{"id":"https://openalex.org/W7165675281","doi":"https://doi.org/10.48550/arxiv.2606.21956","title":"Denoising-Enhanced Coarse-to-Fine Infrared Small Target Detection with Attention Prior-Guided Knowledge Distillation","display_name":"Denoising-Enhanced Coarse-to-Fine Infrared Small Target Detection with Attention Prior-Guided Knowledge Distillation","publication_year":2026,"publication_date":"2026-06-20","ids":{"openalex":"https://openalex.org/W7165675281","doi":"https://doi.org/10.48550/arxiv.2606.21956"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.21956","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.21956","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.21956","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5032109527","display_name":"Houzhang Fang","orcid":"https://orcid.org/0000-0002-7949-8846"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Fang, Houzhang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039554378","display_name":"Rui Huang","orcid":"https://orcid.org/0000-0001-6820-2440"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huang, Ruixuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023222077","display_name":"Q.Y. Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Qiuhuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139150369","display_name":"Xiaolin Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Xiaolin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139141495","display_name":"Yi Chang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chang, Yi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139128421","display_name":"Luxin Yan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yan, Luxin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12389","display_name":"Infrared Target Detection Methodologies","score":0.9138000011444092,"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":0.9138000011444092,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.034699998795986176,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.003700000001117587,"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/discriminative-model","display_name":"Discriminative model","score":0.6628000140190125},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6299999952316284},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.6133999824523926},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5253999829292297},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.48030000925064087},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.435699999332428},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4327000081539154},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.4235000014305115},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.3833000063896179}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7659000158309937},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6865000128746033},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.6628000140190125},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6299999952316284},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.6133999824523926},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5253999829292297},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.48030000925064087},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.435699999332428},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4327000081539154},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.4235000014305115},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.3833000063896179},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.3833000063896179},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3449000120162964},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.3418999910354614},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.3328999876976013},{"id":"https://openalex.org/C163294075","wikidata":"https://www.wikidata.org/wiki/Q581861","display_name":"Noise reduction","level":2,"score":0.32429999113082886},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.32350000739097595},{"id":"https://openalex.org/C66905080","wikidata":"https://www.wikidata.org/wiki/Q17005494","display_name":"Binary classification","level":3,"score":0.31119999289512634},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3075999915599823},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.30480000376701355},{"id":"https://openalex.org/C32022120","wikidata":"https://www.wikidata.org/wiki/Q797225","display_name":"Interference (communication)","level":3,"score":0.29750001430511475},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.290800005197525},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2822999954223633},{"id":"https://openalex.org/C2993807640","wikidata":"https://www.wikidata.org/wiki/Q103709453","display_name":"Attention network","level":2,"score":0.25690001249313354},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.2563000023365021},{"id":"https://openalex.org/C126422989","wikidata":"https://www.wikidata.org/wiki/Q93586","display_name":"Feature detection (computer vision)","level":4,"score":0.2549999952316284},{"id":"https://openalex.org/C190839683","wikidata":"https://www.wikidata.org/wiki/Q2448197","display_name":"Train","level":2,"score":0.2540000081062317},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2522999942302704}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.21956","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.21956","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.21956","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.21956","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.6542206406593323,"id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Infrared":[0],"small":[1,33,85,222],"target":[2,65,86,164,177,212],"detection":[3,50,87],"(IRSTD)":[4],"in":[5,72],"high-resolution":[6],"images":[7],"is":[8],"crucial":[9],"for":[10,185,220],"many":[11],"practical":[12],"applications,":[13],"such":[14],"as":[15,40,42],"surveillance":[16],"of":[17,38,64],"unmanned":[18],"aerial":[19],"vehicles":[20],"(UAVs)":[21],"and":[22,35,61,138,160,188,239],"UAV-based":[23],"ground":[24],"monitoring.":[25],"However,":[26],"IRSTD":[27],"remains":[28],"challenging":[29],"due":[30],"to":[31,113,142,155,179,208],"the":[32,97,140,144,170,180,199,206,216],"size":[34],"weak":[36],"features":[37],"targets,":[39],"well":[41],"significant":[43],"interference":[44],"from":[45,54,166],"complex":[46,73],"dynamic":[47],"backgrounds.":[48,74],"Existing":[49],"methods":[51],"often":[52],"suffer":[53],"redundant":[55],"computations":[56],"on":[57,108,210,226],"non-target":[58],"background":[59,167],"regions":[60],"insufficient":[62],"exploitation":[63],"context":[66,117,159],"information,":[67],"which":[68],"limits":[69],"their":[70],"performance":[71],"To":[75],"address":[76],"these":[77],"issues,":[78],"we":[79,100,121,173,191],"propose":[80,192],"an":[81],"efficient":[82],"coarse-to-fine":[83],"infrared":[84,221,229],"framework":[88],"with":[89],"attention":[90],"prior-guided":[91],"knowledge":[92,194],"distillation,":[93],"termed":[94],"ECFNet.":[95],"In":[96,169],"coarse":[98,181],"stage,":[99,172],"design":[101],"a":[102,123,149,175,193],"region":[103,118,183],"binary":[104],"classification":[105],"network":[106,141],"(RBCN)":[107],"grid-based":[109],"multi-scale":[110],"feature":[111,136,218],"maps":[112,137],"efficiently":[114],"recognize":[115],"target-containing":[116],"proposals.":[119],"Moreover,":[120],"introduce":[122],"novel":[124],"denoising-assisted":[125],"training":[126],"strategy":[127,196],"that":[128,232],"incorporates":[129],"noisy":[130],"ground-truth":[131],"(GT)":[132],"masks":[133,147],"into":[134],"RBCN":[135],"trains":[139],"reconstruct":[143],"original":[145],"GT":[146],"through":[148],"denoising":[150],"task,":[151],"thereby":[152,214],"encouraging":[153],"it":[154],"explicitly":[156],"learn":[157],"target-background":[158],"thus":[161],"better":[162],"distinguish":[163],"proposals":[165,184],"regions.":[168],"fine":[171],"customize":[174],"lightweight":[176],"detector":[178],"stage's":[182],"balancing":[186],"accuracy":[187],"efficiency.":[189,247],"Furthermore,":[190],"distillation":[195],"guided":[197],"by":[198],"teacher-student":[200],"cross-attention":[201],"prior.":[202],"This":[203],"mechanism":[204],"directs":[205],"student":[207],"focus":[209],"critical":[211],"regions,":[213],"enhancing":[215],"discriminative":[217],"representation":[219],"targets.":[223],"Extensive":[224],"experiments":[225],"three":[227],"real":[228],"datasets":[230],"demonstrate":[231],"our":[233],"method":[234],"outperforms":[235],"both":[236],"existing":[237],"single-stage":[238],"two-stage":[240],"approaches":[241],"while":[242],"maintaining":[243],"high":[244],"real-time":[245],"processing":[246]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-06-24T00:00:00"}