{"id":"https://openalex.org/W4412536213","doi":"https://doi.org/10.1109/lgrs.2025.3591045","title":"Toward Effective Knowledge Distillation for Fine-Grained Object Recognition in Remote Sensing","display_name":"Toward Effective Knowledge Distillation for Fine-Grained Object Recognition in Remote Sensing","publication_year":2025,"publication_date":"2025-01-01","ids":{"openalex":"https://openalex.org/W4412536213","doi":"https://doi.org/10.1109/lgrs.2025.3591045"},"language":"en","primary_location":{"id":"doi:10.1109/lgrs.2025.3591045","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2025.3591045","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Geoscience and Remote Sensing Letters","raw_type":"journal-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/A5119029661","display_name":"Yangte Gao","orcid":null},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yangte Gao","raw_affiliation_strings":["School of Information and Electronics, Beijing Institute of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Information and Electronics, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079668307","display_name":"Chenwei Deng","orcid":"https://orcid.org/0000-0002-3747-5128"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenwei Deng","raw_affiliation_strings":["School of Information and Electronics, Beijing Institute of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Information and Electronics, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100334661","display_name":"Liang Chen","orcid":"https://orcid.org/0000-0003-3273-5522"},"institutions":[{"id":"https://openalex.org/I125839683","display_name":"Beijing Institute of Technology","ror":"https://ror.org/01skt4w74","country_code":"CN","type":"education","lineage":["https://openalex.org/I125839683","https://openalex.org/I890469752"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liang Chen","raw_affiliation_strings":["School of Information and Electronics, Beijing Institute of Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Information and Electronics, Beijing Institute of Technology, Beijing, China","institution_ids":["https://openalex.org/I125839683"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5119029661"],"corresponding_institution_ids":["https://openalex.org/I125839683"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.24377553,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"22","issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9634000062942505,"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"}},"topics":[{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9634000062942505,"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"}},{"id":"https://openalex.org/T12389","display_name":"Infrared Target Detection Methodologies","score":0.9122999906539917,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7174654006958008},{"id":"https://openalex.org/keywords/distillation","display_name":"Distillation","score":0.6277686953544617},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5054192543029785},{"id":"https://openalex.org/keywords/cognitive-neuroscience-of-visual-object-recognition","display_name":"Cognitive neuroscience of visual object recognition","score":0.4782347083091736},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.462518572807312},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4316878318786621},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.35248124599456787},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.35085999965667725},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.29964107275009155},{"id":"https://openalex.org/keywords/geology","display_name":"Geology","score":0.09315618872642517}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7174654006958008},{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.6277686953544617},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5054192543029785},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.4782347083091736},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.462518572807312},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4316878318786621},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.35248124599456787},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.35085999965667725},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.29964107275009155},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.09315618872642517},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/lgrs.2025.3591045","is_oa":false,"landing_page_url":"https://doi.org/10.1109/lgrs.2025.3591045","pdf_url":null,"source":{"id":"https://openalex.org/S126920919","display_name":"IEEE Geoscience and Remote Sensing Letters","issn_l":"1545-598X","issn":["1545-598X","1558-0571"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Geoscience and Remote Sensing Letters","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/2","score":0.41999998688697815,"display_name":"Zero hunger"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1821462560","https://openalex.org/W1969922692","https://openalex.org/W2194775991","https://openalex.org/W2963140444","https://openalex.org/W4206640001","https://openalex.org/W4226426325","https://openalex.org/W4387385613","https://openalex.org/W4387934926","https://openalex.org/W4392173997","https://openalex.org/W4403598959","https://openalex.org/W4404101481","https://openalex.org/W4410491961","https://openalex.org/W6637551013","https://openalex.org/W6761472960","https://openalex.org/W6839053282"],"related_works":["https://openalex.org/W3026162553","https://openalex.org/W2114275278","https://openalex.org/W4292830139","https://openalex.org/W4319309705","https://openalex.org/W1489511283","https://openalex.org/W2974914859","https://openalex.org/W2026565050","https://openalex.org/W2110244802","https://openalex.org/W2163728705","https://openalex.org/W949345935"],"abstract_inverted_index":{"With":[0],"advancements":[1],"in":[2,50,57,151,163,175],"on-board":[3],"computing":[4],"devices":[5],"deployed":[6],"on":[7,157],"remote":[8,16,58,152,164],"sensing":[9,17,59,153,165],"platforms,":[10],"the":[11,67,70,74,77,90,114,122,133,137,144],"demand":[12],"for":[13],"efficiently":[14],"processing":[15],"imagery":[18],"has":[19,30],"become":[20],"increasingly":[21],"prominent.":[22],"Knowledge":[23],"distillation,":[24],"as":[25,66],"an":[26],"effective":[27,91],"lightweight":[28],"method,":[29],"been":[31],"introduced":[32],"into":[33],"this":[34,99],"domain.":[35],"Intuitively,":[36],"distillation":[37,105,186],"from":[38],"a":[39,63,103],"larger":[40],"teacher":[41,71,95],"model":[42,72,79],"is":[43],"expected":[44],"to":[45,131,143],"yield":[46],"better":[47],"performance.":[48],"However,":[49],"our":[51,168],"investigation":[52],"of":[53,69,76,93,136],"fine-grained":[54,159],"object":[55,160],"recognition":[56,161],"imagery,":[60],"we":[61,101],"observed":[62],"counter-intuitive":[64],"phenomenon:":[65],"size":[68],"increases,":[73],"performance":[75],"student":[78,172],"initially":[80],"improves":[81,171],"but":[82],"then":[83],"degrades.":[84],"This":[85],"capacity":[86,138],"gap":[87,139],"issue":[88],"hinders":[89],"utilization":[92],"stronger":[94],"models.":[96],"To":[97],"address":[98],"issue,":[100],"propose":[102],"novel":[104],"framework":[106],"named":[107],"BL-KD.":[108],"It":[109],"integrates":[110],"two":[111],"tailored":[112],"components:":[113],"Class-level":[115],"Learnable":[116],"Orthogonal":[117],"Projection":[118],"(CLOP)":[119],"module":[120],"and":[121,148,181],"Object":[123],"Re-Balance":[124],"(ORB)":[125],"module,":[126],"which":[127],"are":[128],"jointly":[129],"optimized":[130],"mitigate":[132],"negative":[134],"impact":[135],"while":[140],"effectively":[141],"adapting":[142],"unique":[145],"distributional":[146],"patterns":[147],"challenges":[149],"inherent":[150],"imagery.":[154],"Experiments":[155],"conducted":[156],"multiple":[158],"tasks":[162],"demonstrate":[166],"that":[167],"method":[169],"consistently":[170],"performance,":[173],"particularly":[174],"scenarios":[176],"involving":[177],"large":[178],"teacher-student":[179],"gaps,":[180],"outperforms":[182],"several":[183],"widely":[184],"used":[185],"baselines.":[187]},"counts_by_year":[],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-07-22T00:00:00"}