{"id":"https://openalex.org/W7147536758","doi":"https://doi.org/10.1109/icvisp68610.2025.11451746","title":"A Few-Shot Class-Incremental Learning Approach for Multi-Band Radar Target Recognition","display_name":"A Few-Shot Class-Incremental Learning Approach for Multi-Band Radar Target Recognition","publication_year":2025,"publication_date":"2025-11-28","ids":{"openalex":"https://openalex.org/W7147536758","doi":"https://doi.org/10.1109/icvisp68610.2025.11451746"},"language":null,"primary_location":{"id":"doi:10.1109/icvisp68610.2025.11451746","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icvisp68610.2025.11451746","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 9th International Conference on Vision, Image and Signal Processing (ICVISP)","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/A5100430876","display_name":"Xi Zhang","orcid":"https://orcid.org/0000-0003-3415-5345"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xuchen Zhang","raw_affiliation_strings":["Engineering Xidian University,The School of Electronic,Xi&#x2019;an,China"],"affiliations":[{"raw_affiliation_string":"Engineering Xidian University,The School of Electronic,Xi&#x2019;an,China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5106406698","display_name":"Tianyu Wang","orcid":"https://orcid.org/0009-0009-9400-0557"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Tianyu Wang","raw_affiliation_strings":["Engineering Xidian University,The School of Electronic,Xi&#x2019;an,China"],"affiliations":[{"raw_affiliation_string":"Engineering Xidian University,The School of Electronic,Xi&#x2019;an,China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5015795934","display_name":"Xiangyang Li","orcid":"https://orcid.org/0000-0002-2625-1807"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangyang Li","raw_affiliation_strings":["Engineering Xidian University,The School of Electronic,Xi&#x2019;an,China"],"affiliations":[{"raw_affiliation_string":"Engineering Xidian University,The School of Electronic,Xi&#x2019;an,China","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5100430876"],"corresponding_institution_ids":["https://openalex.org/I149594827"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.92725393,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11038","display_name":"Advanced SAR Imaging Techniques","score":0.7339000105857849,"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/T11038","display_name":"Advanced SAR Imaging Techniques","score":0.7339000105857849,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.19130000472068787,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"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/T12131","display_name":"Wireless Signal Modulation Classification","score":0.013100000098347664,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/radar","display_name":"Radar","score":0.6585000157356262},{"id":"https://openalex.org/keywords/automatic-target-recognition","display_name":"Automatic target recognition","score":0.5357999801635742},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5320000052452087},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.4702000021934509},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.45210000872612},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.391400009393692},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3817000091075897},{"id":"https://openalex.org/keywords/joint","display_name":"Joint (building)","score":0.3781999945640564}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.696399986743927},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.6585000157356262},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6294999718666077},{"id":"https://openalex.org/C117623542","wikidata":"https://www.wikidata.org/wiki/Q621974","display_name":"Automatic target recognition","level":3,"score":0.5357999801635742},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5320000052452087},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.4702000021934509},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.45210000872612},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4115000069141388},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.391400009393692},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3817000091075897},{"id":"https://openalex.org/C18555067","wikidata":"https://www.wikidata.org/wiki/Q8375051","display_name":"Joint (building)","level":2,"score":0.3781999945640564},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.37529999017715454},{"id":"https://openalex.org/C10929652","wikidata":"https://www.wikidata.org/wiki/Q7279985","display_name":"Radar imaging","level":3,"score":0.31929999589920044},{"id":"https://openalex.org/C7149132","wikidata":"https://www.wikidata.org/wiki/Q1377840","display_name":"Forgetting","level":2,"score":0.31630000472068787},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.311599999666214},{"id":"https://openalex.org/C2780735816","wikidata":"https://www.wikidata.org/wiki/Q28324931","display_name":"Incremental learning","level":2,"score":0.28700000047683716},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.28619998693466187},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2793999910354614},{"id":"https://openalex.org/C32283439","wikidata":"https://www.wikidata.org/wiki/Q1407014","display_name":"Radar tracker","level":3,"score":0.27230000495910645},{"id":"https://openalex.org/C147345108","wikidata":"https://www.wikidata.org/wiki/Q6693040","display_name":"Low probability of intercept radar","level":5,"score":0.2624000012874603},{"id":"https://openalex.org/C132094186","wikidata":"https://www.wikidata.org/wiki/Q641585","display_name":"Clutter","level":3,"score":0.2549999952316284}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icvisp68610.2025.11451746","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icvisp68610.2025.11451746","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 9th International Conference on Vision, Image and Signal Processing (ICVISP)","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":22,"referenced_works":["https://openalex.org/W2142889010","https://openalex.org/W2520774990","https://openalex.org/W2884282566","https://openalex.org/W2964189064","https://openalex.org/W3035342403","https://openalex.org/W3163842339","https://openalex.org/W3177494822","https://openalex.org/W3186183429","https://openalex.org/W3204291495","https://openalex.org/W4225660138","https://openalex.org/W4226134030","https://openalex.org/W4226420958","https://openalex.org/W4296345598","https://openalex.org/W4306965295","https://openalex.org/W4313005250","https://openalex.org/W4321488352","https://openalex.org/W4363649654","https://openalex.org/W4385196562","https://openalex.org/W4388037272","https://openalex.org/W4389967325","https://openalex.org/W4400680576","https://openalex.org/W4404003056"],"related_works":[],"abstract_inverted_index":{"Under":[0],"few-shot":[1,36,137],"conditions,":[2],"radar":[3,48,124,139],"target":[4,125,140],"recognition":[5,49,141],"models":[6],"often":[7],"suffer":[8],"from":[9],"catastrophic":[10],"forgetting":[11],"and":[12,83,92],"overfitting,":[13],"leading":[14],"to":[15,39,99,108],"severe":[16],"performance":[17],"degradation":[18],"on":[19,120],"previously":[20],"learned":[21],"tasks.":[22,50,142],"To":[23],"address":[24],"these":[25],"challenges":[26],"in":[27,46,136],"datascarce":[28],"scenarios,":[29],"this":[30],"paper":[31],"explores":[32],"the":[33,41,78,85,106,129],"integration":[34],"of":[35,102],"class-incremental":[37,138],"learning":[38,44],"enhance":[40,77],"model\u2019s":[42,79],"continual":[43],"capability":[45],"incremental":[47],"The":[51],"main":[52],"contributions":[53],"are":[54],"as":[55],"follows:":[56],"We":[57,76,95],"design":[58],"an":[59],"embedding":[60],"space":[61,67],"reservation":[62],"mechanism":[63],"that":[64,128],"allocates":[65],"feature":[66,80,86],"for":[68],"newly":[69],"introduced":[70],"classes,":[71,104],"which":[72],"improving":[73],"forward":[74],"compatibility;":[75],"extraction":[81],"capacity":[82],"optimize":[84],"distribution":[87],"through":[88],"joint":[89],"classification":[90],"optimization":[91],"loss":[93],"regularization;":[94],"generate":[96],"virtual":[97],"instances":[98],"simulate":[100],"features":[101],"novel":[103,111],"enabling":[105],"model":[107],"effectively":[109],"predict":[110],"classes":[112],"with":[113],"limited":[114],"samples.":[115],"Finally,":[116],"extensive":[117],"experiments":[118],"conducted":[119],"a":[121],"highfidelity":[122],"multi-band":[123],"datasets":[126],"demonstrate":[127],"proposed":[130],"approach":[131],"significantly":[132],"outperforms":[133],"existing":[134],"methods":[135]},"counts_by_year":[],"updated_date":"2026-04-02T13:53:19.096889","created_date":"2026-04-02T00:00:00"}
