{"id":"https://openalex.org/W4283213570","doi":"https://doi.org/10.48550/arxiv.2206.08842","title":"Entity-Graph Enhanced Cross-Modal Pretraining for Instance-level Product Retrieval","display_name":"Entity-Graph Enhanced Cross-Modal Pretraining for Instance-level Product Retrieval","publication_year":2022,"publication_date":"2022-06-17","ids":{"openalex":"https://openalex.org/W4283213570","doi":"https://doi.org/10.48550/arxiv.2206.08842"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2206.08842","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2206.08842","pdf_url":"https://arxiv.org/pdf/2206.08842","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":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2206.08842","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5101415459","display_name":"Xiao Dong","orcid":"https://orcid.org/0000-0001-9508-6246"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Dong, Xiao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5049388503","display_name":"Xunlin Zhan","orcid":"https://orcid.org/0000-0001-5053-7349"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhan, Xunlin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087043856","display_name":"Yunchao Wei","orcid":"https://orcid.org/0000-0002-2812-8781"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wei, Yunchao","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034340826","display_name":"Xiaoyong Wei","orcid":"https://orcid.org/0000-0001-9677-4227"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wei, Xiaoyong","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100631216","display_name":"Yaowei Wang","orcid":"https://orcid.org/0000-0003-2197-9038"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yaowei","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037966954","display_name":"Minlong Lu","orcid":"https://orcid.org/0000-0002-9851-6480"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu, Minlong","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068837264","display_name":"Xiaochun Cao","orcid":"https://orcid.org/0000-0001-7141-708X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cao, Xiaochun","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5047878798","display_name":"Xiaodan Liang","orcid":"https://orcid.org/0000-0003-3213-3062"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liang, Xiaodan","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5101415459"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9934999942779541,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9934999942779541,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9916999936103821,"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/T12016","display_name":"Web Data Mining and Analysis","score":0.9627000093460083,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/computer-science","display_name":"Computer science","score":0.8029360771179199},{"id":"https://openalex.org/keywords/modal","display_name":"Modal","score":0.6435719728469849},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5831055045127869},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5082513689994812},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.45768940448760986},{"id":"https://openalex.org/keywords/generalizability-theory","display_name":"Generalizability theory","score":0.4509515166282654},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4097726345062256},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.4026898145675659},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.40073150396347046},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3576464056968689},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.2726457715034485}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8029360771179199},{"id":"https://openalex.org/C71139939","wikidata":"https://www.wikidata.org/wiki/Q910194","display_name":"Modal","level":2,"score":0.6435719728469849},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5831055045127869},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5082513689994812},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45768940448760986},{"id":"https://openalex.org/C27158222","wikidata":"https://www.wikidata.org/wiki/Q5532422","display_name":"Generalizability theory","level":2,"score":0.4509515166282654},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4097726345062256},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4026898145675659},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.40073150396347046},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3576464056968689},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.2726457715034485},{"id":"https://openalex.org/C188027245","wikidata":"https://www.wikidata.org/wiki/Q750446","display_name":"Polymer chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2206.08842","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2206.08842","pdf_url":"https://arxiv.org/pdf/2206.08842","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":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2206.08842","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2206.08842","pdf_url":null,"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":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2206.08842","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2206.08842","pdf_url":"https://arxiv.org/pdf/2206.08842","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":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4283213570.pdf"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2118717649","https://openalex.org/W2413243053","https://openalex.org/W410723623","https://openalex.org/W2015341305","https://openalex.org/W17155033","https://openalex.org/W2035068594","https://openalex.org/W4225593417","https://openalex.org/W2573498121","https://openalex.org/W3022298670","https://openalex.org/W3160494304"],"abstract_inverted_index":{"Our":[0],"goal":[1],"in":[2,12,63,143],"this":[3],"research":[4],"is":[5,75,92,132],"to":[6,40,56,83,172],"study":[7],"a":[8,85,124,154],"more":[9,86],"realistic":[10],"environment":[11],"which":[13,91,158],"we":[14,81],"can":[15],"conduct":[16],"weakly-supervised":[17],"multi-modal":[18,102,151],"instance-level":[19,37,53,135],"product":[20,24,60],"retrieval":[21,38],"for":[22,134],"fine-grained":[23],"categories.":[25],"We":[26],"first":[27],"contribute":[28],"the":[29,42,45,59,64,70,101,115,118,150,161,170,183],"Product1M":[30],"datasets,":[31],"and":[32,48,67,111,117,146,185,198],"define":[33],"two":[34],"real":[35,177],"practical":[36],"tasks":[39],"enable":[41],"evaluations":[43],"on":[44,174],"price":[46],"comparison":[47],"personalized":[49],"recommendations.":[50],"For":[51],"both":[52,144],"tasks,":[54],"how":[55],"accurately":[57],"pinpoint":[58],"target":[61],"mentioned":[62],"visual-linguistic":[65],"data":[66],"effectively":[68,168],"decrease":[69],"influence":[71],"of":[72,95,187],"irrelevant":[73],"contents":[74],"quite":[76],"challenging.":[77],"To":[78],"address":[79],"this,":[80],"exploit":[82],"train":[84],"effective":[87],"cross-modal":[88,193],"pertaining":[89],"model":[90,131],"adaptively":[93],"capable":[94],"incorporating":[96],"key":[97],"concept":[98],"information":[99],"from":[100],"data,":[103],"by":[104],"using":[105],"an":[106],"entity":[107,116,141],"graph":[108],"whose":[109],"node":[110],"edge":[112],"respectively":[113],"denote":[114],"similarity":[119],"relation":[120],"between":[121,163],"entities.":[122],"Specifically,":[123],"novel":[125],"Entity-Graph":[126],"Enhanced":[127],"Cross-Modal":[128],"Pretraining":[129],"(EGE-CMP)":[130],"proposed":[133],"commodity":[136],"retrieval,":[137],"that":[138],"explicitly":[139],"injects":[140],"knowledge":[142],"node-based":[145],"subgraph-based":[147],"ways":[148],"into":[149],"networks":[152],"via":[153],"self-supervised":[155],"hybrid-stream":[156],"transformer,":[157],"could":[159],"reduce":[160],"confusion":[162],"different":[164],"object":[165],"contents,":[166],"thereby":[167],"guiding":[169],"network":[171],"focus":[173],"entities":[175],"with":[176],"semantic.":[178],"Experimental":[179],"results":[180],"well":[181],"verify":[182],"efficacy":[184],"generalizability":[186],"our":[188],"EGE-CMP,":[189],"outperforming":[190],"several":[191],"SOTA":[192],"baselines":[194],"like":[195],"CLIP,":[196],"UNITER":[197],"CAPTURE.":[199]},"counts_by_year":[],"updated_date":"2026-03-20T23:20:44.827607","created_date":"2025-10-10T00:00:00"}