{"id":"https://openalex.org/W2891538076","doi":"https://doi.org/10.1109/icip.2018.8451667","title":"Dense Convolution for Semantic Segmentation","display_name":"Dense Convolution for Semantic Segmentation","publication_year":2018,"publication_date":"2018-09-07","ids":{"openalex":"https://openalex.org/W2891538076","doi":"https://doi.org/10.1109/icip.2018.8451667","mag":"2891538076"},"language":"en","primary_location":{"id":"doi:10.1109/icip.2018.8451667","doi":"https://doi.org/10.1109/icip.2018.8451667","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2018.8451667","pdf_url":null,"source":{"id":"https://openalex.org/S4210223844","display_name":"Proceedings - International Conference on Image Processing","issn_l":"1522-4880","issn":["1522-4880","2381-8549"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","jufo-1","norway-1"],"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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 25th IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"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/A5002823491","display_name":"Chaoyi Han","orcid":"https://orcid.org/0000-0001-9969-5778","observed_orcids":["https://orcid.org/0000-0001-9969-5778"]},"institutions":[{"id":"https://openalex.org/I4405271667","display_name":"Beijing National Research Center for Information Science and Technology","ror":"https://ror.org/02tcm9e24","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4405271667"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chaoyi Han","raw_affiliation_strings":["Beijing National Research Center for Information Science and Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing National Research Center for Information Science and Technology","institution_ids":["https://openalex.org/I4405271667"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Xiaoming Tao","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4405271667","display_name":"Beijing National Research Center for Information Science and Technology","ror":"https://ror.org/02tcm9e24","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4405271667"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoming Tao","raw_affiliation_strings":["Beijing National Research Center for Information Science and Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing National Research Center for Information Science and Technology","institution_ids":["https://openalex.org/I4405271667"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025137837","display_name":"Yiping Duan","orcid":"https://orcid.org/0000-0001-9638-7112","observed_orcids":["https://orcid.org/0000-0001-9638-7112"]},"institutions":[{"id":"https://openalex.org/I4405271667","display_name":"Beijing National Research Center for Information Science and Technology","ror":"https://ror.org/02tcm9e24","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4405271667"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yiping Duan","raw_affiliation_strings":["Beijing National Research Center for Information Science and Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing National Research Center for Information Science and Technology","institution_ids":["https://openalex.org/I4405271667"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5053012932","display_name":"Jianhua L\u00fc","orcid":"https://orcid.org/0000-0002-0314-9172","observed_orcids":["https://orcid.org/0000-0002-0314-9172"]},"institutions":[{"id":"https://openalex.org/I4405271667","display_name":"Beijing National Research Center for Information Science and Technology","ror":"https://ror.org/02tcm9e24","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4405271667"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianhua Lu","raw_affiliation_strings":["Beijing National Research Center for Information Science and Technology"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beijing National Research Center for Information Science and Technology","institution_ids":["https://openalex.org/I4405271667"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I4405271667"],"apc_list":null,"apc_paid":null,"fwci":0.0734,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.30137883,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":93},"biblio":{"volume":null,"issue":null,"first_page":"2222","last_page":"2226"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9976000189781189,"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":0.9976000189781189,"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":0.0013000000035390258,"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"}},{"id":"https://openalex.org/T10052","display_name":"Medical Image Segmentation Techniques","score":0.00039999998989515007,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/semantic-segmentation","display_name":"semantic segmentation","score":0.9990000128746033},{"id":"https://openalex.org/keywords/fully-convolutional-network","display_name":"fully convolutional network","score":0.781000018119812},{"id":"https://openalex.org/keywords/spatial-overlap","display_name":"spatial overlap","score":0.7070000171661377},{"id":"https://openalex.org/keywords/receptive-fields","display_name":"receptive fields","score":0.8360000252723694},{"id":"https://openalex.org/keywords/local-connections","display_name":"local connections","score":0.6809999942779541},{"id":"https://openalex.org/keywords/dense-prediction","display_name":"dense prediction","score":0.5410000085830688}],"concepts":[{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.8827872276306152},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.8337818384170532},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7682496309280396},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.7360702157020569},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6480144262313843},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5680435299873352},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.4641963243484497},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.45629170536994934},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.45426860451698303},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.4353662431240082},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4087798297405243},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.38984888792037964},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.31272369623184204},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.157841295003891},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"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},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icip.2018.8451667","doi":"https://doi.org/10.1109/icip.2018.8451667","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icip.2018.8451667","pdf_url":null,"source":{"id":"https://openalex.org/S4210223844","display_name":"Proceedings - International Conference on Image Processing","issn_l":"1522-4880","issn":["1522-4880","2381-8549"],"is_oa":false,"is_in_doaj":false,"is_core":true,"listed_in":["cwts-core","jufo-1","norway-1"],"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":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 25th IEEE International Conference on Image Processing (ICIP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W1686810756","https://openalex.org/W1903029394","https://openalex.org/W2124592697","https://openalex.org/W2144794286","https://openalex.org/W2155893237","https://openalex.org/W2194775991","https://openalex.org/W2333621733","https://openalex.org/W2412782625","https://openalex.org/W2555182955","https://openalex.org/W2560023338","https://openalex.org/W2563705555","https://openalex.org/W2592939477","https://openalex.org/W2598097916","https://openalex.org/W2598666589","https://openalex.org/W2601564443","https://openalex.org/W2792926444","https://openalex.org/W2963150697","https://openalex.org/W2963446712"],"related_works":["https://openalex.org/W2935161635","https://openalex.org/W2559597482","https://openalex.org/W2592939477","https://openalex.org/W2890429900","https://openalex.org/W2792926444","https://openalex.org/W2885583125","https://openalex.org/W2286929393","https://openalex.org/W2944105070","https://openalex.org/W4308909683","https://openalex.org/W2978589128"],"abstract_inverted_index":{"State-of-the-art":[0],"semantic":[1,50,119],"segmentation":[2,34,120],"methods":[3],"adopt":[4],"fully":[5,18],"convolutional":[6],"neural":[7],"networks":[8],"(FCNs)":[9],"to":[10,93],"solve":[11],"this":[12,37],"dense":[13,42,58,84,90],"prediction":[14],"problem.":[15],"However,":[16],"replacing":[17],"connected":[19],"layers":[20],"with":[21,86],"the":[22,61,112,115],"standard":[23],"2D":[24],"convolution":[25,43,59,91],"layer":[26],"is":[27,46,79],"straightforward":[28],"yet":[29],"not":[30],"optimal":[31],"in":[32,118],"generating":[33,54],"results.":[35],"In":[36],"paper":[38],"we":[39],"develop":[40],"a":[41,55],"scheme":[44],"that":[45],"more":[47,101],"suitable":[48],"for":[49,105],"segmentation.":[51],"Instead":[52],"of":[53,64,114],"single":[56],"output,":[57],"produces":[60],"same":[62],"number":[63],"output":[65],"as":[66],"its":[67],"input":[68],"and":[69,99],"introduces":[70],"spatial":[71],"overlaps":[72],"into":[73],"current":[74],"convolutions.":[75],"Then":[76],"each":[77],"activation":[78],"obtained":[80],"from":[81],"multiple":[82],"overlapped":[83],"convolutions":[85],"learnable":[87],"weights.":[88],"Such":[89],"helps":[92],"reinforce":[94],"local":[95],"connections":[96],"between":[97],"activations":[98],"provide":[100],"flexible":[102],"receptive":[103],"fields":[104],"predictions.":[106],"Experiments":[107],"on":[108],"benchmark":[109],"dataset":[110],"demonstrate":[111],"effectiveness":[113],"proposed":[116],"approach":[117],"tasks.":[121]},"counts_by_year":[{"year":2019,"cited_by_count":1}],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2018-09-27T00:00:00"}
