{"id":"https://openalex.org/W4416748961","doi":"https://doi.org/10.1109/iros60139.2025.11246061","title":"KDMOS:Knowledge Distillation for Motion Segmentation","display_name":"KDMOS:Knowledge Distillation for Motion Segmentation","publication_year":2025,"publication_date":"2025-10-19","ids":{"openalex":"https://openalex.org/W4416748961","doi":"https://doi.org/10.1109/iros60139.2025.11246061"},"language":null,"primary_location":{"id":"doi:10.1109/iros60139.2025.11246061","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros60139.2025.11246061","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","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":null,"display_name":"Chunyu Cao","orcid":null},"institutions":[{"id":"https://openalex.org/I187400657","display_name":"South China Normal University","ror":"https://ror.org/01kq0pv72","country_code":"CN","type":"education","lineage":["https://openalex.org/I187400657"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Chunyu Cao","raw_affiliation_strings":["South China Normal University,School of Electronic and Information Engineering,Foshan,China,528225"],"affiliations":[{"raw_affiliation_string":"South China Normal University,School of Electronic and Information Engineering,Foshan,China,528225","institution_ids":["https://openalex.org/I187400657"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114973857","display_name":"Jintao Cheng","orcid":"https://orcid.org/0009-0008-0121-4162"},"institutions":[{"id":"https://openalex.org/I187400657","display_name":"South China Normal University","ror":"https://ror.org/01kq0pv72","country_code":"CN","type":"education","lineage":["https://openalex.org/I187400657"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jintao Cheng","raw_affiliation_strings":["South China Normal University,School of Electronic and Information Engineering,Foshan,China,528225"],"affiliations":[{"raw_affiliation_string":"South China Normal University,School of Electronic and Information Engineering,Foshan,China,528225","institution_ids":["https://openalex.org/I187400657"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009314726","display_name":"Zeyu Chen","orcid":"https://orcid.org/0000-0003-2765-7902"},"institutions":[{"id":"https://openalex.org/I187400657","display_name":"South China Normal University","ror":"https://ror.org/01kq0pv72","country_code":"CN","type":"education","lineage":["https://openalex.org/I187400657"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zeyu Chen","raw_affiliation_strings":["South China Normal University,School of Electronic and Information Engineering,Foshan,China,528225"],"affiliations":[{"raw_affiliation_string":"South China Normal University,School of Electronic and Information Engineering,Foshan,China,528225","institution_ids":["https://openalex.org/I187400657"]}]},{"author_position":"middle","author":{"id":null,"display_name":"Linfan Zhan","orcid":null},"institutions":[{"id":"https://openalex.org/I187400657","display_name":"South China Normal University","ror":"https://ror.org/01kq0pv72","country_code":"CN","type":"education","lineage":["https://openalex.org/I187400657"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Linfan Zhan","raw_affiliation_strings":["South China Normal University,School of Electronic and Information Engineering,Foshan,China,528225"],"affiliations":[{"raw_affiliation_string":"South China Normal University,School of Electronic and Information Engineering,Foshan,China,528225","institution_ids":["https://openalex.org/I187400657"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007508326","display_name":"Rui Fan","orcid":"https://orcid.org/0000-0001-8768-932X"},"institutions":[{"id":"https://openalex.org/I116953780","display_name":"Tongji University","ror":"https://ror.org/03rc6as71","country_code":"CN","type":"education","lineage":["https://openalex.org/I116953780"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Rui Fan","raw_affiliation_strings":["Tongji University,College of Electronics &amp; Information Engineering, Shanghai Research Institute for Intelligent Autonomous Systems, the State Key Laboratory of Intelligent Autonomous Systems, and Frontiers Science Center for Intelligent Autonomous Systems,Shanghai,China,201804"],"affiliations":[{"raw_affiliation_string":"Tongji University,College of Electronics &amp; Information Engineering, Shanghai Research Institute for Intelligent Autonomous Systems, the State Key Laboratory of Intelligent Autonomous Systems, and Frontiers Science Center for Intelligent Autonomous Systems,Shanghai,China,201804","institution_ids":["https://openalex.org/I116953780"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050790810","display_name":"Zhilong He","orcid":"https://orcid.org/0000-0003-0589-4069"},"institutions":[{"id":"https://openalex.org/I4210152380","display_name":"Shenzhen Technology University","ror":"https://ror.org/04qzpec27","country_code":"CN","type":"education","lineage":["https://openalex.org/I4210152380"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Zhijian He","raw_affiliation_strings":["Shenzhen Technology University,College of Big Data and Internet,Shenzhen,China"],"affiliations":[{"raw_affiliation_string":"Shenzhen Technology University,College of Big Data and Internet,Shenzhen,China","institution_ids":["https://openalex.org/I4210152380"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5071443236","display_name":"Xiaoyu Tang","orcid":"https://orcid.org/0000-0002-6038-9623"},"institutions":[{"id":"https://openalex.org/I187400657","display_name":"South China Normal University","ror":"https://ror.org/01kq0pv72","country_code":"CN","type":"education","lineage":["https://openalex.org/I187400657"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoyu Tang","raw_affiliation_strings":["South China Normal University,School of Electronic and Information Engineering,Foshan,China,528225"],"affiliations":[{"raw_affiliation_string":"South China Normal University,School of Electronic and Information Engineering,Foshan,China,528225","institution_ids":["https://openalex.org/I187400657"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I187400657"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.36113387,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"10727","last_page":"10733"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.35600000619888306,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.35600000619888306,"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/T10191","display_name":"Robotics and Sensor-Based Localization","score":0.24729999899864197,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace 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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.23549999296665192,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive 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/segmentation","display_name":"Segmentation","score":0.6578999757766724},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5953999757766724},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.5788000226020813},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.5052000284194946},{"id":"https://openalex.org/keywords/path","display_name":"Path (computing)","score":0.49540001153945923},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.48339998722076416},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.47999998927116394},{"id":"https://openalex.org/keywords/false-positive-paradox","display_name":"False positive paradox","score":0.4350000023841858},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.413100004196167},{"id":"https://openalex.org/keywords/reduction","display_name":"Reduction (mathematics)","score":0.3837999999523163}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7612000107765198},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6880999803543091},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6578999757766724},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5953999757766724},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.5788000226020813},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5156999826431274},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.5052000284194946},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.49540001153945923},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.48339998722076416},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.47999998927116394},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4381999969482422},{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.4350000023841858},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.413100004196167},{"id":"https://openalex.org/C111335779","wikidata":"https://www.wikidata.org/wiki/Q3454686","display_name":"Reduction (mathematics)","level":2,"score":0.3837999999523163},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.35839998722076416},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.3531999886035919},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.34860000014305115},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.33629998564720154},{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.3319000005722046},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3151000142097473},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.3089999854564667},{"id":"https://openalex.org/C80519477","wikidata":"https://www.wikidata.org/wiki/Q3532236","display_name":"Scenario testing","level":3,"score":0.30079999566078186},{"id":"https://openalex.org/C169903167","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Test set","level":2,"score":0.29170000553131104},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.2849999964237213},{"id":"https://openalex.org/C155542232","wikidata":"https://www.wikidata.org/wiki/Q736111","display_name":"Optical flow","level":3,"score":0.2824000120162964},{"id":"https://openalex.org/C115901376","wikidata":"https://www.wikidata.org/wiki/Q184199","display_name":"Automation","level":2,"score":0.2816999852657318},{"id":"https://openalex.org/C82142266","wikidata":"https://www.wikidata.org/wiki/Q3456604","display_name":"Dynamic Bayesian network","level":3,"score":0.27889999747276306},{"id":"https://openalex.org/C81074085","wikidata":"https://www.wikidata.org/wiki/Q366872","display_name":"Motion planning","level":3,"score":0.2777999937534332},{"id":"https://openalex.org/C6683253","wikidata":"https://www.wikidata.org/wiki/Q7075535","display_name":"Obstacle avoidance","level":4,"score":0.2752000093460083},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2689000070095062},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.25999999046325684},{"id":"https://openalex.org/C112789634","wikidata":"https://www.wikidata.org/wiki/Q18207010","display_name":"False positives and false negatives","level":3,"score":0.2574999928474426},{"id":"https://openalex.org/C39920418","wikidata":"https://www.wikidata.org/wiki/Q11476","display_name":"Kinematics","level":2,"score":0.2554999887943268}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iros60139.2025.11246061","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros60139.2025.11246061","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W2031489346","https://openalex.org/W2955544543","https://openalex.org/W2991216808","https://openalex.org/W2997006708","https://openalex.org/W3004127093","https://openalex.org/W3161855852","https://openalex.org/W3177330511","https://openalex.org/W3202821542","https://openalex.org/W3207554615","https://openalex.org/W4213002936","https://openalex.org/W4221141829","https://openalex.org/W4226426325","https://openalex.org/W4285110186","https://openalex.org/W4285199148","https://openalex.org/W4312414163","https://openalex.org/W4312418574","https://openalex.org/W4312419284","https://openalex.org/W4386160130","https://openalex.org/W4387757567","https://openalex.org/W4389665673","https://openalex.org/W4390873058","https://openalex.org/W4390873369","https://openalex.org/W4401414645","https://openalex.org/W4402436699","https://openalex.org/W4402667890","https://openalex.org/W4403780498"],"related_works":[],"abstract_inverted_index":{"Motion":[0],"Object":[1],"Segmentation":[2],"(MOS)":[3],"is":[4,162],"crucial":[5],"for":[6,48],"autonomous":[7],"driving,":[8],"as":[9,68,75],"it":[10],"enhances":[11],"localization,":[12],"path":[13],"planning,":[14],"map":[15],"construction,":[16],"scene":[17],"flow":[18],"estimation,":[19],"and":[20,32,71,85,91,112,124,151],"future":[21],"state":[22],"prediction.":[23],"While":[24],"existing":[25],"methods":[26],"achieve":[27,125],"strong":[28],"performance,":[29],"balancing":[30],"accuracy":[31,53],"real-time":[33,56],"inference":[34],"remains":[35],"a":[36,43,61,72,126,137],"challenge.":[37],"To":[38,78],"address":[39],"this,":[40],"we":[41,59,88,116],"propose":[42],"logits-based":[44],"knowledge":[45],"distillation":[46,94],"framework":[47],"MOS,":[49],"aiming":[50],"to":[51,100],"improve":[52],"while":[54],"maintaining":[55],"efficiency.":[57],"Specifically,":[58],"adopt":[60],"Bird\u2019s":[62],"Eye":[63],"View":[64],"(BEV)":[65],"projection-based":[66],"model":[67,74,99],"the":[69,76,80,97,121,143,148,156],"student":[70],"non-projection":[73],"teacher.":[77],"handle":[79],"severe":[81],"imbalance":[82],"between":[83],"moving":[84],"non-moving":[86],"classes,":[87],"decouple":[89],"them":[90],"apply":[92],"tailored":[93],"strategies,":[95],"allowing":[96],"teacher":[98],"better":[101],"learn":[102],"key":[103],"motion-related":[104],"features.":[105],"This":[106],"approach":[107],"significantly":[108],"reduces":[109],"false":[110,113],"positives":[111],"negatives.":[114],"Additionally,":[115],"introduce":[117],"dynamic":[118],"upsampling,":[119],"optimize":[120],"network":[122],"architecture,":[123],"7.69%":[127],"reduction":[128],"in":[129],"parameter":[130],"count,":[131],"mitigating":[132],"overfitting.":[133],"Our":[134],"method":[135],"achieves":[136],"notable":[138],"IoU":[139],"of":[140,147],"78.8%":[141],"on":[142,155],"hidden":[144],"test":[145],"set":[146],"SemanticKITTI-MOS":[149],"dataset":[150],"delivers":[152],"competitive":[153],"results":[154],"Apollo":[157],"dataset.":[158],"The":[159],"KDMOS":[160],"implementation":[161],"available":[163],"at":[164],"https://github.com/SCNU-RISLAB/KDMOS.":[165]},"counts_by_year":[],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-11-28T00:00:00"}
