{"id":"https://openalex.org/W3201655941","doi":"https://doi.org/10.1109/ijcnn52387.2021.9534106","title":"Optical Flow Estimation through Fusion Network based on Self-supervised Deep Learning","display_name":"Optical Flow Estimation through Fusion Network based on Self-supervised Deep Learning","publication_year":2021,"publication_date":"2021-07-18","ids":{"openalex":"https://openalex.org/W3201655941","doi":"https://doi.org/10.1109/ijcnn52387.2021.9534106","mag":"3201655941"},"language":"en","primary_location":{"id":"doi:10.1109/ijcnn52387.2021.9534106","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9534106","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":"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/A5103868831","display_name":"Cong Liu","orcid":"https://orcid.org/0000-0002-7313-9711"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Cong Liu","raw_affiliation_strings":["College of Computer, National University of Defense Technology (NUDT), Changsha, China"],"affiliations":[{"raw_affiliation_string":"College of Computer, National University of Defense Technology (NUDT), Changsha, China","institution_ids":["https://openalex.org/I170215575"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019758816","display_name":"Dianxi Shi","orcid":"https://orcid.org/0000-0002-8112-371X"},"institutions":[{"id":"https://openalex.org/I170215575","display_name":"National University of Defense Technology","ror":"https://ror.org/05d2yfz11","country_code":"CN","type":"education","lineage":["https://openalex.org/I170215575"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dianxi Shi","raw_affiliation_strings":["National Innovation Institute of Defense Technology (NIIDT), Beijing, China","Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin, China"],"affiliations":[{"raw_affiliation_string":"National Innovation Institute of Defense Technology (NIIDT), Beijing, China","institution_ids":["https://openalex.org/I170215575"]},{"raw_affiliation_string":"Tianjin Artificial Intelligence Innovation Center (TAIIC), Tianjin, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101477550","display_name":"Ruihao Li","orcid":"https://orcid.org/0000-0002-9839-1489"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ruihao Li","raw_affiliation_strings":["Artificial Intelligence Research Center (AIRC), National Innovation Institute of Defense Technology (NIIDT), Beijing, China"],"affiliations":[{"raw_affiliation_string":"Artificial Intelligence Research Center (AIRC), National Innovation Institute of Defense Technology (NIIDT), Beijing, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055645026","display_name":"Huachi Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Huachi Xu","raw_affiliation_strings":["Artificial Intelligence Research Center (AIRC), National Innovation Institute of Defense Technology (NIIDT), Beijing, China"],"affiliations":[{"raw_affiliation_string":"Artificial Intelligence Research Center (AIRC), National Innovation Institute of Defense Technology (NIIDT), Beijing, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5103868831"],"corresponding_institution_ids":["https://openalex.org/I170215575"],"apc_list":null,"apc_paid":null,"fwci":0.1003,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.4347588,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":"abs 1805 3988","issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"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/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9995999932289124,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"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/T11992","display_name":"CCD and CMOS Imaging Sensors","score":0.9991999864578247,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"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/T13114","display_name":"Image Processing Techniques and Applications","score":0.9927999973297119,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8079633712768555},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7188176512718201},{"id":"https://openalex.org/keywords/optical-flow","display_name":"Optical flow","score":0.6386911273002625},{"id":"https://openalex.org/keywords/fusion","display_name":"Fusion","score":0.5163058638572693},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.49391990900039673},{"id":"https://openalex.org/keywords/supervised-learning","display_name":"Supervised learning","score":0.46018630266189575},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.43652117252349854},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.42465686798095703},{"id":"https://openalex.org/keywords/sensor-fusion","display_name":"Sensor fusion","score":0.41309791803359985},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3774676024913788},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.35854145884513855},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3265293538570404},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.2875739634037018}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8079633712768555},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7188176512718201},{"id":"https://openalex.org/C155542232","wikidata":"https://www.wikidata.org/wiki/Q736111","display_name":"Optical flow","level":3,"score":0.6386911273002625},{"id":"https://openalex.org/C158525013","wikidata":"https://www.wikidata.org/wiki/Q2593739","display_name":"Fusion","level":2,"score":0.5163058638572693},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.49391990900039673},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.46018630266189575},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.43652117252349854},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.42465686798095703},{"id":"https://openalex.org/C33954974","wikidata":"https://www.wikidata.org/wiki/Q486494","display_name":"Sensor fusion","level":2,"score":0.41309791803359985},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3774676024913788},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.35854145884513855},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3265293538570404},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2875739634037018},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","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},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ijcnn52387.2021.9534106","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ijcnn52387.2021.9534106","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"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G1860839707","display_name":null,"funder_award_id":"2017YFB1001901","funder_id":"https://openalex.org/F4320335777","funder_display_name":"National Key Research and Development Program of China"}],"funders":[{"id":"https://openalex.org/F4320335777","display_name":"National Key Research and Development Program of China","ror":null}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":40,"referenced_works":["https://openalex.org/W206948248","https://openalex.org/W764651262","https://openalex.org/W1901129140","https://openalex.org/W1975697167","https://openalex.org/W1998339166","https://openalex.org/W2016574277","https://openalex.org/W2040324237","https://openalex.org/W2118877769","https://openalex.org/W2155302366","https://openalex.org/W2400276193","https://openalex.org/W2469278928","https://openalex.org/W2507953016","https://openalex.org/W2560474170","https://openalex.org/W2739136981","https://openalex.org/W2745933219","https://openalex.org/W2750704532","https://openalex.org/W2752782242","https://openalex.org/W2752940099","https://openalex.org/W2783185291","https://openalex.org/W2785582094","https://openalex.org/W2788172931","https://openalex.org/W2796368857","https://openalex.org/W2800907080","https://openalex.org/W2802377087","https://openalex.org/W2884195444","https://openalex.org/W2890113354","https://openalex.org/W2963510238","https://openalex.org/W2992384714","https://openalex.org/W3035642679","https://openalex.org/W3092083701","https://openalex.org/W3097980825","https://openalex.org/W3099039688","https://openalex.org/W3105213754","https://openalex.org/W6639824700","https://openalex.org/W6677548441","https://openalex.org/W6743261063","https://openalex.org/W6743661683","https://openalex.org/W6750667323","https://openalex.org/W6753202846","https://openalex.org/W6754167189"],"related_works":["https://openalex.org/W4375867731","https://openalex.org/W4386083130","https://openalex.org/W2611989081","https://openalex.org/W2117442182","https://openalex.org/W2132659060","https://openalex.org/W2031992971","https://openalex.org/W3214791684","https://openalex.org/W2353265673","https://openalex.org/W2152662039","https://openalex.org/W2145797872"],"abstract_inverted_index":{"Compared":[0],"with":[1,54],"conventional":[2],"cameras,":[3],"dynamic":[4,23],"vision":[5],"sensors":[6],"(namely":[7],"event":[8,56,97,135],"cameras)":[9],"are":[10,69,124],"especially":[11],"suitable":[12],"for":[13],"high-speed":[14],"and":[15,35,58,71,83,99,103,147],"high-dynamic":[16],"applications":[17],"due":[18],"to":[19,115,126,138],"their":[20],"advantages":[21],"(high":[22],"range,":[24],"low":[25],"latency,":[26],"etc.).":[27],"However,":[28],"it":[29],"still":[30],"struggles":[31],"when":[32],"encountering":[33],"low-speed":[34],"low-texture":[36],"scenes.":[37],"Aiming":[38],"at":[39],"the":[40,63,78,84,128,133,140,161],"problem":[41],"of":[42,160],"optical":[43,142],"flow":[44,143],"estimation,":[45],"we":[46],"propose":[47],"a":[48,106],"novel":[49],"unsupervised":[50],"learning":[51],"estimation":[52,144,162],"method":[53,145,154],"both":[55],"data":[57],"gray":[59,100],"image":[60,101],"frames":[61,98,123],"as":[62],"input.":[64],"Two":[65],"different":[66],"fusion":[67],"mechanisms":[68],"presented":[70],"discussed":[72],"in":[73,158],"our":[74,153],"work,":[75],"one":[76,86],"is":[77,87],"Direct":[79],"Fusion":[80],"Network":[81,91],"(DFEV-FlowNet),":[82],"other":[85],"Local":[88],"Squeeze":[89],"Extraction":[90],"(LSENet).":[92],"DFEV-FlowNet":[93],"directly":[94],"fuses":[95],"synthesized":[96],"frames,":[102],"LSENet":[104],"adopts":[105],"local":[107],"squeeze":[108],"extraction":[109],"weights":[110],"adaptive":[111],"mechanism.":[112],"In":[113],"order":[114],"achieve":[116],"self-supervised":[117],"learning,":[118],"photometric":[119],"constraints":[120],"between":[121],"consecutive":[122],"used":[125],"drive":[127],"network":[129],"training.":[130],"We":[131],"use":[132],"public":[134],"dataset":[136],"MVSEC":[137],"evaluate":[139],"proposed":[141],"qualitatively":[146],"quantitatively.":[148],"The":[149],"results":[150],"show":[151],"that":[152],"demonstrate":[155],"better":[156],"performance":[157],"terms":[159],"accuracy.":[163]},"counts_by_year":[{"year":2024,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}