{"id":"https://openalex.org/W3162566581","doi":"https://doi.org/10.1109/icassp39728.2021.9414323","title":"Differential Convolution Feature Guided Deep Multi-Scale Multiple Instance Learning for Aerial Scene Classification","display_name":"Differential Convolution Feature Guided Deep Multi-Scale Multiple Instance Learning for Aerial Scene Classification","publication_year":2021,"publication_date":"2021-05-13","ids":{"openalex":"https://openalex.org/W3162566581","doi":"https://doi.org/10.1109/icassp39728.2021.9414323","mag":"3162566581"},"language":"en","primary_location":{"id":"doi:10.1109/icassp39728.2021.9414323","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp39728.2021.9414323","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","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/A5078527481","display_name":"Beichen Zhou","orcid":"https://orcid.org/0000-0002-6922-4358"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Beichen Zhou","raw_affiliation_strings":["School of Remote Sensing and Information Engineering, Wuhan University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Remote Sensing and Information Engineering, Wuhan University, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068565464","display_name":"Jingjun Yi","orcid":"https://orcid.org/0000-0002-4249-3021"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jingjun Yi","raw_affiliation_strings":["School of Remote Sensing and Information Engineering, Wuhan University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Remote Sensing and Information Engineering, Wuhan University, China","institution_ids":["https://openalex.org/I37461747"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5035415308","display_name":"Qi Bi","orcid":"https://orcid.org/0000-0002-1047-4790"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qi Bi","raw_affiliation_strings":["School of Remote Sensing and Information Engineering, Wuhan University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Remote Sensing and Information Engineering, Wuhan University, China","institution_ids":["https://openalex.org/I37461747"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I37461747"],"apc_list":null,"apc_paid":null,"fwci":0.7593,"has_fulltext":false,"cited_by_count":7,"citation_normalized_percentile":{"value":0.7457607,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"4595","last_page":"4599"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.9980000257492065,"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":0.9980000257492065,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9972000122070312,"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/T13890","display_name":"Remote Sensing and Land Use","score":0.9886999726295471,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.7997159957885742},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7611433863639832},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7498752474784851},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.654101550579071},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.6356216669082642},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6333059668540955},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6078454852104187},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.5484359264373779},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5435675382614136},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5378192663192749},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5205767750740051},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.5061535239219666},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.417239248752594},{"id":"https://openalex.org/keywords/extractor","display_name":"Extractor","score":0.41141799092292786},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3800576329231262},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.22404879331588745},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.15939491987228394},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.07189598679542542}],"concepts":[{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7997159957885742},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7611433863639832},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7498752474784851},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.654101550579071},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.6356216669082642},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6333059668540955},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6078454852104187},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.5484359264373779},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5435675382614136},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5378192663192749},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5205767750740051},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5061535239219666},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.417239248752594},{"id":"https://openalex.org/C117978034","wikidata":"https://www.wikidata.org/wiki/Q5422192","display_name":"Extractor","level":2,"score":0.41141799092292786},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3800576329231262},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.22404879331588745},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.15939491987228394},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07189598679542542},{"id":"https://openalex.org/C21880701","wikidata":"https://www.wikidata.org/wiki/Q2144042","display_name":"Process engineering","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C58640448","wikidata":"https://www.wikidata.org/wiki/Q42515","display_name":"Cartography","level":1,"score":0.0},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icassp39728.2021.9414323","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icassp39728.2021.9414323","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.7699999809265137}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":30,"referenced_works":["https://openalex.org/W1989316905","https://openalex.org/W2149648623","https://openalex.org/W2358876993","https://openalex.org/W2515866431","https://openalex.org/W2531897166","https://openalex.org/W2563764042","https://openalex.org/W2592165076","https://openalex.org/W2592962403","https://openalex.org/W2621526417","https://openalex.org/W2727875856","https://openalex.org/W2746325398","https://openalex.org/W2783165089","https://openalex.org/W2785934082","https://openalex.org/W2829067510","https://openalex.org/W2890732922","https://openalex.org/W2902184095","https://openalex.org/W2962749812","https://openalex.org/W2964069537","https://openalex.org/W2969689033","https://openalex.org/W2988666519","https://openalex.org/W2991488782","https://openalex.org/W2991682101","https://openalex.org/W3100892511","https://openalex.org/W3103856189","https://openalex.org/W3105577662","https://openalex.org/W3123212500","https://openalex.org/W6706861870","https://openalex.org/W6745956249","https://openalex.org/W6747701563","https://openalex.org/W6756314655"],"related_works":["https://openalex.org/W2965546495","https://openalex.org/W4389116644","https://openalex.org/W2153315159","https://openalex.org/W3208297503","https://openalex.org/W2761785940","https://openalex.org/W3119773509","https://openalex.org/W2889153461","https://openalex.org/W2964117661","https://openalex.org/W4388405611","https://openalex.org/W2619127353"],"abstract_inverted_index":{"Aerial":[0],"image":[1],"classification":[2,126],"is":[3,25],"challenging":[4],"for":[5],"current":[6],"deep":[7,52,84],"learning":[8,56,95],"models":[9],"due":[10],"to":[11,27,41,59,74,97,113],"the":[12,18,29,61,76,83,108,115,134],"varied":[13],"geo-spatial":[14],"object":[15],"scales":[16,38],"and":[17],"complicated":[19],"scene":[20,125],"spatial":[21],"arrangement.":[22],"Thus,":[23],"it":[24],"necessary":[26],"stress":[28],"key":[30],"local":[31],"feature":[32,72],"response":[33],"from":[34,79,106],"a":[35,51,67,92,99,138],"variety":[36],"of":[37,86],"so":[39],"as":[40],"represent":[42],"discriminative":[43],"convolutional":[44],"features.":[45],"In":[46],"this":[47],"paper,":[48],"we":[49,65],"propose":[50],"multi-scale":[53,69],"multiple":[54,93],"instance":[55,94],"(DMSMIL)":[57],"framework":[58],"tackle":[60],"above":[62],"challenges.":[63],"Firstly,":[64],"develop":[66],"differential":[68],"dilated":[70],"convolution":[71],"extractor":[73],"exploit":[75],"different":[77,80],"patterns":[78],"scales.":[81],"Then,":[82],"features":[85],"each":[87],"scale":[88],"are":[89,111],"fed":[90],"into":[91],"module":[96],"generate":[98,114],"bag-level":[100],"probability":[101,104],"prediction.":[102,118],"Lastly,":[103],"predictions":[105],"all":[107],"MIL":[109],"branches":[110],"fused":[112],"final":[116],"semantic":[117],"Extensive":[119],"experiments":[120],"on":[121],"three":[122],"widely-utilized":[123],"aerial":[124],"benchmarks":[127],"demonstrate":[128],"that":[129],"our":[130],"proposed":[131],"DMSMIL":[132],"outperforms":[133],"state-of-the-art":[135],"approaches":[136],"by":[137],"large":[139],"margin.":[140]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}