{"id":"https://openalex.org/W3133883948","doi":"https://doi.org/10.1109/ijcnn52387.2021.9534302","title":"Fast Interactive Video Object Segmentation with Graph Neural Networks","display_name":"Fast Interactive Video Object Segmentation with Graph Neural Networks","publication_year":2021,"publication_date":"2021-07-18","ids":{"openalex":"https://openalex.org/W3133883948","doi":"https://doi.org/10.1109/ijcnn52387.2021.9534302","mag":"3133883948"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn52387.2021.9534302","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9534302","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2103.03821","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5055832443","display_name":"Viktor Varga","orcid":"https://orcid.org/0000-0002-1665-969X"},"institutions":[{"id":"https://openalex.org/I106118109","display_name":"E\u00f6tv\u00f6s Lor\u00e1nd University","ror":"https://ror.org/01jsq2704","country_code":"HU","type":"education","lineage":["https://openalex.org/I106118109"]}],"countries":["HU"],"is_corresponding":true,"raw_author_name":"Viktor Varga","raw_affiliation_strings":["Faculty of Informatics, E\u00f6tv\u00f6s Lor\u00e1nd University, Budapest, Hungary","E\u00f6tv\u00f6s Lor\u00e1nd University"],"affiliations":[{"raw_affiliation_string":"Faculty of Informatics, E\u00f6tv\u00f6s Lor\u00e1nd University, Budapest, Hungary","institution_ids":["https://openalex.org/I106118109"]},{"raw_affiliation_string":"E\u00f6tv\u00f6s Lor\u00e1nd University","institution_ids":["https://openalex.org/I106118109"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066832439","display_name":"Andr\u00e1s L\u00f6rincz","orcid":"https://orcid.org/0000-0002-1280-3447"},"institutions":[{"id":"https://openalex.org/I106118109","display_name":"E\u00f6tv\u00f6s Lor\u00e1nd University","ror":"https://ror.org/01jsq2704","country_code":"HU","type":"education","lineage":["https://openalex.org/I106118109"]}],"countries":["HU"],"is_corresponding":false,"raw_author_name":"Andras Lorincz","raw_affiliation_strings":["Faculty of Informatics, E\u00f6tv\u00f6s Lor\u00e1nd University, Budapest, Hungary","E\u00f6tv\u00f6s Lor\u00e1nd University"],"affiliations":[{"raw_affiliation_string":"Faculty of Informatics, E\u00f6tv\u00f6s Lor\u00e1nd University, Budapest, Hungary","institution_ids":["https://openalex.org/I106118109"]},{"raw_affiliation_string":"E\u00f6tv\u00f6s Lor\u00e1nd University","institution_ids":["https://openalex.org/I106118109"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5055832443"],"corresponding_institution_ids":["https://openalex.org/I106118109"],"apc_list":null,"apc_paid":null,"fwci":0.10221948,"has_fulltext":true,"cited_by_count":1,"citation_normalized_percentile":{"value":0.34741233,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11605","display_name":"Visual Attention and Saliency Detection","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/T11605","display_name":"Visual Attention and Saliency Detection","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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","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/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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8481370210647583},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.699435293674469},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6752120852470398},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6479272246360779},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.6148130297660828},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5207502841949463},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5102031230926514},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4871775507926941},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.48221099376678467},{"id":"https://openalex.org/keywords/process","display_name":"Process (computing)","score":0.4444847106933594},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.41346675157546997},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3863057494163513},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.370526522397995},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.36838197708129883},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.15174943208694458}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8481370210647583},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.699435293674469},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6752120852470398},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6479272246360779},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.6148130297660828},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5207502841949463},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5102031230926514},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4871775507926941},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48221099376678467},{"id":"https://openalex.org/C98045186","wikidata":"https://www.wikidata.org/wiki/Q205663","display_name":"Process (computing)","level":2,"score":0.4444847106933594},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.41346675157546997},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3863057494163513},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.370526522397995},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.36838197708129883},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.15174943208694458},{"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/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/ijcnn52387.2021.9534302","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9534302","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 International Joint Conference on Neural Networks (IJCNN)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2103.03821","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2103.03821","pdf_url":"https://arxiv.org/pdf/2103.03821","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"mag:3133883948","is_oa":true,"landing_page_url":"http://arxiv.org/pdf/2103.03821.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.2103.03821","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2103.03821","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2103.03821","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2103.03821","pdf_url":"https://arxiv.org/pdf/2103.03821","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3133883948.pdf","grobid_xml":"https://content.openalex.org/works/W3133883948.grobid-xml"},"referenced_works_count":65,"referenced_works":["https://openalex.org/W589665618","https://openalex.org/W1501856433","https://openalex.org/W1560354729","https://openalex.org/W1836465849","https://openalex.org/W1943880421","https://openalex.org/W2023618850","https://openalex.org/W2101309634","https://openalex.org/W2102840968","https://openalex.org/W2113137767","https://openalex.org/W2116341502","https://openalex.org/W2124351162","https://openalex.org/W2143516773","https://openalex.org/W2145023731","https://openalex.org/W2189430190","https://openalex.org/W2194775991","https://openalex.org/W2200599981","https://openalex.org/W2462481369","https://openalex.org/W2560474170","https://openalex.org/W2585592883","https://openalex.org/W2597968591","https://openalex.org/W2605229288","https://openalex.org/W2624431344","https://openalex.org/W2766453196","https://openalex.org/W2768242641","https://openalex.org/W2795276939","https://openalex.org/W2799089472","https://openalex.org/W2803576056","https://openalex.org/W2806331055","https://openalex.org/W2890447039","https://openalex.org/W2905224888","https://openalex.org/W2907492528","https://openalex.org/W2916743882","https://openalex.org/W2954137266","https://openalex.org/W2963163009","https://openalex.org/W2967890649","https://openalex.org/W2970971581","https://openalex.org/W2987391422","https://openalex.org/W2990205821","https://openalex.org/W3013741643","https://openalex.org/W3021120485","https://openalex.org/W3033114334","https://openalex.org/W3034263000","https://openalex.org/W3034275286","https://openalex.org/W3035442500","https://openalex.org/W3080555959","https://openalex.org/W3107304365","https://openalex.org/W3111839891","https://openalex.org/W4210257598","https://openalex.org/W6617526114","https://openalex.org/W6638667902","https://openalex.org/W6688028962","https://openalex.org/W6735467293","https://openalex.org/W6735925877","https://openalex.org/W6738964360","https://openalex.org/W6745537798","https://openalex.org/W6746451923","https://openalex.org/W6751794770","https://openalex.org/W6751936687","https://openalex.org/W6754033419","https://openalex.org/W6757374366","https://openalex.org/W6766978945","https://openalex.org/W6775610265","https://openalex.org/W6780231044","https://openalex.org/W6781932242","https://openalex.org/W6787129352"],"related_works":["https://openalex.org/W2592414189","https://openalex.org/W3034798428","https://openalex.org/W2983670181","https://openalex.org/W2738569017","https://openalex.org/W3111983220","https://openalex.org/W2795068684","https://openalex.org/W1489833900","https://openalex.org/W3016743283","https://openalex.org/W3183813924","https://openalex.org/W2997742385","https://openalex.org/W3013441941","https://openalex.org/W2962919941","https://openalex.org/W2948495724","https://openalex.org/W2950305897","https://openalex.org/W2560563774","https://openalex.org/W2756191144","https://openalex.org/W2990682087","https://openalex.org/W2866912866","https://openalex.org/W3139559957","https://openalex.org/W2749895778"],"abstract_inverted_index":{"Pixelwise":[0],"annotation":[1],"of":[2,29,56,62,79,109,126],"image":[3],"sequences":[4],"can":[5,154],"be":[6,87,155],"very":[7,159],"tedious":[8],"for":[9,105],"humans.":[10],"Interactive":[11],"video":[12,111],"object":[13,112],"segmentation":[14],"aims":[15],"to":[16,20,40,66,86,122,145],"utilize":[17],"automatic":[18],"methods":[19],"speed":[21],"up":[22],"the":[23,27,30,49,107,124,127],"process":[24,43],"and":[25,42,58,89,153],"reduce":[26,123],"workload":[28],"annotators.":[31],"Most":[32],"contemporary":[33],"approaches":[34],"rely":[35],"on":[36,117],"deep":[37],"convolutional":[38],"networks":[39,53],"collect":[41],"information":[44],"from":[45],"human":[46],"annotations":[47],"throughout":[48],"video.":[50],"However,":[51],"such":[52],"contain":[54],"millions":[55],"parameters":[57,142],"need":[59],"huge":[60],"amounts":[61],"labeled":[63],"training":[64],"data":[65],"avoid":[67],"overfitting.":[68],"Beyond":[69],"that,":[70],"label":[71],"propagation":[72],"is":[73,84,90,143],"usually":[74],"executed":[75],"as":[76],"a":[77,99,139],"series":[78],"frame-by-frame":[80],"inference":[81,150],"steps,":[82],"which":[83,119],"difficult":[85],"parallelized":[88],"thus":[91],"time":[92],"consuming.":[93],"In":[94],"this":[95],"paper":[96],"we":[97],"present":[98],"graph":[100],"neural":[101],"network":[102,115,136],"based":[103],"approach":[104],"tackling":[106],"problem":[108,128],"interactive":[110],"segmentation.":[113],"Our":[114],"operates":[116],"superpixel-graphs":[118],"allow":[120],"us":[121],"dimensionality":[125],"by":[129],"several":[130],"magnitudes.":[131],"We":[132],"show,":[133],"that":[134],"our":[135],"possessing":[137],"only":[138],"few":[140],"thousand":[141],"able":[144],"achieve":[146],"state-of-the-art":[147],"performance,":[148],"while":[149],"remains":[151],"fast":[152],"trained":[156],"quickly":[157],"with":[158],"little":[160],"data.":[161]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2026-02-09T09:26:11.010843","created_date":"2025-10-10T00:00:00"}
