{"id":"https://openalex.org/W4405270558","doi":"https://doi.org/10.1109/cvmi61877.2024.10782648","title":"SERNet-Former: Segmentation by Efficient-ResNet with Attention-Boosting Gates and Attention-Fusion Networks","display_name":"SERNet-Former: Segmentation by Efficient-ResNet with Attention-Boosting Gates and Attention-Fusion Networks","publication_year":2024,"publication_date":"2024-10-19","ids":{"openalex":"https://openalex.org/W4405270558","doi":"https://doi.org/10.1109/cvmi61877.2024.10782648"},"language":"en","primary_location":{"id":"doi:10.1109/cvmi61877.2024.10782648","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvmi61877.2024.10782648","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Computer Vision and Machine Intelligence (CVMI)","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/A5027544217","display_name":"Serdar Eri\u015fen","orcid":"https://orcid.org/0000-0002-7192-0889"},"institutions":[{"id":"https://openalex.org/I66514158","display_name":"Hacettepe University","ror":"https://ror.org/04kwvgz42","country_code":"TR","type":"education","lineage":["https://openalex.org/I66514158"]}],"countries":["TR"],"is_corresponding":true,"raw_author_name":"Serdar Eri\u015fen","raw_affiliation_strings":["Hacettepe University,Faculty of Architecture,Ankara,Turkey"],"affiliations":[{"raw_affiliation_string":"Hacettepe University,Faculty of Architecture,Ankara,Turkey","institution_ids":["https://openalex.org/I66514158"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5027544217"],"corresponding_institution_ids":["https://openalex.org/I66514158"],"apc_list":null,"apc_paid":null,"fwci":6.0868,"has_fulltext":false,"cited_by_count":19,"citation_normalized_percentile":{"value":0.96745199,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":100},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11323","display_name":"Gamma-ray bursts and supernovae","score":0.954800009727478,"subfield":{"id":"https://openalex.org/subfields/3103","display_name":"Astronomy and Astrophysics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11323","display_name":"Gamma-ray bursts and supernovae","score":0.954800009727478,"subfield":{"id":"https://openalex.org/subfields/3103","display_name":"Astronomy and Astrophysics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13937","display_name":"Genetics, Bioinformatics, and Biomedical Research","score":0.910099983215332,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.9112555980682373},{"id":"https://openalex.org/keywords/residual-neural-network","display_name":"Residual neural network","score":0.773938775062561},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6567558646202087},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5793185830116272},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5647268295288086},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.4546452462673187},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.27855414152145386}],"concepts":[{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.9112555980682373},{"id":"https://openalex.org/C2944601119","wikidata":"https://www.wikidata.org/wiki/Q43744058","display_name":"Residual neural network","level":3,"score":0.773938775062561},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6567558646202087},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5793185830116272},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5647268295288086},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.4546452462673187},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.27855414152145386},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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":1,"locations":[{"id":"doi:10.1109/cvmi61877.2024.10782648","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvmi61877.2024.10782648","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE International Conference on Computer Vision and Machine Intelligence (CVMI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2125652721","https://openalex.org/W1540371141","https://openalex.org/W1549363203","https://openalex.org/W2147697413","https://openalex.org/W2154063878","https://openalex.org/W4231274751","https://openalex.org/W2556012038","https://openalex.org/W1489772951","https://openalex.org/W1538046993","https://openalex.org/W2571255492"],"abstract_inverted_index":{"This":[0],"research":[1,91],"proposes":[2],"an":[3],"encoder-decoder":[4],"architecture":[5],"with":[6,19,44],"a":[7],"unique,":[8],"efficient":[9,33],"residual":[10,34,94],"network,":[11,105],"Efficient-ResNet.":[12],"Attention-boosting":[13],"gates":[14],"and":[15,24,83,117],"modules":[16],"are":[17,55],"fused":[18],"the":[20,25,28,32,37,50,60,72,80,93,98,103,114,124,128],"feature-based":[21],"semantic":[22,64],"information":[23,65],"output":[26],"of":[27,31,63,100],"global":[29],"context":[30],"network":[35,41,76],"in":[36,71,89],"encoder.":[38],"The":[39,86],"decoder":[40,73],"is":[42,77],"developed":[43,104],"additional":[45,68],"attention-fusion":[46],"networks":[47,54,95],"inspired":[48],"by":[49,66],"attention-boosting":[51],"modules.":[52],"Attention-fusion":[53],"designed":[56],"to":[57],"efficiently":[58],"improve":[59,92],"one-to-one":[61],"conversion":[62],"deploying":[67],"convolution":[69],"layers":[70],"part.":[74],"Our":[75],"tested":[78],"on":[79,113,123,127],"challenging":[81,118],"CamVid":[82,115],"Cityscapes":[84,129],"datasets.":[85,130],"proposed":[87],"methods":[88],"this":[90],"significantly.":[96],"To":[97],"best":[99],"our":[101],"knowledge,":[102],"SERNet-Former,":[106],"achieves":[107],"state-of-the-art":[108],"results":[109,119],"(84.62%":[110],"mean":[111,121],"IoU)":[112],"dataset":[116],"(87.35%":[120],"IoU":[122],"validation":[125],"dataset)":[126]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":17}],"updated_date":"2026-04-23T09:07:50.710637","created_date":"2025-10-10T00:00:00"}
