{"id":"https://openalex.org/W2913390712","doi":"https://doi.org/10.1109/iccchina.2018.8641146","title":"ResNet with Global and Local Image Features, Stacked Pooling Block, for Semantic Segmentation","display_name":"ResNet with Global and Local Image Features, Stacked Pooling Block, for Semantic Segmentation","publication_year":2018,"publication_date":"2018-08-01","ids":{"openalex":"https://openalex.org/W2913390712","doi":"https://doi.org/10.1109/iccchina.2018.8641146","mag":"2913390712"},"language":"en","primary_location":{"id":"doi:10.1109/iccchina.2018.8641146","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccchina.2018.8641146","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE/CIC International Conference on Communications in China (ICCC)","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/A5103095524","display_name":"Hui Song","orcid":"https://orcid.org/0000-0001-8703-3224"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hui Song","raw_affiliation_strings":["School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022422213","display_name":"Yun Zhou","orcid":"https://orcid.org/0000-0001-7328-0275"},"institutions":[{"id":"https://openalex.org/I4210111085","display_name":"Academy of Broadcasting Science","ror":"https://ror.org/01z4nez64","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210111085"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yun Zhou","raw_affiliation_strings":["Academy of Broadcasting Science, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Academy of Broadcasting Science, Beijing, China","institution_ids":["https://openalex.org/I4210111085"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002774811","display_name":"Zhuqing Jiang","orcid":"https://orcid.org/0000-0001-6308-5708"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhuqing Jiang","raw_affiliation_strings":["School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Information and Communication Engineering, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102864522","display_name":"Xiaoqiang Guo","orcid":"https://orcid.org/0000-0003-2355-0569"},"institutions":[{"id":"https://openalex.org/I4210111085","display_name":"Academy of Broadcasting Science","ror":"https://ror.org/01z4nez64","country_code":"CN","type":"government","lineage":["https://openalex.org/I4210111085"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoqiang Guo","raw_affiliation_strings":["Academy of Broadcasting Science, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Academy of Broadcasting Science, Beijing, China","institution_ids":["https://openalex.org/I4210111085"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5056191120","display_name":"Zixuan Yang","orcid":"https://orcid.org/0000-0002-7764-3595"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zixuan Yang","raw_affiliation_strings":["School of Digital Media and Design Arts, Beijing University of Posts and Telecommunications, Beijing, China"],"affiliations":[{"raw_affiliation_string":"School of Digital Media and Design Arts, Beijing University of Posts and Telecommunications, Beijing, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5103095524"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.17859882,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"79","last_page":"83"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":1.0,"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":1.0,"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":0.9994000196456909,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9993000030517578,"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/computer-science","display_name":"Computer science","score":0.794008195400238},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.7767306566238403},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.714205801486969},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6740816831588745},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5663638114929199},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5608747005462646},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.557369589805603},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5461456179618835},{"id":"https://openalex.org/keywords/merge","display_name":"Merge (version control)","score":0.536564290523529},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.5257713794708252},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.44483867287635803},{"id":"https://openalex.org/keywords/segmentation-based-object-categorization","display_name":"Segmentation-based object categorization","score":0.4220667779445648},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.4136332869529724},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.40564438700675964},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.14404168725013733},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.07848814129829407}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.794008195400238},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.7767306566238403},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.714205801486969},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6740816831588745},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5663638114929199},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5608747005462646},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.557369589805603},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5461456179618835},{"id":"https://openalex.org/C197129107","wikidata":"https://www.wikidata.org/wiki/Q1921621","display_name":"Merge (version control)","level":2,"score":0.536564290523529},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.5257713794708252},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.44483867287635803},{"id":"https://openalex.org/C25694479","wikidata":"https://www.wikidata.org/wiki/Q7446278","display_name":"Segmentation-based object categorization","level":5,"score":0.4220667779445648},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.4136332869529724},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.40564438700675964},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.14404168725013733},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.07848814129829407},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iccchina.2018.8641146","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccchina.2018.8641146","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE/CIC International Conference on Communications in China (ICCC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11","score":0.7400000095367432}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":64,"referenced_works":["https://openalex.org/W54257720","https://openalex.org/W360623563","https://openalex.org/W1507506748","https://openalex.org/W1522301498","https://openalex.org/W1665214252","https://openalex.org/W1677182931","https://openalex.org/W1686810756","https://openalex.org/W1745334888","https://openalex.org/W1836465849","https://openalex.org/W1899185266","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W1905829557","https://openalex.org/W1913356549","https://openalex.org/W1923697677","https://openalex.org/W1992178727","https://openalex.org/W2017313218","https://openalex.org/W2097117768","https://openalex.org/W2100588357","https://openalex.org/W2102605133","https://openalex.org/W2103328396","https://openalex.org/W2108598243","https://openalex.org/W2117539524","https://openalex.org/W2124592697","https://openalex.org/W2161236525","https://openalex.org/W2194775991","https://openalex.org/W2274287116","https://openalex.org/W2340897893","https://openalex.org/W2381998130","https://openalex.org/W2412782625","https://openalex.org/W2516063657","https://openalex.org/W2560023338","https://openalex.org/W2593403058","https://openalex.org/W2601564443","https://openalex.org/W2949117887","https://openalex.org/W2952793010","https://openalex.org/W2962850830","https://openalex.org/W2963108253","https://openalex.org/W2963166928","https://openalex.org/W2963446712","https://openalex.org/W2963840672","https://openalex.org/W2963996492","https://openalex.org/W2964121744","https://openalex.org/W2964350391","https://openalex.org/W4244914727","https://openalex.org/W4293406525","https://openalex.org/W4300126339","https://openalex.org/W6602211262","https://openalex.org/W6630336748","https://openalex.org/W6637242042","https://openalex.org/W6637373629","https://openalex.org/W6638667902","https://openalex.org/W6639824700","https://openalex.org/W6639824712","https://openalex.org/W6640295612","https://openalex.org/W6675415620","https://openalex.org/W6694260854","https://openalex.org/W6696085341","https://openalex.org/W6703848168","https://openalex.org/W6710148796","https://openalex.org/W6717372056","https://openalex.org/W6725739302","https://openalex.org/W6726623924","https://openalex.org/W6734303423"],"related_works":["https://openalex.org/W3144569342","https://openalex.org/W2185902295","https://openalex.org/W2945274617","https://openalex.org/W2372421320","https://openalex.org/W2057775483","https://openalex.org/W2041871225","https://openalex.org/W2386644571","https://openalex.org/W4205800335","https://openalex.org/W2387793296","https://openalex.org/W1558398159"],"abstract_inverted_index":{"Recently,":[0],"deep":[1],"convolutional":[2],"neural":[3],"networks":[4],"(CNNs)":[5],"have":[6,95],"achieved":[7],"great":[8],"success":[9],"in":[10,51,55,108],"semantic":[11,23,82,124],"segmentation":[12,24,126],"systems.":[13],"In":[14],"this":[15],"paper,":[16],"we":[17,36,58],"show":[18,116],"how":[19],"to":[20,44,67,79],"improve":[21],"pixel-wise":[22],"by":[25],"combine":[26],"both":[27],"global":[28,46],"context":[29],"information":[30],"and":[31,48,76,131],"local":[32,49,81],"image":[33,125],"features.":[34],"First,":[35],"implement":[37],"a":[38,60],"fusion":[39],"layer":[40],"that":[41,117],"allows":[42],"us":[43],"merge":[45],"features":[47,50,74],"encoder":[52],"network.":[53],"Second,":[54],"decoder":[56],"network,":[57],"introduce":[59],"stacked":[61],"pooling":[62],"block,":[63],"which":[64,91,133],"is":[65,77,87,106],"able":[66],"significantly":[68],"expand":[69],"the":[70,121],"receptive":[71],"fields":[72],"of":[73,123],"maps":[75],"essential":[78],"contextualize":[80],"predictions.":[83],"Furthermore,":[84],"our":[85,93,118],"approach":[86],"based":[88],"on":[89,127],"ResNet18,":[90],"makes":[92],"model":[94],"much":[96],"less":[97],"parameters":[98],"than":[99],"current":[100],"published":[101],"models.":[102],"The":[103],"whole":[104],"framework":[105],"trained":[107],"an":[109],"end-to-end":[110],"fashion":[111],"without":[112],"any":[113],"post-processing.":[114],"We":[115],"method":[119],"improves":[120],"performance":[122],"two":[128],"datasets":[129],"CamVid":[130],"Cityscapes,":[132],"demonstrate":[134],"its":[135],"effectiveness.":[136]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}