{"id":"https://openalex.org/W4405029438","doi":"https://doi.org/10.48550/arxiv.2411.18428","title":"MM-Path: Multi-modal, Multi-granularity Path Representation Learning --\n Extended Version","display_name":"MM-Path: Multi-modal, Multi-granularity Path Representation Learning --\n Extended Version","publication_year":2024,"publication_date":"2024-11-27","ids":{"openalex":"https://openalex.org/W4405029438","doi":"https://doi.org/10.48550/arxiv.2411.18428"},"language":"en","primary_location":{"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2411.18428","pdf_url":"http://arxiv.org/pdf/2411.18428","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":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false},"type":"preprint","type_crossref":"posted-content","indexed_in":["arxiv"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"http://arxiv.org/pdf/2411.18428","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5091189663","display_name":"Ronghui Xu","orcid":"https://orcid.org/0000-0002-2822-0561"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Ronghui","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101355211","display_name":"Hanyin Cheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cheng, Hanyin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5084021933","display_name":"Chenjuan Guo","orcid":"https://orcid.org/0000-0002-4516-4637"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Guo, Chenjuan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048336298","display_name":"Hongfan Gao","orcid":"https://orcid.org/0000-0002-4522-8389"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gao, Hongfan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5108217213","display_name":"Jilin Hu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hu, Jilin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054708051","display_name":"Bin Yang","orcid":"https://orcid.org/0000-0001-7819-2290"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Sean Bin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5009308205","display_name":"Bin Yang","orcid":"https://orcid.org/0000-0001-5894-9233"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Bin","raw_affiliation_strings":[],"affiliations":[]}],"institution_assertions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":0,"max":84},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9934,"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"}},"topics":[{"id":"https://openalex.org/T10181","display_name":"Natural Language Processing Techniques","score":0.9934,"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/T12031","display_name":"Speech and dialogue systems","score":0.976,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9643,"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/granularity","display_name":"Granularity","score":0.759227},{"id":"https://openalex.org/keywords/representation","display_name":"Representation","score":0.6524769}],"concepts":[{"id":"https://openalex.org/C71139939","wikidata":"https://www.wikidata.org/wiki/Q910194","display_name":"Modal","level":2,"score":0.7608782},{"id":"https://openalex.org/C177774035","wikidata":"https://www.wikidata.org/wiki/Q1246948","display_name":"Granularity","level":2,"score":0.759227},{"id":"https://openalex.org/C2777735758","wikidata":"https://www.wikidata.org/wiki/Q817765","display_name":"Path (computing)","level":2,"score":0.75745726},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.6524769},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5328399},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34554726},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3423555},{"id":"https://openalex.org/C192562407","wikidata":"https://www.wikidata.org/wiki/Q228736","display_name":"Materials science","level":0,"score":0.21818423},{"id":"https://openalex.org/C159985019","wikidata":"https://www.wikidata.org/wiki/Q181790","display_name":"Composite material","level":1,"score":0.0519284},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.04712978},{"id":"https://openalex.org/C31258907","wikidata":"https://www.wikidata.org/wiki/Q1301371","display_name":"Computer network","level":1,"score":0.044412494},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.041030288},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2411.18428","pdf_url":"http://arxiv.org/pdf/2411.18428","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":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false}],"best_oa_location":{"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2411.18428","pdf_url":"http://arxiv.org/pdf/2411.18428","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":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false},"sustainable_development_goals":[],"grants":[],"datasets":[],"versions":[],"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W936373746","https://openalex.org/W4382701072","https://openalex.org/W4256502920","https://openalex.org/W4226090359","https://openalex.org/W2975817033","https://openalex.org/W2931688134","https://openalex.org/W2378857091","https://openalex.org/W2377919138","https://openalex.org/W2059697060","https://openalex.org/W103652678"],"abstract_inverted_index":{"Developing":[0],"effective":[1,103],"path":[2,16,126],"representations":[3],"has":[4],"become":[5],"increasingly":[6],"essential":[7],"across":[8,198],"various":[9],"fields":[10],"within":[11],"intelligent":[12],"transportation.":[13],"Although":[14],"pre-trained":[15],"representation":[17,69,127],"learning":[18],"models":[19],"have":[20],"shown":[21],"improved":[22],"performance,":[23],"they":[24],"predominantly":[25],"focus":[26],"on":[27,208],"the":[28,39,79,94,140,166,179,218,221],"topological":[29],"structures":[30],"from":[31,58,131],"single":[32],"modality":[33],"data,":[34,144],"i.e.,":[35],"road":[36,83,133,155,158],"networks,":[37],"overlooking":[38],"geometric":[40],"and":[41,71,88,105,135,157,173,201],"contextual":[42],"features":[43],"associated":[44],"with":[45,160],"path-related":[46],"images,":[47],"e.g.,":[48],"remote":[49],"sensing":[50],"images.":[51],"Similar":[52],"to":[53,194],"human":[54],"understanding,":[55],"integrating":[56,129],"information":[57,76,172,197],"multiple":[59],"modalities":[60,130,200],"can":[61,122],"provide":[62],"a":[63,112,124,147,187],"more":[64],"comprehensive":[65],"view,":[66],"enhancing":[67],"both":[68,132,169],"accuracy":[70],"generalization.":[72],"However,":[73],"variations":[74],"in":[75],"granularity":[77],"impede":[78],"semantic":[80],"alignment":[81,141,149],"of":[82,96,142,168,181,220],"network-based":[84],"paths":[85,90,134,159],"(road":[86],"paths)":[87],"image-based":[89],"(image":[91],"paths),":[92],"while":[93],"heterogeneity":[95,180],"multi-modal":[97,143,182],"data":[98,183],"poses":[99],"substantial":[100],"challenges":[101],"for":[102],"fusion":[104,191],"utilization.":[106],"In":[107],"this":[108],"paper,":[109],"we":[110,145,185,204],"propose":[111],"novel":[113],"Multi-modal,":[114],"Multi-granularity":[115],"Path":[116],"Representation":[117],"Learning":[118],"Framework":[119],"(MM-Path),":[120],"which":[121],"learn":[123],"generic":[125],"by":[128],"image":[136,163],"paths.":[137],"To":[138,177],"enhance":[139],"develop":[146],"multi-granularity":[148],"strategy":[150],"that":[151],"systematically":[152],"associates":[153],"nodes,":[154],"sub-paths,":[156],"their":[161],"corresponding":[162],"patches,":[164],"ensuring":[165],"synchronization":[167],"detailed":[170],"local":[171],"broader":[174],"global":[175],"contexts.":[176],"address":[178],"effectively,":[184],"introduce":[186],"graph-based":[188],"cross-modal":[189],"residual":[190],"component":[192],"designed":[193],"comprehensively":[195],"fuse":[196],"different":[199],"granularities.":[202],"Finally,":[203],"conduct":[205],"extensive":[206],"experiments":[207],"two":[209,214],"large-scale":[210],"real-world":[211],"datasets":[212],"under":[213],"downstream":[215],"tasks,":[216],"validating":[217],"effectiveness":[219],"proposed":[222],"MM-Path.":[223],"The":[224],"code":[225],"is":[226],"available":[227],"at:":[228],"https://github.com/decisionintelligence/MM-Path.":[229]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4405029438","counts_by_year":[],"updated_date":"2024-12-16T02:42:38.746571","created_date":"2024-12-05"}