{"id":"https://openalex.org/W4415536798","doi":"https://doi.org/10.1145/3746027.3754572","title":"Spatial-Frequency Mamba Collaborative Learning Network for Infrared Small Target Detection","display_name":"Spatial-Frequency Mamba Collaborative Learning Network for Infrared Small Target Detection","publication_year":2025,"publication_date":"2025-10-25","ids":{"openalex":"https://openalex.org/W4415536798","doi":"https://doi.org/10.1145/3746027.3754572"},"language":null,"primary_location":{"id":"doi:10.1145/3746027.3754572","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746027.3754572","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Multimedia","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/A5070122639","display_name":"Yongji Li","orcid":"https://orcid.org/0000-0002-2212-7673"},"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":"Yongji Li","raw_affiliation_strings":["Sun Yat-sen University, Shenzhen, Guangdong, China"],"raw_orcid":"https://orcid.org/0000-0002-2212-7673","affiliations":[{"raw_affiliation_string":"Sun Yat-sen University, Shenzhen, Guangdong, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100748064","display_name":"Luping Wang","orcid":"https://orcid.org/0000-0003-4119-4799"},"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":"Luping Wang","raw_affiliation_strings":["Sun Yat-sen University, Shenzhen, Guangdong, China and Zhejiang aerospace Runbo measurement and Control Technology Co., Ltd, Hangzhou, China"],"raw_orcid":"https://orcid.org/0000-0003-4119-4799","affiliations":[{"raw_affiliation_string":"Sun Yat-sen University, Shenzhen, Guangdong, China and Zhejiang aerospace Runbo measurement and Control Technology Co., Ltd, Hangzhou, 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":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.37388596,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"5404","last_page":"5412"},"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/T14257","display_name":"Advanced Measurement and Detection Methods","score":0.9955000281333923,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T14158","display_name":"Optical Systems and Laser Technology","score":0.9940999746322632,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/benchmark","display_name":"Benchmark (surveying)","score":0.4553999900817871},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.423799991607666},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.41019999980926514},{"id":"https://openalex.org/keywords/contrast","display_name":"Contrast (vision)","score":0.3614000082015991},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.3278000056743622},{"id":"https://openalex.org/keywords/perspective","display_name":"Perspective (graphical)","score":0.32760000228881836},{"id":"https://openalex.org/keywords/reinforcement-learning","display_name":"Reinforcement learning","score":0.32120001316070557},{"id":"https://openalex.org/keywords/infrared","display_name":"Infrared","score":0.3116999864578247}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.654699981212616},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5361999869346619},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4553999900817871},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.423799991607666},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.41019999980926514},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.40639999508857727},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.3614000082015991},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3278000056743622},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.32760000228881836},{"id":"https://openalex.org/C97541855","wikidata":"https://www.wikidata.org/wiki/Q830687","display_name":"Reinforcement learning","level":2,"score":0.32120001316070557},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.31769999861717224},{"id":"https://openalex.org/C158355884","wikidata":"https://www.wikidata.org/wiki/Q11388","display_name":"Infrared","level":2,"score":0.3116999864578247},{"id":"https://openalex.org/C204241405","wikidata":"https://www.wikidata.org/wiki/Q461499","display_name":"Transformation (genetics)","level":3,"score":0.3073999881744385},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3034999966621399},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.2964000105857849},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2921000123023987},{"id":"https://openalex.org/C2776937971","wikidata":"https://www.wikidata.org/wiki/Q4384217","display_name":"Heading (navigation)","level":2,"score":0.29120001196861267},{"id":"https://openalex.org/C129844170","wikidata":"https://www.wikidata.org/wiki/Q41299","display_name":"Quadratic equation","level":2,"score":0.28949999809265137},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.28600001335144043},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.2777000069618225},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2757999897003174},{"id":"https://openalex.org/C2777052490","wikidata":"https://www.wikidata.org/wiki/Q5072826","display_name":"Chaotic","level":2,"score":0.275299996137619},{"id":"https://openalex.org/C138020889","wikidata":"https://www.wikidata.org/wiki/Q2349659","display_name":"Collaborative learning","level":2,"score":0.2671999931335449},{"id":"https://openalex.org/C45273575","wikidata":"https://www.wikidata.org/wiki/Q578970","display_name":"Spectrogram","level":2,"score":0.2606000006198883},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2596000134944916},{"id":"https://openalex.org/C71813955","wikidata":"https://www.wikidata.org/wiki/Q503560","display_name":"Ground-penetrating radar","level":3,"score":0.2556999921798706}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3746027.3754572","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746027.3754572","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 33rd ACM International Conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G3587745215","display_name":null,"funder_award_id":"2022AIZD0012","funder_id":"https://openalex.org/F4320322162","funder_display_name":"Hangzhou Science and Technology Bureau"}],"funders":[{"id":"https://openalex.org/F4320322162","display_name":"Hangzhou Science and Technology Bureau","ror":"https://ror.org/04gvkax77"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":10,"referenced_works":["https://openalex.org/W2049769803","https://openalex.org/W3086144474","https://openalex.org/W3111390112","https://openalex.org/W3138516171","https://openalex.org/W4205894295","https://openalex.org/W4213019189","https://openalex.org/W4317794940","https://openalex.org/W4396887831","https://openalex.org/W4403792190","https://openalex.org/W4404635233"],"related_works":[],"abstract_inverted_index":{"Infrared":[0,14],"(IR)":[1],"search":[2],"and":[3,11,22,96,101,120,153],"track":[4],"systems":[5],"are":[6,37],"widely":[7],"applied":[8],"in":[9,19],"aerospace":[10],"defense":[12],"fields.":[13],"small":[15,35,92,110,129,148],"target":[16,149],"detection":[17],"(IRSTD)":[18],"heavy":[20],"clouds":[21],"chaotic":[23],"terrestrial":[24],"environments":[25],"remains":[26],"a":[27,64],"challenging":[28],"task.":[29],"The":[30,81,112],"semantic":[31,107],"features":[32,108,122],"of":[33,45,109,128],"IR":[34],"targets":[36,93],"highly":[38],"prone":[39],"to":[40,123,144],"vanishing":[41],"with":[42,49],"the":[43,75,106,125,133,135,146],"addition":[44],"network":[46],"layers.":[47],"Transformer":[48],"quadratic":[50],"computational":[51],"complexity":[52],"struggles":[53],"for":[54],"local":[55],"feature":[56],"refinement.":[57],"To":[58,131],"tackle":[59],"this":[60],"issue,":[61],"we":[62],"introduce":[63],"Mamba-driven":[65],"approach":[66],"dubbed":[67],"Spatial-Frequency":[68],"Mamba":[69,100,103],"Collaborative":[70],"Learning":[71],"Network":[72],"(SMCLNet).":[73],"Specifically,":[74],"perspective":[76],"transformation":[77],"structures":[78],"heterogeneous":[79],"backgrounds.":[80],"reconstructed":[82],"data":[83],"couples":[84],"Mamba's":[85],"flattened":[86],"multidirectional":[87],"scanning":[88],"mechanism.":[89],"Given":[90],"that":[91,157],"possess":[94],"sparse":[95],"high-frequency":[97],"properties,":[98],"spatial":[99,119],"frequency":[102,121],"collaboratively":[104],"enrich":[105],"targets.":[111,130],"Texture":[113],"Enhancement":[114],"Module":[115,138],"(TEM)":[116],"effectively":[117],"fuses":[118],"enhance":[124],"contrast":[126],"information":[127],"refine":[132],"features,":[134],"Fine-Grained":[136],"Reinforcement":[137],"(FRM)":[139],"integrates":[140],"multiple":[141,167],"gradient":[142],"operators":[143],"inscribe":[145],"intact":[147],"profile.":[150],"Both":[151],"qualitative":[152],"quantitative":[154],"experiments":[155],"demonstrate":[156],"our":[158],"proposed":[159],"SMCLNet":[160],"outperforms":[161],"14":[162],"recent":[163],"benchmark":[164],"algorithms":[165],"on":[166],"public":[168],"datasets.":[169]},"counts_by_year":[],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-25T00:00:00"}
