{"id":"https://openalex.org/W4391741850","doi":"https://doi.org/10.1109/tim.2024.3365160","title":"EV-MGRFlowNet: Motion-Guided Recurrent Network for Unsupervised Event-Based Optical Flow With Hybrid Motion-Compensation Loss","display_name":"EV-MGRFlowNet: Motion-Guided Recurrent Network for Unsupervised Event-Based Optical Flow With Hybrid Motion-Compensation Loss","publication_year":2024,"publication_date":"2024-01-01","ids":{"openalex":"https://openalex.org/W4391741850","doi":"https://doi.org/10.1109/tim.2024.3365160"},"language":"en","primary_location":{"id":"doi:10.1109/tim.2024.3365160","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2024.3365160","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"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 Instrumentation and Measurement","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/A5100613968","display_name":"Hao Zhuang","orcid":"https://orcid.org/0000-0002-3731-7162"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Hao Zhuang","raw_affiliation_strings":["Faculty of Robot Science and Engineering and the Key Laboratory of Data Analytics and Optimization for Smart Industry, Ministry of Education, Northeastern University, Shenyang, China","National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Shenyang, China","Faculty of Robot Science and Engineering, Northeastern University, Shenyang, China"],"affiliations":[{"raw_affiliation_string":"Faculty of Robot Science and Engineering and the Key Laboratory of Data Analytics and Optimization for Smart Industry, Ministry of Education, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]},{"raw_affiliation_string":"National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Shenyang, China","institution_ids":[]},{"raw_affiliation_string":"Faculty of Robot Science and Engineering, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064687463","display_name":"Zheng Fang","orcid":"https://orcid.org/0000-0003-3887-3141"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zheng Fang","raw_affiliation_strings":["Faculty of Robot Science and Engineering and the Key Laboratory of Data Analytics and Optimization for Smart Industry, Ministry of Education, Northeastern University, Shenyang, China","National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Shenyang, China","Faculty of Robot Science and Engineering, Northeastern University, Shenyang, China"],"affiliations":[{"raw_affiliation_string":"Faculty of Robot Science and Engineering and the Key Laboratory of Data Analytics and Optimization for Smart Industry, Ministry of Education, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]},{"raw_affiliation_string":"National Frontiers Science Center for Industrial Intelligence and Systems Optimization, Shenyang, China","institution_ids":[]},{"raw_affiliation_string":"Faculty of Robot Science and Engineering, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5091584780","display_name":"Xinjie Huang","orcid":"https://orcid.org/0000-0002-9367-2476"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xinjie Huang","raw_affiliation_strings":["Faculty of Robot Science and Engineering, Northeastern University, Shenyang, China"],"affiliations":[{"raw_affiliation_string":"Faculty of Robot Science and Engineering, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037817267","display_name":"Kuanxu Hou","orcid":"https://orcid.org/0000-0001-5009-8171"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Kuanxu Hou","raw_affiliation_strings":["Faculty of Robot Science and Engineering, Northeastern University, Shenyang, China"],"affiliations":[{"raw_affiliation_string":"Faculty of Robot Science and Engineering, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5024368503","display_name":"Delei Kong","orcid":"https://orcid.org/0000-0002-5681-587X"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Delei Kong","raw_affiliation_strings":["Faculty of Robot Science and Engineering, Northeastern University, Shenyang, China"],"affiliations":[{"raw_affiliation_string":"Faculty of Robot Science and Engineering, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5108612551","display_name":"Chenming Hu","orcid":"https://orcid.org/0009-0000-9599-3401"},"institutions":[{"id":"https://openalex.org/I9224756","display_name":"Northeastern University","ror":"https://ror.org/03awzbc87","country_code":"CN","type":"education","lineage":["https://openalex.org/I9224756"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chenming Hu","raw_affiliation_strings":["Faculty of Robot Science and Engineering, Northeastern University, Shenyang, China"],"affiliations":[{"raw_affiliation_string":"Faculty of Robot Science and Engineering, Northeastern University, Shenyang, China","institution_ids":["https://openalex.org/I9224756"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5100613968"],"corresponding_institution_ids":["https://openalex.org/I9224756"],"apc_list":null,"apc_paid":null,"fwci":2.2965,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.88014155,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":"73","issue":null,"first_page":"1","last_page":"15"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10502","display_name":"Advanced Memory and Neural Computing","score":0.9998000264167786,"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":0.9998000264167786,"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/T12611","display_name":"Neural Networks and Reservoir Computing","score":0.9973000288009644,"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/T11992","display_name":"CCD and CMOS Imaging Sensors","score":0.9940999746322632,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/optical-flow","display_name":"Optical flow","score":0.7945808172225952},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7620469331741333},{"id":"https://openalex.org/keywords/motion-compensation","display_name":"Motion compensation","score":0.658343493938446},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6525435447692871},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5379387140274048},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5287669897079468},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.5153447985649109},{"id":"https://openalex.org/keywords/motion-estimation","display_name":"Motion estimation","score":0.512871503829956},{"id":"https://openalex.org/keywords/compensation","display_name":"Compensation (psychology)","score":0.5019228458404541},{"id":"https://openalex.org/keywords/quarter-pixel-motion","display_name":"Quarter-pixel motion","score":0.49685099720954895},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.47986453771591187},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.4561828672885895},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.4558990001678467},{"id":"https://openalex.org/keywords/unsupervised-learning","display_name":"Unsupervised learning","score":0.43018850684165955},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3552563786506653},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.09867709875106812}],"concepts":[{"id":"https://openalex.org/C155542232","wikidata":"https://www.wikidata.org/wiki/Q736111","display_name":"Optical flow","level":3,"score":0.7945808172225952},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7620469331741333},{"id":"https://openalex.org/C128840427","wikidata":"https://www.wikidata.org/wiki/Q1302174","display_name":"Motion compensation","level":2,"score":0.658343493938446},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6525435447692871},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5379387140274048},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5287669897079468},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.5153447985649109},{"id":"https://openalex.org/C10161872","wikidata":"https://www.wikidata.org/wiki/Q557891","display_name":"Motion estimation","level":2,"score":0.512871503829956},{"id":"https://openalex.org/C2780023022","wikidata":"https://www.wikidata.org/wiki/Q1338171","display_name":"Compensation (psychology)","level":2,"score":0.5019228458404541},{"id":"https://openalex.org/C174493125","wikidata":"https://www.wikidata.org/wiki/Q1073461","display_name":"Quarter-pixel motion","level":3,"score":0.49685099720954895},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.47986453771591187},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.4561828672885895},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.4558990001678467},{"id":"https://openalex.org/C8038995","wikidata":"https://www.wikidata.org/wiki/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.43018850684165955},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3552563786506653},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.09867709875106812},{"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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C11171543","wikidata":"https://www.wikidata.org/wiki/Q41630","display_name":"Psychoanalysis","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},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/tim.2024.3365160","is_oa":false,"landing_page_url":"https://doi.org/10.1109/tim.2024.3365160","pdf_url":null,"source":{"id":"https://openalex.org/S10892749","display_name":"IEEE Transactions on Instrumentation and Measurement","issn_l":"0018-9456","issn":["0018-9456","1557-9662"],"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 Instrumentation and Measurement","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2311391187","display_name":null,"funder_award_id":"B16009","funder_id":"https://openalex.org/F4320327912","funder_display_name":"Higher Education Discipline Innovation Project"},{"id":"https://openalex.org/G2871798451","display_name":null,"funder_award_id":"N2226001","funder_id":"https://openalex.org/F4320335787","funder_display_name":"Fundamental Research Funds for the Central Universities"},{"id":"https://openalex.org/G6509363187","display_name":null,"funder_award_id":"62073066","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"},{"id":"https://openalex.org/G8544294156","display_name":null,"funder_award_id":"U20A20197","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"},{"id":"https://openalex.org/F4320327912","display_name":"Higher Education Discipline Innovation Project","ror":null},{"id":"https://openalex.org/F4320335787","display_name":"Fundamental Research Funds for the Central Universities","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":57,"referenced_works":["https://openalex.org/W2035379092","https://openalex.org/W2567239141","https://openalex.org/W2785582094","https://openalex.org/W2788172931","https://openalex.org/W2796368857","https://openalex.org/W2796402180","https://openalex.org/W2883294120","https://openalex.org/W2904275768","https://openalex.org/W2963149042","https://openalex.org/W2967293965","https://openalex.org/W2967686751","https://openalex.org/W2968243907","https://openalex.org/W2980116241","https://openalex.org/W2982688540","https://openalex.org/W2998281665","https://openalex.org/W3039515597","https://openalex.org/W3040838455","https://openalex.org/W3085759494","https://openalex.org/W3092083701","https://openalex.org/W3097980825","https://openalex.org/W3102178346","https://openalex.org/W3105213754","https://openalex.org/W3106188738","https://openalex.org/W3109192943","https://openalex.org/W3109908659","https://openalex.org/W3122190729","https://openalex.org/W3127641627","https://openalex.org/W3129601843","https://openalex.org/W3138915575","https://openalex.org/W3173114505","https://openalex.org/W3177222833","https://openalex.org/W3187634853","https://openalex.org/W3200334356","https://openalex.org/W3205291758","https://openalex.org/W3212566730","https://openalex.org/W3215059102","https://openalex.org/W4205106798","https://openalex.org/W4205419872","https://openalex.org/W4205982568","https://openalex.org/W4225886149","https://openalex.org/W4285043186","https://openalex.org/W4285272888","https://openalex.org/W4312279626","https://openalex.org/W4312506424","https://openalex.org/W4313029475","https://openalex.org/W4313190957","https://openalex.org/W4321392758","https://openalex.org/W4366503954","https://openalex.org/W4367146527","https://openalex.org/W4368232643","https://openalex.org/W4385245566","https://openalex.org/W4387010759","https://openalex.org/W6685670348","https://openalex.org/W6745829810","https://openalex.org/W6840495885","https://openalex.org/W6849908995","https://openalex.org/W6991013602"],"related_works":["https://openalex.org/W1579157894","https://openalex.org/W1530267862","https://openalex.org/W2153529351","https://openalex.org/W2016848112","https://openalex.org/W1967654336","https://openalex.org/W1541334882","https://openalex.org/W2157391823","https://openalex.org/W2040149295","https://openalex.org/W2140285056","https://openalex.org/W2169116484"],"abstract_inverted_index":{"Event":[0],"cameras":[1],"offer":[2],"promising":[3],"properties,":[4],"such":[5],"as":[6],"high":[7,11],"temporal":[8],"resolution":[9],"and":[10,50],"dynamic":[12],"range.":[13],"These":[14],"benefits":[15],"have":[16,43],"been":[17],"utilized":[18],"into":[19],"many":[20],"machine":[21],"vision":[22],"tasks,":[23],"especially":[24],"optical":[25,38,82],"flow":[26,83],"estimation.":[27],"Currently,":[28],"most":[29],"existing":[30],"event-based":[31,81],"works":[32],"use":[33],"deep":[34],"learning":[35],"to":[36,67,109,121,134],"estimate":[37],"flow.":[39],"However,":[40],"their":[41,54],"networks":[42,89],"not":[44,58],"fully":[45,59,104],"exploited":[46],"prior":[47,106,123],"hidden":[48,107],"states":[49,108],"motion":[51,112,124],"flows.":[52,125],"Additionally,":[53],"supervision":[55],"strategy":[56],"has":[57],"leveraged":[60],"the":[61,69,139,152,158],"geometric":[62,136],"constraints":[63,137],"of":[64,71,143,165],"event":[65],"data":[66],"unlock":[68],"potential":[70],"networks.":[72],"In":[73],"this":[74],"paper,":[75],"we":[76,96,115,127],"propose":[77,97,116],"EV-MGRFlowNet,":[78],"an":[79,162],"unsupervised":[80,181],"estimation":[84],"pipeline":[85],"with":[86,161],"motion-guided":[87],"recurrent":[88,100],"using":[90],"a":[91,98,117,129],"hybrid":[92,130],"motion-compensation":[93,131],"loss.":[94],"First,":[95],"feature-enhanced":[99],"encoder":[101],"(FER-Encoder)":[102],"which":[103],"utilizes":[105],"obtain":[110],"multi-level":[111],"features.":[113],"Then,":[114],"flow-guided":[118],"decoder":[119],"(FG-Decoder)":[120],"integrate":[122],"Finally,":[126],"design":[128],"loss":[132],"(HMC-Loss)":[133],"strengthen":[135],"for":[138],"more":[140],"accurate":[141],"alignment":[142],"events.":[144],"Experimental":[145],"results":[146],"show":[147],"that":[148],"our":[149,174,176],"method":[150,156,177],"outperforms":[151],"current":[153],"state-of-the-art":[154],"(SOTA)":[155],"on":[157],"MVSEC":[159],"dataset,":[160],"average":[163,169],"reduction":[164],"approximately":[166],"22.71%":[167],"in":[168],"endpoint":[170],"error":[171],"(AEE).":[172],"To":[173],"knowledge,":[175],"ranks":[178],"first":[179],"among":[180],"learning-based":[182],"methods.":[183]},"counts_by_year":[{"year":2025,"cited_by_count":7},{"year":2024,"cited_by_count":4}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}
