{"id":"https://openalex.org/W3169503093","doi":"https://doi.org/10.1109/icme51207.2021.9428302","title":"Stereo Superpixel Segmentation Via Dual-Attention Fusion Networks","display_name":"Stereo Superpixel Segmentation Via Dual-Attention Fusion Networks","publication_year":2021,"publication_date":"2021-06-09","ids":{"openalex":"https://openalex.org/W3169503093","doi":"https://doi.org/10.1109/icme51207.2021.9428302","mag":"3169503093"},"language":"en","primary_location":{"id":"doi:10.1109/icme51207.2021.9428302","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme51207.2021.9428302","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Multimedia and Expo (ICME)","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/A5062752301","display_name":"Ruiqi Wu","orcid":"https://orcid.org/0000-0003-2384-2414"},"institutions":[{"id":"https://openalex.org/I196699116","display_name":"Wuhan University of Technology","ror":"https://ror.org/03fe7t173","country_code":"CN","type":"education","lineage":["https://openalex.org/I196699116"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ruiqi Wu","raw_affiliation_strings":["Wuhan University of Technology,School of Computer Science and Technology,China","School of Computer Science and Technology, Wuhan University of Technology, China"],"affiliations":[{"raw_affiliation_string":"Wuhan University of Technology,School of Computer Science and Technology,China","institution_ids":["https://openalex.org/I196699116"]},{"raw_affiliation_string":"School of Computer Science and Technology, Wuhan University of Technology, China","institution_ids":["https://openalex.org/I196699116"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071839026","display_name":"Yajuan Du","orcid":"https://orcid.org/0000-0002-8937-8055"},"institutions":[{"id":"https://openalex.org/I196699116","display_name":"Wuhan University of Technology","ror":"https://ror.org/03fe7t173","country_code":"CN","type":"education","lineage":["https://openalex.org/I196699116"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yajuan Du","raw_affiliation_strings":["Wuhan University of Technology,School of Computer Science and Technology,China","School of Computer Science and Technology, Wuhan University of Technology, China"],"affiliations":[{"raw_affiliation_string":"Wuhan University of Technology,School of Computer Science and Technology,China","institution_ids":["https://openalex.org/I196699116"]},{"raw_affiliation_string":"School of Computer Science and Technology, Wuhan University of Technology, China","institution_ids":["https://openalex.org/I196699116"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078094074","display_name":"Hua Li","orcid":"https://orcid.org/0000-0003-0740-0691"},"institutions":[{"id":"https://openalex.org/I168719708","display_name":"City University of Hong Kong","ror":"https://ror.org/03q8dnn23","country_code":"HK","type":"education","lineage":["https://openalex.org/I168719708"]},{"id":"https://openalex.org/I20942203","display_name":"Hainan University","ror":"https://ror.org/03q648j11","country_code":"CN","type":"education","lineage":["https://openalex.org/I20942203"]}],"countries":["CN","HK"],"is_corresponding":false,"raw_author_name":"Hua Li","raw_affiliation_strings":["Hainan University,School of Computer Science and Cyberspace Security,China","School of Computer Science and Cyberspace Security, Hainan University, China","Department of Computer Science, City University of Hong Kong, China"],"affiliations":[{"raw_affiliation_string":"Hainan University,School of Computer Science and Cyberspace Security,China","institution_ids":["https://openalex.org/I20942203"]},{"raw_affiliation_string":"School of Computer Science and Cyberspace Security, Hainan University, China","institution_ids":["https://openalex.org/I20942203"]},{"raw_affiliation_string":"Department of Computer Science, City University of Hong Kong, China","institution_ids":["https://openalex.org/I168719708"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5085704984","display_name":"Yucong Dai","orcid":null},"institutions":[{"id":"https://openalex.org/I196699116","display_name":"Wuhan University of Technology","ror":"https://ror.org/03fe7t173","country_code":"CN","type":"education","lineage":["https://openalex.org/I196699116"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yucong Dai","raw_affiliation_strings":["Wuhan University of Technology,School of Computer Science and Technology,China","School of Computer Science and Technology, Wuhan University of Technology, China"],"affiliations":[{"raw_affiliation_string":"Wuhan University of Technology,School of Computer Science and Technology,China","institution_ids":["https://openalex.org/I196699116"]},{"raw_affiliation_string":"School of Computer Science and Technology, Wuhan University of Technology, China","institution_ids":["https://openalex.org/I196699116"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5062752301"],"corresponding_institution_ids":["https://openalex.org/I196699116"],"apc_list":null,"apc_paid":null,"fwci":0.6233,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.71516616,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11659","display_name":"Advanced Image Fusion Techniques","score":0.9993000030517578,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.9987999796867371,"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/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9986000061035156,"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/artificial-intelligence","display_name":"Artificial intelligence","score":0.8530957102775574},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7888023853302002},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6634151339530945},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.559363842010498},{"id":"https://openalex.org/keywords/parallax","display_name":"Parallax","score":0.5088632106781006},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5055115222930908},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.475144624710083},{"id":"https://openalex.org/keywords/dual","display_name":"Dual (grammatical number)","score":0.44541293382644653},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.4150514304637909},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4030119478702545},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.2765619158744812}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.8530957102775574},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7888023853302002},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6634151339530945},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.559363842010498},{"id":"https://openalex.org/C15759828","wikidata":"https://www.wikidata.org/wiki/Q165074","display_name":"Parallax","level":2,"score":0.5088632106781006},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5055115222930908},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.475144624710083},{"id":"https://openalex.org/C2780980858","wikidata":"https://www.wikidata.org/wiki/Q110022","display_name":"Dual (grammatical number)","level":2,"score":0.44541293382644653},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.4150514304637909},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4030119478702545},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2765619158744812},{"id":"https://openalex.org/C142362112","wikidata":"https://www.wikidata.org/wiki/Q735","display_name":"Art","level":0,"score":0.0},{"id":"https://openalex.org/C124952713","wikidata":"https://www.wikidata.org/wiki/Q8242","display_name":"Literature","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icme51207.2021.9428302","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme51207.2021.9428302","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Multimedia and Expo (ICME)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8399999737739563,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":23,"referenced_works":["https://openalex.org/W1938929646","https://openalex.org/W2003060733","https://openalex.org/W2016872984","https://openalex.org/W2110158442","https://openalex.org/W2118246710","https://openalex.org/W2286655030","https://openalex.org/W2340897893","https://openalex.org/W2513634012","https://openalex.org/W2605910930","https://openalex.org/W2767623212","https://openalex.org/W2798380924","https://openalex.org/W2799046095","https://openalex.org/W2883606943","https://openalex.org/W2922435819","https://openalex.org/W2943125866","https://openalex.org/W2963091558","https://openalex.org/W2963420686","https://openalex.org/W2967085153","https://openalex.org/W2969377765","https://openalex.org/W3015582066","https://openalex.org/W3035241330","https://openalex.org/W6640464587","https://openalex.org/W6703848168"],"related_works":["https://openalex.org/W2310668644","https://openalex.org/W2329110763","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3193565141","https://openalex.org/W3133861977","https://openalex.org/W3167935049","https://openalex.org/W2964954556","https://openalex.org/W3029198973","https://openalex.org/W3019910406"],"abstract_inverted_index":{"Stereo":[0],"image":[1,59],"pairs":[2],"can":[3,144],"improve":[4],"performance":[5,148],"of":[6,87,102],"many":[7],"tasks":[8],"benefiting":[9],"from":[10,15,97],"the":[11,39,42,54,84,94,98,103,119,146,150],"additional":[12,95],"information":[13,56,96],"obtained":[14],"a":[16,78,111,125],"second":[17],"viewpoint":[18],"when":[19],"compared":[20],"with":[21,133],"single":[22,33],"images.":[23,89],"Existing":[24],"superpixel":[25],"segmentation":[26],"algorithms":[27],"for":[28,68],"stereo":[29,58,69,88,120],"images":[30,34,70],"mostly":[31],"adopt":[32],"as":[35],"input,":[36],"and":[37,44,61,105,114,152],"neglect":[38],"correspondence":[40],"between":[41,57],"left":[43,104],"right":[45,106],"views.":[46],"In":[47],"this":[48],"work,":[49],"we":[50],"consider":[51],"to":[52,71,82,91],"exploit":[53],"depth":[55],"pairs,":[60],"propose":[62],"an":[63],"end-to-end":[64,131],"dual-attention":[65],"fusion":[66],"network":[67,81],"generate":[72],"parallax-consistency":[73],"superpixels.":[74],"We":[75],"first":[76],"utilize":[77,93],"deep":[79,85,134],"convolution":[80],"extract":[83],"features":[86,101],"Then,":[90],"effectively":[92],"other":[99],"view,":[100],"views":[107],"is":[108,130],"integrated":[109],"by":[110,124],"parallax":[112],"attention":[113,116],"channel":[115],"mechanism.":[117],"Finally,":[118],"superpixels":[121],"are":[122],"generated":[123],"differentiable":[126],"clustering":[127],"algorithm,":[128],"which":[129],"trainable":[132],"learning":[135],"networks.":[136],"Comprehensive":[137],"experimental":[138],"results":[139],"demonstrate":[140],"that":[141],"our":[142],"method":[143],"outperform":[145],"state-of-the-art":[147],"on":[149],"KITTI2015":[151],"Cityscapes":[153],"dataset.":[154]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":4}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}