{"id":"https://openalex.org/W4387925332","doi":"https://doi.org/10.1145/3607834.3616569","title":"CDMNet: Contrastive Distribution Mapped Network for Infrared Small Target Detection","display_name":"CDMNet: Contrastive Distribution Mapped Network for Infrared Small Target Detection","publication_year":2023,"publication_date":"2023-10-25","ids":{"openalex":"https://openalex.org/W4387925332","doi":"https://doi.org/10.1145/3607834.3616569"},"language":"en","primary_location":{"id":"doi:10.1145/3607834.3616569","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3607834.3616569","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 Workshop on UAVs in Multimedia: Capturing the World from a New Perspective","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/A5056240323","display_name":"Chengtao Lv","orcid":"https://orcid.org/0000-0001-9599-6557"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chengtao Lv","raw_affiliation_strings":["Beihang University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-9599-6557","affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039703932","display_name":"Jinyang Guo","orcid":"https://orcid.org/0000-0003-1956-3367"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jinyang Guo","raw_affiliation_strings":["Beihang University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-1956-3367","affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Jiaqi Yu","orcid":"https://orcid.org/0000-0003-1956-3367"},"institutions":[{"id":"https://openalex.org/I202334528","display_name":"Beijing Electronic Science and Technology Institute","ror":"https://ror.org/01xdzh226","country_code":"CN","type":"education","lineage":["https://openalex.org/I202334528"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiaqi Yu","raw_affiliation_strings":["Beijing Institute of Control and Electronic Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-1956-3367","affiliations":[{"raw_affiliation_string":"Beijing Institute of Control and Electronic Technology, Beijing, China","institution_ids":["https://openalex.org/I202334528"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Ruiyan Zhang","orcid":"https://orcid.org/0000-0003-1956-3367"},"institutions":[{"id":"https://openalex.org/I202334528","display_name":"Beijing Electronic Science and Technology Institute","ror":"https://ror.org/01xdzh226","country_code":"CN","type":"education","lineage":["https://openalex.org/I202334528"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ruiyan Zhang","raw_affiliation_strings":["Beijing Institute of Control and Electronic Technology, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0003-1956-3367","affiliations":[{"raw_affiliation_string":"Beijing Institute of Control and Electronic Technology, Beijing, China","institution_ids":["https://openalex.org/I202334528"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5024067284","display_name":"Xianglong Liu","orcid":"https://orcid.org/0000-0001-8425-4195"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xianglong Liu","raw_affiliation_strings":["Beihang University, Beijing, China"],"raw_orcid":"https://orcid.org/0000-0001-8425-4195","affiliations":[{"raw_affiliation_string":"Beihang University, Beijing, China","institution_ids":["https://openalex.org/I82880672"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5056240323"],"corresponding_institution_ids":["https://openalex.org/I82880672"],"apc_list":null,"apc_paid":null,"fwci":0.811,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.85331772,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"63","last_page":"67"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12389","display_name":"Infrared Target Detection Methodologies","score":0.9998999834060669,"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"}},"topics":[{"id":"https://openalex.org/T12389","display_name":"Infrared Target Detection Methodologies","score":0.9998999834060669,"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/T11856","display_name":"Thermography and Photoacoustic Techniques","score":0.9945999979972839,"subfield":{"id":"https://openalex.org/subfields/2211","display_name":"Mechanics of Materials"},"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.991100013256073,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8035460710525513},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5970376133918762},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5796248912811279},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5640884041786194},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.5256180167198181},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.5031699538230896},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4232054352760315},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4213102459907532},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.4209066331386566},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.41628536581993103},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.34393608570098877},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.32770341634750366},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.23356327414512634}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8035460710525513},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5970376133918762},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5796248912811279},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5640884041786194},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.5256180167198181},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.5031699538230896},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4232054352760315},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4213102459907532},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.4209066331386566},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.41628536581993103},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.34393608570098877},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.32770341634750366},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.23356327414512634},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3607834.3616569","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3607834.3616569","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2023 Workshop on UAVs in Multimedia: Capturing the World from a New Perspective","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":42,"referenced_works":["https://openalex.org/W1536680647","https://openalex.org/W1974872009","https://openalex.org/W1978993121","https://openalex.org/W2041560658","https://openalex.org/W2041888557","https://openalex.org/W2051185466","https://openalex.org/W2060449488","https://openalex.org/W2080197848","https://openalex.org/W2341998679","https://openalex.org/W2407220925","https://openalex.org/W2544366167","https://openalex.org/W2560311620","https://openalex.org/W2578504242","https://openalex.org/W2604768956","https://openalex.org/W2780449407","https://openalex.org/W2900678331","https://openalex.org/W2912919760","https://openalex.org/W2991758591","https://openalex.org/W2998715470","https://openalex.org/W3028151507","https://openalex.org/W3036968235","https://openalex.org/W3039443125","https://openalex.org/W3088317060","https://openalex.org/W3103897295","https://openalex.org/W3109340983","https://openalex.org/W3118249006","https://openalex.org/W3118934234","https://openalex.org/W3122412340","https://openalex.org/W3157653192","https://openalex.org/W3165625783","https://openalex.org/W3171950886","https://openalex.org/W3172615411","https://openalex.org/W3194423521","https://openalex.org/W3195975500","https://openalex.org/W4226043641","https://openalex.org/W4234552385","https://openalex.org/W4286630479","https://openalex.org/W4287600707","https://openalex.org/W4287755086","https://openalex.org/W4288391464","https://openalex.org/W4313065862","https://openalex.org/W4377710209"],"related_works":["https://openalex.org/W2366107444","https://openalex.org/W4388145910","https://openalex.org/W2381570729","https://openalex.org/W1976205134","https://openalex.org/W4248336175","https://openalex.org/W2031260042","https://openalex.org/W2391445434","https://openalex.org/W3009369890","https://openalex.org/W4312490297","https://openalex.org/W2062212388"],"abstract_inverted_index":{"Single-frame":[0],"infrared":[1,87,151],"small":[2,88,152],"target":[3,153],"(SIRST)":[4],"detection":[5,132,139],"is":[6],"an":[7],"extremely":[8],"challenging":[9],"task":[10],"due":[11],"to":[12,23,79,134],"its":[13],"low":[14,18],"signal-to-noise":[15],"ratio":[16],"and":[17,34,54,125,156],"contrast.":[19],"Previous":[20],"methods":[21,92],"fail":[22],"achieve":[24],"promising":[25],"performance":[26],"as":[27],"they":[28],"do":[29],"not":[30],"consider":[31],"the":[32,51,55,62,68,77,84,94,116,122,129],"analogous":[33],"blurred":[35],"background":[36],"surrounding.":[37],"To":[38],"this":[39],"end,":[40],"we":[41,105],"first":[42],"propose":[43],"a":[44,60,107,136,167],"prototype-based":[45],"contrastive":[46],"loss":[47],"(PCL)":[48],"by":[49,98],"modeling":[50],"foreground":[52],"targets":[53],"surrounding":[56],"nearest":[57],"backgrounds.":[58],"As":[59],"result,":[61],"prototypes":[63],"of":[64,86,169],"different":[65],"categories":[66],"in":[67,101],"latent":[69],"space":[70],"could":[71],"be":[72],"far":[73],"away,":[74],"which":[75,113],"enables":[76],"model":[78,161],"make":[80],"clear":[81],"decisions":[82],"on":[83,149,166],"boundaries":[85],"targets.":[89],"Moreover,":[90],"previous":[91],"neglect":[93],"distribution":[95,117],"inconsistency":[96,118],"caused":[97],"feature":[99],"fusion":[100,110],"U-shaped":[102],"architecture.":[103],"Therefore,":[104],"design":[106],"multi-scale":[108],"distribution-mapped":[109],"(MDMF)":[111],"module,":[112],"greatly":[114],"mitigates":[115],"issue.":[119],"We":[120],"incorporate":[121],"proposed":[123],"PCL":[124],"MDMF":[126],"module":[127],"into":[128],"existing":[130],"SIRST":[131,138],"method":[133],"construct":[135],"new":[137],"framework":[140],"called":[141],"Contrastive":[142],"Distribution":[143],"Mapped":[144],"Network":[145],"(CDMNet).":[146],"Extensive":[147],"experiments":[148],"two":[150],"datasets,":[154],"NUDT-SIRST":[155],"IRSTD-1k,":[157],"demonstrate":[158],"that":[159],"our":[160],"outperforms":[162],"current":[163],"competitive":[164],"models":[165],"variety":[168],"metrics.":[170]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2026-04-29T09:16:38.111599","created_date":"2025-10-10T00:00:00"}
