{"id":"https://openalex.org/W4292262751","doi":"https://doi.org/10.1109/access.2022.3199368","title":"Divide to Attend: A Multiple Receptive Field Attention Module for Object Detection in Remote Sensing Images","display_name":"Divide to Attend: A Multiple Receptive Field Attention Module for Object Detection in Remote Sensing Images","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4292262751","doi":"https://doi.org/10.1109/access.2022.3199368"},"language":"en","primary_location":{"id":"doi:10.1109/access.2022.3199368","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3199368","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09858151.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"type":"article","indexed_in":["crossref","doaj"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09858151.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5047403295","display_name":"Haotian Tan","orcid":"https://orcid.org/0000-0001-8698-0115"},"institutions":[{"id":"https://openalex.org/I96908189","display_name":"Xinjiang University","ror":"https://ror.org/059gw8r13","country_code":"CN","type":"education","lineage":["https://openalex.org/I96908189"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Haotian Tan","raw_affiliation_strings":["School of Information Science and Engineering, Xinjiang University, Urumqi, China"],"raw_orcid":"https://orcid.org/0000-0001-8698-0115","affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Xinjiang University, Urumqi, China","institution_ids":["https://openalex.org/I96908189"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020873493","display_name":"Jiong Yu","orcid":"https://orcid.org/0000-0002-9181-6720"},"institutions":[{"id":"https://openalex.org/I96908189","display_name":"Xinjiang University","ror":"https://ror.org/059gw8r13","country_code":"CN","type":"education","lineage":["https://openalex.org/I96908189"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu Jiong","raw_affiliation_strings":["School of Information Science and Engineering, Xinjiang University, Urumqi, China","School of Software, Xinjiang University, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Information Science and Engineering, Xinjiang University, Urumqi, China","institution_ids":["https://openalex.org/I96908189"]},{"raw_affiliation_string":"School of Software, Xinjiang University, China","institution_ids":["https://openalex.org/I96908189"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021564561","display_name":"Xueqiang Wan","orcid":null},"institutions":[{"id":"https://openalex.org/I96908189","display_name":"Xinjiang University","ror":"https://ror.org/059gw8r13","country_code":"CN","type":"education","lineage":["https://openalex.org/I96908189"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xueqiang Wan","raw_affiliation_strings":["School of Software, Xinjiang University, Urumqi, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Software, Xinjiang University, Urumqi, China","institution_ids":["https://openalex.org/I96908189"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100395832","display_name":"Junjie Wang","orcid":"https://orcid.org/0009-0003-8754-7948"},"institutions":[{"id":"https://openalex.org/I96908189","display_name":"Xinjiang University","ror":"https://ror.org/059gw8r13","country_code":"CN","type":"education","lineage":["https://openalex.org/I96908189"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Junjie Wang","raw_affiliation_strings":["School of Software, Xinjiang University, Urumqi, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Software, Xinjiang University, Urumqi, China","institution_ids":["https://openalex.org/I96908189"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":{"value":1850,"currency":"USD","value_usd":1850},"apc_paid":{"value":1850,"currency":"USD","value_usd":1850},"fwci":0.2031,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.47637013,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"10","issue":null,"first_page":"87266","last_page":"87281"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9994999766349792,"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":0.9994999766349792,"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":0.9991000294685364,"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/T10689","display_name":"Remote-Sensing Image Classification","score":0.9988999962806702,"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/computer-science","display_name":"Computer science","score":0.8392651081085205},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6865354776382446},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6844034790992737},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.640998899936676},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5641423463821411},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.5612500309944153},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5308780670166016},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.5298946499824524},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.5115369558334351},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.5025880336761475},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4507066607475281},{"id":"https://openalex.org/keywords/detector","display_name":"Detector","score":0.45064812898635864},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4405132234096527},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.41873079538345337},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.3089781403541565},{"id":"https://openalex.org/keywords/telecommunications","display_name":"Telecommunications","score":0.0801239013671875},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.06954434514045715}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8392651081085205},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6865354776382446},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6844034790992737},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.640998899936676},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5641423463821411},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5612500309944153},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5308780670166016},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.5298946499824524},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5115369558334351},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.5025880336761475},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4507066607475281},{"id":"https://openalex.org/C94915269","wikidata":"https://www.wikidata.org/wiki/Q1834857","display_name":"Detector","level":2,"score":0.45064812898635864},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4405132234096527},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.41873079538345337},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3089781403541565},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0801239013671875},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.06954434514045715},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","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},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","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}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1109/access.2022.3199368","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3199368","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09858151.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},{"id":"pmh:oai:doaj.org/article:8cee2965933e4ed782915c13b6572abb","is_oa":true,"landing_page_url":"https://doaj.org/article/8cee2965933e4ed782915c13b6572abb","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"IEEE Access, Vol 10, Pp 87266-87281 (2022)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.1109/access.2022.3199368","is_oa":true,"landing_page_url":"https://doi.org/10.1109/access.2022.3199368","pdf_url":"https://ieeexplore.ieee.org/ielx7/6287639/6514899/09858151.pdf","source":{"id":"https://openalex.org/S2485537415","display_name":"IEEE Access","issn_l":"2169-3536","issn":["2169-3536"],"is_oa":true,"is_in_doaj":true,"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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Access","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.5799999833106995,"id":"https://metadata.un.org/sdg/15","display_name":"Life in Land"}],"awards":[{"id":"https://openalex.org/G450270574","display_name":"\u793e\u4ea4\u7f51\u7edc\u73af\u5883\u4e0b\u57fa\u4e8e\u534f\u540c\u8fc7\u6ee4\u7684\u4e0a\u4e0b\u6587\u611f\u77e5\u63a8\u8350\u7cfb\u7edf\u7814\u7a76","funder_award_id":"61462079","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G5238719182","display_name":"\u5927\u6570\u636e\u6d41\u5f0f\u8ba1\u7b97\u80fd\u8017\u6a21\u578b\u53ca\u4f18\u5316\u7814\u7a76","funder_award_id":"61862060","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G7068400936","display_name":"\u57fa\u4e8e\u5185\u5b58\u8ba1\u7b97\u7684\u9065\u611f\u6570\u636e\u9ad8\u6548\u5e76\u884c\u548c\u4efb\u52a1\u8c03\u5ea6\u7814\u7a76","funder_award_id":"61562086","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4292262751.pdf","grobid_xml":"https://content.openalex.org/works/W4292262751.grobid-xml"},"referenced_works_count":59,"referenced_works":["https://openalex.org/W1536680647","https://openalex.org/W1861492603","https://openalex.org/W1923697677","https://openalex.org/W2070452328","https://openalex.org/W2102605133","https://openalex.org/W2107034620","https://openalex.org/W2161969291","https://openalex.org/W2194775991","https://openalex.org/W2400429454","https://openalex.org/W2412782625","https://openalex.org/W2512351403","https://openalex.org/W2515866431","https://openalex.org/W2565639579","https://openalex.org/W2625829240","https://openalex.org/W2664380602","https://openalex.org/W2752782242","https://openalex.org/W2787091153","https://openalex.org/W2883105896","https://openalex.org/W2884585870","https://openalex.org/W2935079508","https://openalex.org/W2956175719","https://openalex.org/W2963037989","https://openalex.org/W2963091558","https://openalex.org/W2963150697","https://openalex.org/W2963323244","https://openalex.org/W2963351448","https://openalex.org/W2963504571","https://openalex.org/W2963727650","https://openalex.org/W2963840672","https://openalex.org/W2963857746","https://openalex.org/W2964241181","https://openalex.org/W2981689412","https://openalex.org/W2982220924","https://openalex.org/W2982770724","https://openalex.org/W2992240579","https://openalex.org/W3034552520","https://openalex.org/W3034752215","https://openalex.org/W3035396860","https://openalex.org/W3090092719","https://openalex.org/W3105577662","https://openalex.org/W3107867277","https://openalex.org/W3118695918","https://openalex.org/W3119205652","https://openalex.org/W3134703268","https://openalex.org/W3156315514","https://openalex.org/W3164062973","https://openalex.org/W3172087149","https://openalex.org/W3177052299","https://openalex.org/W3184439416","https://openalex.org/W3190905472","https://openalex.org/W4205659311","https://openalex.org/W4293584584","https://openalex.org/W6620707391","https://openalex.org/W6640295612","https://openalex.org/W6678174250","https://openalex.org/W6684191040","https://openalex.org/W6696085341","https://openalex.org/W6750227808","https://openalex.org/W6798838024"],"related_works":["https://openalex.org/W2366906938","https://openalex.org/W2349391998","https://openalex.org/W4205655149","https://openalex.org/W2000775715","https://openalex.org/W2074467390","https://openalex.org/W2795393339","https://openalex.org/W2626393719","https://openalex.org/W4390618967","https://openalex.org/W2174745845","https://openalex.org/W1522196789"],"abstract_inverted_index":{"The":[0],"study":[1],"of":[2,21,31,51,58,85,140,152,170,203],"remote":[3,39,153,158,187],"sensing":[4,40,154,159,188],"image":[5,189],"object":[6,32,160],"detection":[7,49,161],"has":[8],"excellent":[9],"research":[10],"value":[11],"in":[12,38,116,123,149],"environmental":[13],"protection":[14],"and":[15,34,60,73,113,164,194,201],"public":[16],"safety.":[17],"However,":[18],"the":[19,22,28,48,52,56,83,94,109,117,131,150,168,181,184,199],"performance":[20,50,169],"detectors":[23],"is":[24,44,75,173],"unsatisfactory":[25],"due":[26],"to":[27,46,69,92,137],"large":[29],"variability":[30],"size":[33],"complex":[35],"background":[36,151],"noise":[37,148],"images.":[41,155],"Therefore,":[42],"it":[43],"essential":[45],"improve":[47],"detectors.":[53],"Inspired":[54],"by":[55],"idea":[57],"\"divide":[59],"conquer\",":[61],"we":[62,81,129],"proposed":[63],"a":[64,76],"Multiple":[65],"Receptive":[66],"Field":[67],"Attention(MRFA)":[68],"solve":[70],"these":[71,124],"problems":[72],"which":[74,144],"plug-and-play":[77],"attention":[78,133,178],"method.":[79,205],"First,":[80],"use":[82],"method":[84,136,172],"multiple":[86,132],"receptive":[87,104],"field":[88],"feature":[89,96,100,119,125],"map":[90,97,134],"generation":[91],"convert":[93],"input":[95,118],"into":[98],"four":[99],"maps":[101,120],"with":[102],"different":[103,141],"fields.":[105],"In":[106],"this":[107],"way,":[108],"small,":[110],"medium,":[111],"large,":[112],"immense":[114],"objects":[115,139],"are":[121],"\"seen\"":[122],"maps,":[126],"respectively.":[127],"Then,":[128],"used":[130],"fusion":[135],"focus":[138],"sizes":[142],"separately,":[143],"can":[145],"effectively":[146],"suppress":[147],"Experiments":[156],"on":[157,186],"datasets":[162],"DIOR":[163],"HRRSD":[165],"demonstrate":[166],"that":[167],"our":[171,204],"better":[174],"than":[175],"other":[176],"state-of-the-art":[177],"modules.":[179],"At":[180],"same":[182],"time,":[183],"experiments":[185],"semantic":[190],"segmentation":[191],"dataset":[192,196],"WHDLD":[193],"classification":[195],"AID":[197],"prove":[198],"generalization":[200],"superiority":[202]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}