{"id":"https://openalex.org/W4376122650","doi":"https://doi.org/10.48550/arxiv.2305.05095","title":"Less is More: Removing Text-regions Improves CLIP Training Efficiency and Robustness","display_name":"Less is More: Removing Text-regions Improves CLIP Training Efficiency and Robustness","publication_year":2023,"publication_date":"2023-05-08","ids":{"openalex":"https://openalex.org/W4376122650","doi":"https://doi.org/10.48550/arxiv.2305.05095"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2305.05095","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2305.05095","pdf_url":"https://arxiv.org/pdf/2305.05095","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2305.05095","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5103187717","display_name":"Liangliang Cao","orcid":"https://orcid.org/0000-0003-0900-1512"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Cao, Liangliang","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111279781","display_name":"Bowen Zhang","orcid":"https://orcid.org/0000-0001-6045-8599"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Bowen","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100418563","display_name":"Chen Chen","orcid":"https://orcid.org/0000-0003-3498-2527"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Chen","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112656212","display_name":"Yinfei Yang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yang, Yinfei","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017048711","display_name":"Xianzhi Du","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Du, Xianzhi","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104245976","display_name":"Wencong Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Wencong","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5039693533","display_name":"Zhiyun Lu","orcid":"https://orcid.org/0000-0002-1733-4061"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lu, Zhiyun","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5101201800","display_name":"Yantao Zheng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zheng, Yantao","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5103187717"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":2,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9666000008583069,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9666000008583069,"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/T12357","display_name":"Digital Media Forensic Detection","score":0.9373999834060669,"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/robustness","display_name":"Robustness (evolution)","score":0.8698983788490295},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8249000310897827},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6063377261161804},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4903876781463623},{"id":"https://openalex.org/keywords/de-facto","display_name":"De facto","score":0.4873434007167816},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.44549089670181274},{"id":"https://openalex.org/keywords/language-model","display_name":"Language model","score":0.44128358364105225},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.4223838746547699},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4164939224720001},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3532116115093231},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.3508697748184204},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.28223270177841187}],"concepts":[{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.8698983788490295},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8249000310897827},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6063377261161804},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4903876781463623},{"id":"https://openalex.org/C2992317946","wikidata":"https://www.wikidata.org/wiki/Q712144","display_name":"De facto","level":2,"score":0.4873434007167816},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.44549089670181274},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.44128358364105225},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.4223838746547699},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4164939224720001},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3532116115093231},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3508697748184204},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.28223270177841187},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C104317684","wikidata":"https://www.wikidata.org/wiki/Q7187","display_name":"Gene","level":2,"score":0.0},{"id":"https://openalex.org/C55493867","wikidata":"https://www.wikidata.org/wiki/Q7094","display_name":"Biochemistry","level":1,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"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":2,"locations":[{"id":"pmh:oai:arXiv.org:2305.05095","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2305.05095","pdf_url":"https://arxiv.org/pdf/2305.05095","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550/arxiv.2305.05095","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2305.05095","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2305.05095","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2305.05095","pdf_url":"https://arxiv.org/pdf/2305.05095","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[{"display_name":"Quality Education","score":0.5,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":false},"content_urls":{"pdf":"https://content.openalex.org/works/W4376122650.pdf"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2807906686","https://openalex.org/W2794909825","https://openalex.org/W4247715995","https://openalex.org/W2594962586","https://openalex.org/W2518047880","https://openalex.org/W3175075103","https://openalex.org/W2371349926","https://openalex.org/W4287644835","https://openalex.org/W3092281475","https://openalex.org/W3098003361"],"abstract_inverted_index":{"The":[0],"CLIP":[1,21,37,86,161],"(Contrastive":[2],"Language-Image":[3],"Pre-training)":[4],"model":[5,22,38,140,162],"and":[6,45,83,101,121,129],"its":[7],"variants":[8],"are":[9],"becoming":[10],"the":[11,35,42,59,62,67,81,90,95,111,119,139],"de":[12],"facto":[13],"backbone":[14],"in":[15,51,61,110],"many":[16],"applications.":[17],"However,":[18],"training":[19,91],"a":[20,149,164],"from":[23,141],"hundreds":[24],"of":[25,27,47,85,98,167],"millions":[26],"image-text":[28],"pairs":[29],"can":[30,54],"be":[31],"prohibitively":[32],"expensive.":[33],"Furthermore,":[34],"conventional":[36],"doesn't":[39,65],"differentiate":[40],"between":[41],"visual":[43,69],"semantics":[44],"meaning":[46],"text":[48,60,108,135],"regions":[49,109,136],"embedded":[50,63],"images.":[52],"This":[53],"lead":[55],"to":[56,79],"non-robustness":[57],"when":[58],"region":[64],"match":[66],"image's":[68],"appearance.":[70],"In":[71],"this":[72],"paper,":[73],"we":[74,116,147],"discuss":[75],"two":[76],"effective":[77],"approaches":[78],"improve":[80,118],"efficiency":[82],"robustness":[84],"training:":[87],"(1)":[88],"augmenting":[89],"dataset":[92,151],"while":[93],"maintaining":[94],"same":[96],"number":[97],"optimization":[99],"steps,":[100],"(2)":[102],"filtering":[103],"out":[104,132],"samples":[105],"that":[106],"contain":[107],"image.":[112],"By":[113],"doing":[114],"so,":[115],"significantly":[117],"classification":[120],"retrieval":[122],"accuracy":[123,166,173],"on":[124],"public":[125],"benchmarks":[126],"like":[127],"ImageNet":[128,153],"CoCo.":[130],"Filtering":[131],"images":[133],"with":[134,154],"also":[137],"protects":[138],"typographic":[142],"attacks.":[143],"To":[144],"verify":[145],"this,":[146],"build":[148],"new":[150],"named":[152],"Adversarial":[155],"Text":[156],"Regions":[157],"(ImageNet-Attr).":[158],"Our":[159],"filter-based":[160],"demonstrates":[163],"top-1":[165],"68.78\\%,":[168],"outperforming":[169],"previous":[170],"models":[171],"whose":[172],"was":[174],"all":[175],"below":[176],"50\\%.":[177]},"counts_by_year":[{"year":2024,"cited_by_count":2}],"updated_date":"2026-03-10T16:38:18.471706","created_date":"2023-05-12T00:00:00"}
