{"id":"https://openalex.org/W3048402753","doi":"https://doi.org/10.1109/tcsvt.2020.3015866","title":"Encoder-Decoder With Cascaded CRFs for Semantic Segmentation","display_name":"Encoder-Decoder With Cascaded CRFs for Semantic Segmentation","publication_year":2020,"publication_date":"2020-08-11","ids":{"openalex":"https://openalex.org/W3048402753","doi":"https://doi.org/10.1109/tcsvt.2020.3015866","mag":"3048402753"},"language":"en","primary_location":{"id":"doi:10.1109/tcsvt.2020.3015866","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcsvt.2020.3015866","pdf_url":null,"source":{"id":"https://openalex.org/S115173108","display_name":"IEEE Transactions on Circuits and Systems for Video Technology","issn_l":"1051-8215","issn":["1051-8215","1558-2205"],"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 Transactions on Circuits and Systems for Video Technology","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/A5065835586","display_name":"Jian Ji","orcid":"https://orcid.org/0000-0001-6427-6725"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jian Ji","raw_affiliation_strings":["School of Computer Science and Technology, Xidian University, Xi\u2019an, China"],"raw_orcid":"https://orcid.org/0000-0001-6427-6725","affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101404387","display_name":"Rui Shi","orcid":"https://orcid.org/0000-0002-7569-9894"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rui Shi","raw_affiliation_strings":["School of Computer Science and Technology, Xidian University, Xi\u2019an, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005401651","display_name":"Sitong Li","orcid":"https://orcid.org/0000-0002-2950-0047"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Sitong Li","raw_affiliation_strings":["School of Computer Science and Technology, Xidian University, Xi\u2019an, China"],"raw_orcid":"https://orcid.org/0000-0002-2950-0047","affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5025643309","display_name":"Peng Chen","orcid":"https://orcid.org/0000-0002-1669-0013"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Chen","raw_affiliation_strings":["School of Computer Science and Technology, Xidian University, Xi\u2019an, China"],"raw_orcid":"https://orcid.org/0000-0002-1669-0013","affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]}]},{"author_position":"last","author":{"id":null,"display_name":"Qiguang Miao","orcid":"https://orcid.org/0000-0001-6766-8310"},"institutions":[{"id":"https://openalex.org/I149594827","display_name":"Xidian University","ror":"https://ror.org/05s92vm98","country_code":"CN","type":"education","lineage":["https://openalex.org/I149594827"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qiguang Miao","raw_affiliation_strings":["School of Computer Science and Technology, Xidian University, Xi\u2019an, China"],"raw_orcid":"https://orcid.org/0000-0001-6766-8310","affiliations":[{"raw_affiliation_string":"School of Computer Science and Technology, Xidian University, Xi\u2019an, China","institution_ids":["https://openalex.org/I149594827"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I149594827"],"apc_list":null,"apc_paid":null,"fwci":5.5583,"has_fulltext":false,"cited_by_count":103,"citation_normalized_percentile":{"value":0.96925177,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":100},"biblio":{"volume":"31","issue":"5","first_page":"1926","last_page":"1938"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998000264167786,"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":0.9998000264167786,"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":0.9988999962806702,"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":0.9955999851226807,"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/crfs","display_name":"CRFS","score":0.9778079986572266},{"id":"https://openalex.org/keywords/conditional-random-field","display_name":"Conditional random field","score":0.7644150257110596},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7547603845596313},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6612613797187805},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6118006110191345},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.541641354560852},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5319027900695801},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.5304082632064819},{"id":"https://openalex.org/keywords/boundary","display_name":"Boundary (topology)","score":0.501521110534668},{"id":"https://openalex.org/keywords/scale-space-segmentation","display_name":"Scale-space segmentation","score":0.4813990592956543},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.47429683804512024},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.4416831135749817},{"id":"https://openalex.org/keywords/text-segmentation","display_name":"Text segmentation","score":0.4325818121433258},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1649305522441864}],"concepts":[{"id":"https://openalex.org/C2775953691","wikidata":"https://www.wikidata.org/wiki/Q5013874","display_name":"CRFS","level":3,"score":0.9778079986572266},{"id":"https://openalex.org/C152565575","wikidata":"https://www.wikidata.org/wiki/Q1124538","display_name":"Conditional random field","level":2,"score":0.7644150257110596},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7547603845596313},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6612613797187805},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6118006110191345},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.541641354560852},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5319027900695801},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.5304082632064819},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.501521110534668},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.4813990592956543},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.47429683804512024},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4416831135749817},{"id":"https://openalex.org/C98501671","wikidata":"https://www.wikidata.org/wiki/Q1948408","display_name":"Text segmentation","level":3,"score":0.4325818121433258},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1649305522441864},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"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/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tcsvt.2020.3015866","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tcsvt.2020.3015866","pdf_url":null,"source":{"id":"https://openalex.org/S115173108","display_name":"IEEE Transactions on Circuits and Systems for Video Technology","issn_l":"1051-8215","issn":["1051-8215","1558-2205"],"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 Transactions on Circuits and Systems for Video Technology","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W125693051","https://openalex.org/W1745334888","https://openalex.org/W1923697677","https://openalex.org/W1950117310","https://openalex.org/W1965511886","https://openalex.org/W2067912884","https://openalex.org/W2109255472","https://openalex.org/W2110764733","https://openalex.org/W2124592697","https://openalex.org/W2158305599","https://openalex.org/W2171943915","https://openalex.org/W2194775991","https://openalex.org/W2221898772","https://openalex.org/W2296478878","https://openalex.org/W2395611524","https://openalex.org/W2412782625","https://openalex.org/W2560023338","https://openalex.org/W2563705555","https://openalex.org/W2570343428","https://openalex.org/W2587989515","https://openalex.org/W2605938684","https://openalex.org/W2630837129","https://openalex.org/W2775906317","https://openalex.org/W2798376494","https://openalex.org/W2799390666","https://openalex.org/W2832876791","https://openalex.org/W2928165649","https://openalex.org/W2963136578","https://openalex.org/W2963323244","https://openalex.org/W2963346885","https://openalex.org/W2963418739","https://openalex.org/W2963446712","https://openalex.org/W2963881378","https://openalex.org/W2963977677","https://openalex.org/W2982093251","https://openalex.org/W3017378143","https://openalex.org/W3038020296","https://openalex.org/W3106728613","https://openalex.org/W3108986454","https://openalex.org/W3109301572","https://openalex.org/W4289367614","https://openalex.org/W6640295612","https://openalex.org/W6733387591","https://openalex.org/W6739696289","https://openalex.org/W6755402806","https://openalex.org/W6768371451","https://openalex.org/W6776593621"],"related_works":["https://openalex.org/W2356597680","https://openalex.org/W50079190","https://openalex.org/W182104056","https://openalex.org/W3108423214","https://openalex.org/W2011251309","https://openalex.org/W2796133761","https://openalex.org/W3088215229","https://openalex.org/W2511246383","https://openalex.org/W45206245","https://openalex.org/W2340365369"],"abstract_inverted_index":{"When":[0],"dealing":[1],"with":[2,67,144],"semantic":[3,50,106,132,168],"segmentation,":[4],"how":[5],"to":[6,18,58,109,129,195],"locate":[7,196],"the":[8,34,42,68,72,79,88,103,117,120,131,136,139,145,148,153,157,161,180,192],"object":[9,123,162],"boundary":[10,43,60,90,111,124,163,198],"information":[11,29,112,133],"more":[12,30,166],"accurately":[13],"is":[14,98,142],"a":[15,95,172],"key":[16],"problem":[17],"distinguish":[19],"different":[20,177],"objects":[21],"better.":[22],"The":[23],"existing":[24],"methods":[25,52],"lose":[26],"some":[27,49],"image":[28],"or":[31],"less":[32],"in":[33,122,127],"process":[35],"of":[36,71,82,93,105,119,134,138,147,159,174,184],"feature":[37],"extraction,":[38],"which":[39],"also":[40],"includes":[41],"and":[44,87,100,115,164,182],"context":[45],"information.":[46,199],"At":[47],"present,":[48],"segmentation":[51,107,169],"use":[53],"CRFs":[54,97,141],"(conditional":[55],"random":[56],"fields)":[57],"obtain":[59],"information,":[61],"but":[62],"they":[63],"usually":[64],"only":[65],"deal":[66],"final":[69],"output":[70,137,146],"model.":[73],"In":[74],"this":[75],"article,":[76],"inspired":[77],"by":[78],"skip":[80],"connection":[81],"FCN":[83],"(Fully":[84],"convolution":[85],"network)":[86],"good":[89],"refinement":[91],"ability":[92,118,158,194],"CRFs,":[94],"cascaded":[96,140],"designed":[99],"introduced":[101],"into":[102],"decoder":[104],"model":[108,121,154],"learn":[110],"from":[113],"multi-layers":[114],"enhance":[116,156],"location.":[125],"Furthermore,":[126],"order":[128],"supplement":[130],"images,":[135],"fused":[143],"last":[149],"decoder,":[150],"so":[151],"that":[152,188],"can":[155],"locating":[160],"get":[165],"accurate":[167],"results.":[170],"Finally,":[171],"number":[173],"experiments":[175],"on":[176],"datasets":[178],"illustrate":[179],"feasibility":[181],"efficiency":[183],"our":[185,189],"method,":[186],"showing":[187],"method":[190],"enhances":[191],"model\u2019s":[193],"target":[197]},"counts_by_year":[{"year":2026,"cited_by_count":4},{"year":2025,"cited_by_count":17},{"year":2024,"cited_by_count":24},{"year":2023,"cited_by_count":29},{"year":2022,"cited_by_count":20},{"year":2021,"cited_by_count":8},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}