{"id":"https://openalex.org/W7164941134","doi":"https://doi.org/10.48550/arxiv.2606.15663","title":"OneFocus: Enabling Real-World X-ray Security Screening with a Unified Vision-Language Model","display_name":"OneFocus: Enabling Real-World X-ray Security Screening with a Unified Vision-Language Model","publication_year":2026,"publication_date":"2026-06-14","ids":{"openalex":"https://openalex.org/W7164941134","doi":"https://doi.org/10.48550/arxiv.2606.15663"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.15663","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.15663","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":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.15663","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5138720731","display_name":"Jiali Wen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wen, Jiali","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076692827","display_name":"He Gao","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Gao, Hongxia","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138718214","display_name":"Litao Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Litao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138744218","display_name":"Yixin Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Yixin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5138707030","display_name":"Kaijie Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Kaijie","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133139201","display_name":"Qianyun Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Qianyun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5138720107","display_name":"Xiaoqin Wen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wen, Xiaoqin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"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":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":0.6035000085830688,"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":0.6035000085830688,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.09489999711513519,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.06210000067949295,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.5238000154495239},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5120999813079834},{"id":"https://openalex.org/keywords/bridge","display_name":"Bridge (graph theory)","score":0.47679999470710754},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.47290000319480896},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.40799999237060547},{"id":"https://openalex.org/keywords/border-security","display_name":"Border Security","score":0.40389999747276306},{"id":"https://openalex.org/keywords/core","display_name":"Core (optical fiber)","score":0.3894999921321869},{"id":"https://openalex.org/keywords/critical-infrastructure","display_name":"Critical infrastructure","score":0.3653999865055084}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7429999709129333},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.5238000154495239},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5120999813079834},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.47679999470710754},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.47290000319480896},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45809999108314514},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.40799999237060547},{"id":"https://openalex.org/C2780934415","wikidata":"https://www.wikidata.org/wiki/Q20997131","display_name":"Border Security","level":2,"score":0.40389999747276306},{"id":"https://openalex.org/C2164484","wikidata":"https://www.wikidata.org/wiki/Q5170150","display_name":"Core (optical fiber)","level":2,"score":0.3894999921321869},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.3822000026702881},{"id":"https://openalex.org/C29852176","wikidata":"https://www.wikidata.org/wiki/Q373338","display_name":"Critical infrastructure","level":2,"score":0.3653999865055084},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.30230000615119934},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.295199990272522},{"id":"https://openalex.org/C140547941","wikidata":"https://www.wikidata.org/wiki/Q7797194","display_name":"Threat model","level":2,"score":0.2906999886035919},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.287200003862381},{"id":"https://openalex.org/C32833848","wikidata":"https://www.wikidata.org/wiki/Q4115054","display_name":"Extensibility","level":2,"score":0.2818000018596649},{"id":"https://openalex.org/C77109596","wikidata":"https://www.wikidata.org/wiki/Q4781497","display_name":"Application security","level":5,"score":0.26660001277923584},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.26019999384880066},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.25920000672340393},{"id":"https://openalex.org/C95713431","wikidata":"https://www.wikidata.org/wiki/Q631425","display_name":"Vulnerability (computing)","level":2,"score":0.2574000060558319},{"id":"https://openalex.org/C121822524","wikidata":"https://www.wikidata.org/wiki/Q5157582","display_name":"Computer security model","level":2,"score":0.25200000405311584},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.25110000371932983}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.15663","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.15663","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.15663","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.15663","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":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"X-ray":[0,41,65,148],"contraband":[1,20,85,137,149],"detection":[2],"is":[3],"critical":[4,47],"for":[5,116,161],"security":[6,162],"in":[7,147],"large-scale":[8],"logistics":[9],"and":[10,22,90,140,151],"transportation,":[11],"yet":[12],"conventional":[13],"detectors":[14],"struggle":[15],"to":[16,18,105],"adapt":[17],"emerging":[19],"types":[21],"lack":[23],"fundamental":[24],"visual":[25,134],"understanding.":[26,142],"Vision-language":[27],"models":[28],"(VLMs)":[29],"offer":[30],"strong":[31,158],"generalization":[32],"but":[33],"are":[34],"hindered":[35],"by":[36],"the":[37],"scarcity":[38],"of":[39,56,64],"high-quality":[40],"image-caption":[42,58],"data.":[43],"To":[44,67],"bridge":[45],"this":[46],"gap,":[48],"we":[49,74,123],"present":[50],"MMXray,":[51,122],"a":[52,78,100,126,157],"meticulously":[53],"curated":[54],"benchmark":[55],"52,124":[57],"pairs":[59],"spanning":[60],"28":[61,88],"fine-grained":[62],"classes":[63],"contraband.":[66],"enrich":[68],"MMXray":[69],"with":[70,93,98],"realistic":[71],"occlusion":[72],"patterns,":[73],"further":[75],"introduce":[76],"CleanDET,":[77],"dedicated":[79],"synthesis":[80,102],"dataset":[81],"containing":[82],"clean":[83],"foreground":[84],"images":[86,92],"from":[87],"categories":[89],"background":[91],"diverse":[94],"density":[95],"levels,":[96],"together":[97],"AnyContraSyn,":[99],"controllable":[101],"method":[103],"designed":[104],"operate":[106],"on":[107,121],"CleanDET.":[108],"We":[109],"also":[110],"develop":[111],"OnePipe,":[112],"an":[113],"extensible":[114],"pipeline":[115],"systematic":[117],"data":[118],"curation.":[119],"Built":[120],"propose":[124],"OneFocus,":[125],"unified":[127],"VLM":[128],"that":[129],"supports":[130],"four":[131],"core":[132],"tasks:":[133],"question":[135],"answering,":[136],"localization,":[138],"classification,":[139],"image":[141],"OneFocus":[143],"achieves":[144],"state-of-the-art":[145],"performance":[146],"understanding":[150],"demonstrates":[152],"robust":[153],"cross-domain":[154],"generalization,":[155],"establishing":[156],"vision-language":[159],"baseline":[160],"screening.":[163]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-17T00:00:00"}