords/dual","display_name":"Dual (grammatical number)","score":0.6543675065040588},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6313223838806152},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5769467949867249},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5561410784721375},{"id":"https://openalex.org/keywords/pedestrian","display_name":"Pedestrian","score":0.5235688090324402},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.49419787526130676},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.4645644724369049},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.4469517469406128},{"id":"https://openalex.org/keywords/image-fusion","display_name":"Image fusion","score":0.4233746826648712},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3908713161945343},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.14722269773483276},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.06753882765769958},{"id":"https://openalex.org/keywords/art","display_name":"Art","score":0.0612492561340332}],"concepts":[{"id":"https://openalex.org/C173163844","wikidata":"https://www.wikidata.org/wiki/Q1761440","display_name":"Multispectral image","level":2,"score":0.8633826375007629},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.8353794813156128},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.6543675065040588},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6313223838806152},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5769467949867249},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5561410784721375},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.5235688090324402},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.49419787526130676},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.4645644724369049},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.4469517469406128},{"id":"https://openalex.org/C69744172","wikidata":"https://www.wikidata.org/wiki/Q860822","display_name":"Image fusion","level":3,"score":0.4233746826648712},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3908713161945343},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.14722269773483276},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.06753882765769958},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0612492561340332},{"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/C124952713","wikidata":"https://www.wikidata.org/wiki/Q8242","display_name":"Literature","level":1,"score":0.0},{"id":"https://openalex.org/C22212356","wikidata":"https://www.wikidata.org/wiki/Q775325","display_name":"Transport engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cvprw63382.2024.00305","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvprw63382.2024.00305","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.49000000953674316,"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":42,"referenced_works":["https://openalex.org/W1539811621","https://openalex.org/W1910108985","https://openalex.org/W2741620214","https://openalex.org/W2743197123","https://openalex.org/W2752782242","https://openalex.org/W2922509574","https://openalex.org/W2962858109","https://openalex.org/W2963188557","https://openalex.org/W2963579094","https://openalex.org/W2965096309","https://openalex.org/W2980088508","https://openalex.org/W2982220924","https://openalex.org/W2987131085","https://openalex.org/W3034552520","https://openalex.org/W3035588244","https://openalex.org/W3035750252","https://openalex.org/W3036931590","https://openalex.org/W3116967329","https://openalex.org/W3118570274","https://openalex.org/W3174525637","https://openalex.org/W3188511781","https://openalex.org/W3204166336","https://openalex.org/W3213472242","https://openalex.org/W3214586131","https://openalex.org/W3215899623","https://openalex.org/W4220893768","https://openalex.org/W4281701185","https://openalex.org/W4292787334","https://openalex.org/W4304080362","https://openalex.org/W4312380001","https://openalex.org/W4313007055","https://openalex.org/W4323057734","https://openalex.org/W4385245566","https://openalex.org/W4386189887","https://openalex.org/W6753836424","https://openalex.org/W6755207826","https://openalex.org/W6756834165","https://openalex.org/W6763701032","https://openalex.org/W6768021236","https://openalex.org/W6769627184","https://openalex.org/W6784333009","https://openalex.org/W6845935626"],"related_works":["https://openalex.org/W4318664220","https://openalex.org/W2771047279","https://openalex.org/W4388409104","https://openalex.org/W2124951708","https://openalex.org/W3132270449","https://openalex.org/W4377289091","https://openalex.org/W2972620127","https://openalex.org/W3013647784","https://openalex.org/W2981141433","https://openalex.org/W2997281059"],"abstract_inverted_index":{"Inspired":[0],"by":[1],"how":[2],"humans":[3],"perceive":[4],"and":[5,23,57,91,120,139,175],"interpret":[6],"the":[7,42,63,80,83,87,97,105,134,137,145,162,170,184],"world":[8],"using":[9],"multiple":[10,18],"senses,":[11],"multi-modal":[12,113],"learning":[13,39],"involves":[14],"integrating":[15],"information":[16,95],"from":[17,118],"modalities":[19,177],"to":[20,36,40,96,110,182],"improve":[21],"understanding":[22],"performance":[24],"in":[25,46,115,188],"various":[26],"tasks.":[27],"Aligning":[28],"with":[29],"that":[30,54,168],"notion,":[31],"our":[32],"key":[33],"intuition":[34],"is":[35,73,178],"utilize":[37],"multi-model":[38],"solve":[41],"domain":[43,85,185],"shift":[44,186],"problem":[45,187],"night-time":[47],"pedestrian":[48,66,156,189],"detection.In":[49],"this":[50,71],"paper,":[51],"we":[52,149],"show":[53],"pairing":[55],"RGB":[56,98,119,174],"infrared":[58],"(IR)":[59],"image":[60],"features":[61],"increases":[62],"robustness":[64],"of":[65,79,107,136,147,164,173],"detection":[67],"at":[68,103],"night.":[69],"Indeed,":[70],"solution":[72],"unbiased":[74],"towards":[75],"a":[76,112],"specific":[77],"time":[78],"day":[81],"as":[82,93],"IR":[84,121,176],"reduces":[86],"reliance":[88],"on":[89,152],"lighting":[90],"serves":[92],"complementary":[94,171],"domain.":[99],"Our":[100,123],"work":[101],"aims":[102],"exploiting":[104],"power":[106],"attention":[108],"mechanisms":[109],"guide":[111],"framework":[114],"feature":[116,130],"fusing":[117],"modalities.":[122],"novel":[124],"fusion":[125,131],"approach,":[126],"named":[127],"dual":[128],"attentive":[129],"(DaFF),":[132],"leverages":[133],"duality":[135],"transformer":[138],"channel-wise":[140],"global":[141],"attentions.":[142],"To":[143],"demonstrate":[144],"effectiveness":[146],"DaFF,":[148],"conducted":[150],"experiments":[151],"two":[153],"real-world":[154],"multispectral":[155],"datasets.":[157],"Extensive":[158],"experimental":[159],"results":[160],"reveal":[161],"superiority":[163],"DaFF.":[165],"We":[166],"believe":[167],"combining":[169],"properties":[172],"an":[179],"effective":[180],"remedy":[181],"mitigate":[183],"detection.":[190]},"counts_by_year":[{"year":2026,"cited_by_count":3},{"year":2025,"cited_by_count":11}],"updated_date":"2026-03-27T14:29:43.386196","created_date":"2025-10-10T00:00:00"}