{"id":"https://openalex.org/W7162424725","doi":"https://doi.org/10.48550/arxiv.2605.24503","title":"FoodMonitor: Benchmarking MLLMs for Explainable Compliance Analysis","display_name":"FoodMonitor: Benchmarking MLLMs for Explainable Compliance Analysis","publication_year":2026,"publication_date":"2026-05-23","ids":{"openalex":"https://openalex.org/W7162424725","doi":"https://doi.org/10.48550/arxiv.2605.24503"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.24503","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.24503","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.2605.24503","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137055901","display_name":"Ruihao Xu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xu, Ruihao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137075781","display_name":"Xingming Shui","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shui, Xingming","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137021023","display_name":"Jingxuan Niu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Niu, Jingxuan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137037761","display_name":"Yiqin Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yiqin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137053607","display_name":"Jilin Yu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yu, Jilin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5019012787","display_name":"Haoji Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Haoji","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137077424","display_name":"Yansong Tang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tang, Yansong","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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.6654999852180481,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.6654999852180481,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.14329999685287476,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.03150000050663948,"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/benchmarking","display_name":"Benchmarking","score":0.7404999732971191},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.5045999884605408},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.4945000112056732},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4657000005245209},{"id":"https://openalex.org/keywords/accountability","display_name":"Accountability","score":0.4611000120639801},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.4569999873638153},{"id":"https://openalex.org/keywords/protocol","display_name":"Protocol (science)","score":0.4366999864578247},{"id":"https://openalex.org/keywords/compliance","display_name":"Compliance (psychology)","score":0.4284999966621399},{"id":"https://openalex.org/keywords/corporate-governance","display_name":"Corporate governance","score":0.420199990272522}],"concepts":[{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.7404999732971191},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6883999705314636},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.5045999884605408},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.4945000112056732},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4657000005245209},{"id":"https://openalex.org/C2776007630","wikidata":"https://www.wikidata.org/wiki/Q2798912","display_name":"Accountability","level":2,"score":0.4611000120639801},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.4569999873638153},{"id":"https://openalex.org/C2780385302","wikidata":"https://www.wikidata.org/wiki/Q367158","display_name":"Protocol (science)","level":3,"score":0.4366999864578247},{"id":"https://openalex.org/C2781460075","wikidata":"https://www.wikidata.org/wiki/Q1399332","display_name":"Compliance (psychology)","level":2,"score":0.4284999966621399},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.4221000075340271},{"id":"https://openalex.org/C39389867","wikidata":"https://www.wikidata.org/wiki/Q380767","display_name":"Corporate governance","level":2,"score":0.420199990272522},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.39640000462532043},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3952000141143799},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.392300009727478},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.38339999318122864},{"id":"https://openalex.org/C5911748","wikidata":"https://www.wikidata.org/wiki/Q4072285","display_name":"Conformity assessment","level":2,"score":0.3804999887943268},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.37549999356269836},{"id":"https://openalex.org/C85847156","wikidata":"https://www.wikidata.org/wiki/Q59015987","display_name":"Verifiable secret sharing","level":3,"score":0.3684000074863434},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3465999960899353},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.3382999897003174},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.3199000060558319},{"id":"https://openalex.org/C2776459999","wikidata":"https://www.wikidata.org/wiki/Q2119376","display_name":"Fidelity","level":2,"score":0.3025999963283539},{"id":"https://openalex.org/C2778739407","wikidata":"https://www.wikidata.org/wiki/Q165372","display_name":"CLIPS","level":2,"score":0.2969000041484833},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.2870999872684479},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.2809999883174896},{"id":"https://openalex.org/C33676613","wikidata":"https://www.wikidata.org/wiki/Q13415176","display_name":"Dimension (graph theory)","level":2,"score":0.2782999873161316},{"id":"https://openalex.org/C195094911","wikidata":"https://www.wikidata.org/wiki/Q14167904","display_name":"Process management","level":1,"score":0.27810001373291016},{"id":"https://openalex.org/C58166","wikidata":"https://www.wikidata.org/wiki/Q224821","display_name":"Fuzzy logic","level":2,"score":0.2720000147819519},{"id":"https://openalex.org/C107284991","wikidata":"https://www.wikidata.org/wiki/Q79757","display_name":"Barometer","level":2,"score":0.26899999380111694},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.2635999917984009},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2606000006198883},{"id":"https://openalex.org/C64543145","wikidata":"https://www.wikidata.org/wiki/Q162942","display_name":"Intersection (aeronautics)","level":2,"score":0.2565999925136566},{"id":"https://openalex.org/C184356942","wikidata":"https://www.wikidata.org/wiki/Q830382","display_name":"Best practice","level":2,"score":0.2513999938964844}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.24503","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.24503","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.2605.24503","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.24503","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":{"As":[0],"AI-powered":[1],"compliance":[2,44,53],"monitoring":[3],"becomes":[4],"increasingly":[5],"important":[6],"in":[7,55],"public":[8],"governance":[9],"and":[10,19,75,89,113,125,150,168],"industrial":[11],"safety,":[12],"the":[13,37,140,156],"ability":[14],"to":[15],"provide":[16],"verifiable":[17],"evidence":[18],"traceable":[20],"accountability":[21],"signals":[22],"is":[23],"essential.":[24],"However,":[25],"existing":[26],"video":[27,62],"anomaly":[28],"detection":[29,127],"datasets":[30],"focus":[31],"on":[32],"event-level":[33],"binary":[34],"classification,":[35],"lacking":[36],"rule-driven,":[38],"explainable":[39,52],"analysis":[40,54,160],"required":[41],"for":[42,51,174],"real-world":[43],"scenarios.":[45],"We":[46,97],"introduce":[47],"FoodMonitor,":[48],"a":[49,69,99,104,118],"benchmark":[50],"commercial":[56],"kitchen":[57],"surveillance.":[58],"FoodMonitor":[59],"comprises":[60],"477":[61],"clips":[63],"with":[64,93,103,117,147],"3,307":[65],"violation":[66],"annotations":[67],"across":[68],"dual-channel":[70],"design":[71],"covering":[72],"both":[73],"person-level":[74],"environment-level":[76],"violations.":[77],"Each":[78],"annotation":[79],"specifies":[80],"which":[81],"rule":[82,152],"was":[83],"violated,":[84],"what":[85],"non-compliant":[86],"behavior":[87],"occurred,":[88],"who":[90],"committed":[91],"it":[92],"frame-level":[94],"bounding":[95],"boxes.":[96],"establish":[98],"unified":[100],"evaluation":[101,130],"protocol":[102],"two-stage":[105],"matching":[106],"mechanism":[107],"that":[108,122,139],"separately":[109],"assesses":[110],"spatial":[111,148],"localization":[112,149],"semantic":[114],"understanding,":[115],"along":[116],"composite":[119],"metric":[120],"($C_{\\text{score}}$)":[121],"balances":[123],"environment":[124],"person":[126],"performance.":[128],"Systematic":[129],"of":[131],"several":[132],"state-of-the-art":[133],"multimodal":[134],"large":[135],"language":[136],"models":[137],"reveals":[138],"best-performing":[141],"model":[142,176],"achieves":[143],"only":[144],"0.360":[145],"$C_{\\text{score}}$,":[146],"fine-grained":[151],"understanding":[153],"emerging":[154],"as":[155],"primary":[157],"bottlenecks.":[158],"Our":[159],"identifies":[161],"two":[162],"distinct":[163],"failure":[164],"modes:":[165],"localization-dominated":[166],"errors":[167],"semantics-dominated":[169],"errors,":[170],"providing":[171],"diagnostic":[172],"insights":[173],"future":[175],"development.":[177]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-27T00:00:00"}