{"id":"https://openalex.org/W4416251200","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227248","title":"Multiscale Attention Knowledge Distillation for Object Detection","display_name":"Multiscale Attention Knowledge Distillation for Object Detection","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4416251200","doi":"https://doi.org/10.1109/ijcnn64981.2025.11227248"},"language":null,"primary_location":{"id":"doi:10.1109/ijcnn64981.2025.11227248","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227248","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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/A5016734285","display_name":"Fengshuo Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I25757504","display_name":"China University of Mining and Technology","ror":"https://ror.org/01xt2dr21","country_code":"CN","type":"education","lineage":["https://openalex.org/I25757504"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Fengshuo Zhang","raw_affiliation_strings":["China University of Mining and Technology"],"affiliations":[{"raw_affiliation_string":"China University of Mining and Technology","institution_ids":["https://openalex.org/I25757504"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5016734285"],"corresponding_institution_ids":["https://openalex.org/I25757504"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.37417585,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9664999842643738,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9664999842643738,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.0031999999191612005,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.002099999925121665,"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/object-detection","display_name":"Object detection","score":0.7300999760627747},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6498000025749207},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5856000185012817},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5802000164985657},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5619000196456909},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5511000156402588},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.43459999561309814},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4187000095844269}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7980999946594238},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.7300999760627747},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6807000041007996},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6498000025749207},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5856000185012817},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5802000164985657},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5619000196456909},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5511000156402588},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.43459999561309814},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4311000108718872},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4187000095844269},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.4180000126361847},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.41359999775886536},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.36329999566078186},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.3547999858856201},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.35120001435279846},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34779998660087585},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.34209999442100525},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.3070000112056732},{"id":"https://openalex.org/C186967261","wikidata":"https://www.wikidata.org/wiki/Q5082128","display_name":"Mobile device","level":2,"score":0.28679999709129333},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.27649998664855957},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2635999917984009},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.2563000023365021},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.2551000118255615},{"id":"https://openalex.org/C126422989","wikidata":"https://www.wikidata.org/wiki/Q93586","display_name":"Feature detection (computer vision)","level":4,"score":0.2549999952316284}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn64981.2025.11227248","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn64981.2025.11227248","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 International Joint Conference on Neural Networks (IJCNN)","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":32,"referenced_works":["https://openalex.org/W2145085734","https://openalex.org/W2193145675","https://openalex.org/W2565639579","https://openalex.org/W2570343428","https://openalex.org/W2750432752","https://openalex.org/W2912322140","https://openalex.org/W2946794439","https://openalex.org/W2959289524","https://openalex.org/W2963037989","https://openalex.org/W2963150697","https://openalex.org/W2963351448","https://openalex.org/W2963516811","https://openalex.org/W2964241181","https://openalex.org/W2982770724","https://openalex.org/W2989604896","https://openalex.org/W2997006708","https://openalex.org/W3004127093","https://openalex.org/W3012573144","https://openalex.org/W3034429256","https://openalex.org/W3113709932","https://openalex.org/W3131500599","https://openalex.org/W3173270634","https://openalex.org/W3176459575","https://openalex.org/W3204364304","https://openalex.org/W3204801262","https://openalex.org/W4213308398","https://openalex.org/W4312590184","https://openalex.org/W4386071640","https://openalex.org/W4390871868","https://openalex.org/W4390874023","https://openalex.org/W4403878467","https://openalex.org/W4403878569"],"related_works":[],"abstract_inverted_index":{"Object":[0],"detection":[1,155],"is":[2,160],"a":[3,49,134],"fundamental":[4],"task":[5],"in":[6,34],"computer":[7],"vision,":[8],"involving":[9],"the":[10,85,90,96,127,144],"identification":[11],"and":[12,78,103,138],"localization":[13],"of":[14,89,129,146],"objects":[15,113],"within":[16],"images.":[17],"While":[18],"deep":[19],"learning-based":[20],"object":[21,59,73],"detectors":[22],"have":[23],"demonstrated":[24],"remarkable":[25],"accuracy,":[26],"their":[27],"high":[28,154],"computational":[29,139],"demands":[30],"often":[31],"hinder":[32],"deployment":[33],"resource-constrained":[35],"environments,":[36],"such":[37],"as":[38],"mobile":[39],"devices":[40],"or":[41],"real-time":[42,150],"applications.":[43],"In":[44],"this":[45],"paper,":[46],"we":[47],"propose":[48],"novel":[50],"Multiscale":[51],"Attention-based":[52],"Knowledge":[53],"Distillation":[54],"(MAD)":[55],"method":[56,124],"tailored":[57],"for":[58,149],"detection.":[60],"Our":[61],"approach":[62,148],"systematically":[63],"distills":[64],"knowledge":[65],"by":[66],"utilizing":[67],"multiscale":[68],"feature":[69,91],"maps,":[70],"which":[71],"capture":[72],"details":[74],"at":[75],"various":[76],"resolutions,":[77],"incorporates":[79],"attention":[80],"mechanisms":[81],"to":[82,99,111],"focus":[83],"on":[84,117],"most":[86],"informative":[87],"regions":[88],"space.":[92],"This":[93],"combination":[94],"enables":[95],"student":[97,131],"model":[98],"learn":[100],"both":[101],"global":[102],"local":[104],"contextual":[105],"information":[106],"effectively,":[107],"enhancing":[108],"its":[109],"ability":[110],"detect":[112],"accurately.":[114],"Extensive":[115],"experiments":[116],"benchmark":[118],"datasets":[119],"demonstrate":[120],"that":[121],"our":[122,147],"MAD":[123],"significantly":[125],"improves":[126],"performance":[128,156],"lightweight":[130],"models,":[132],"achieving":[133],"balance":[135],"between":[136],"accuracy":[137],"efficiency.":[140],"The":[141],"results":[142],"highlight":[143],"suitability":[145],"applications,":[151],"where":[152],"maintaining":[153],"with":[157],"limited":[158],"resources":[159],"critical.":[161]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-11-14T00:00:00"}