{"id":"https://openalex.org/W7166648545","doi":"https://doi.org/10.48550/arxiv.2606.30393","title":"SADL: What to Ignore? A Benchmark for Subject-Aware Distractor Localization","display_name":"SADL: What to Ignore? A Benchmark for Subject-Aware Distractor Localization","publication_year":2026,"publication_date":"2026-06-29","ids":{"openalex":"https://openalex.org/W7166648545","doi":"https://doi.org/10.48550/arxiv.2606.30393"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.30393","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.30393","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":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.2606.30393","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139675805","display_name":"Cao-Tri Nguyen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nguyen, Cao-Tri","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5111529622","display_name":"N. X. Luong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Luong, Nguyen-Khoa","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5054588832","display_name":"Vinh-Tiep Nguyen","orcid":"https://orcid.org/0000-0003-4260-7874"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Nguyen, Vinh-Tiep","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139663240","display_name":"Minh-Triet Tran","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tran, Minh-Triet","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":0.5199000239372253,"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":0.5199000239372253,"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/T10803","display_name":"Innovative Human-Technology Interaction","score":0.06840000301599503,"subfield":{"id":"https://openalex.org/subfields/1709","display_name":"Human-Computer Interaction"},"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/T12607","display_name":"Personal Information Management and User Behavior","score":0.04270000010728836,"subfield":{"id":"https://openalex.org/subfields/1802","display_name":"Information Systems and Management"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6909999847412109},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.6276000142097473},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.6051999926567078},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.5735999941825867},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4564000070095062},{"id":"https://openalex.org/keywords/coherence","display_name":"Coherence (philosophical gambling strategy)","score":0.42649999260902405},{"id":"https://openalex.org/keywords/task-analysis","display_name":"Task analysis","score":0.4036000072956085},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.3824000060558319}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.775600016117096},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6909999847412109},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6430000066757202},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.6276000142097473},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.6051999926567078},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.5735999941825867},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4564000070095062},{"id":"https://openalex.org/C2781181686","wikidata":"https://www.wikidata.org/wiki/Q4226068","display_name":"Coherence (philosophical gambling strategy)","level":2,"score":0.42649999260902405},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.4036000072956085},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38519999384880066},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.3824000060558319},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.36390000581741333},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.35339999198913574},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.3479999899864197},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.31520000100135803},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.2973000109195709},{"id":"https://openalex.org/C124304363","wikidata":"https://www.wikidata.org/wiki/Q673661","display_name":"Abstraction","level":2,"score":0.2957000136375427},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.2854999899864197},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.2784000039100647},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.2669999897480011},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.26420000195503235},{"id":"https://openalex.org/C2777855551","wikidata":"https://www.wikidata.org/wiki/Q12310021","display_name":"Subject (documents)","level":2,"score":0.25119999051094055}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.30393","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.30393","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":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.30393","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.30393","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":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.8068029880523682,"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Photographs":[0],"frequently":[1],"contain":[2],"\\emph{visual":[3],"distractors}":[4],"besides":[5],"foregrounds":[6],"and":[7,16,39,116,143,164],"backgrounds":[8],"of":[9,82,131,152,177],"the":[10,58,64,68,80,98],"intended":[11],"subject,":[12],"competing":[13],"for":[14,102],"attention":[15],"weakening":[17],"composition.":[18],"While":[19],"modern":[20],"editing":[21],"tools":[22],"streamline":[23],"object":[24],"removal,":[25],"identifying":[26,178],"which":[27,86,184],"objects":[28],"to":[29,47,49,112,135,202],"remove":[30,67],"remains":[31],"a":[32,128,149,198],"mostly":[33],"manual":[34],"process.":[35],"Existing":[36],"saliency":[37],"models":[38],"open-vocabulary":[40],"detectors":[41],"operate":[42],"without":[43],"subject":[44],"awareness,":[45],"failing":[46],"adapt":[48],"shifting":[50],"user":[51],"intent.":[52],"Furthermore,":[53],"context-agnostic":[54],"removal":[55],"may":[56],"disrupt":[57],"scene's":[59],"semantic":[60],"coherence":[61],"(e.g.,":[62],"keep":[63],"person":[65],"but":[66,180],"chair":[69],"they":[70],"are":[71,123,174],"sitting":[72],"on).":[73],"To":[74],"address":[75],"these":[76],"limitations,":[77],"we":[78],"formalize":[79],"task":[81],"subject-aware":[83,107],"distractor":[84,153],"localization,":[85],"identifies":[87],"distractors":[88,188],"while":[89],"retaining":[90],"compositionally":[91],"essential":[92],"objects.":[93],"This":[94],"paper":[95],"introduces":[96],"\\textsc{SADL},":[97],"first":[99],"real-world":[100],"benchmark":[101],"this":[103,193],"task,":[104],"comprising":[105],"1,800":[106],"cases":[108],"across":[109],"1,000":[110],"photographs":[111],"enable":[113],"systematic":[114],"evaluation":[115],"facilitate":[117],"future":[118],"research.":[119],"In":[120],"total,":[121],"there":[122],"14,617":[124],"annotated":[125],"candidates,":[126],"including":[127],"robust":[129],"set":[130],"1,938":[132],"hard":[133],"negatives":[134],"stress-test":[136],"exclusion":[137,157,167],"calibration.":[138],"We":[139],"evaluate":[140],"seven":[141],"proprietary":[142],"open-weight":[144],"Vision-Language":[145],"Models":[146],"(VLMs)":[147],"on":[148],"sequential":[150],"pipeline":[151],"classification":[154],"followed":[155],"by":[156],"filtering,":[158],"structured":[159],"around":[160],"five":[161],"inclusion":[162],"factors":[163],"three":[165],"contextual":[166],"rules.":[168],"Our":[169],"analysis":[170],"reveals":[171],"that":[172],"VLMs":[173],"highly":[175],"capable":[176],"distractors,":[179],"then":[181],"over-apply":[182],"exclusion,":[183],"systematically":[185],"suppresses":[186],"true":[187],"at":[189],"scale.":[190],"By":[191],"exposing":[192],"critical":[194],"bottleneck,":[195],"\\textsc{SADL}":[196],"provides":[197],"foundational":[199],"diagnostic":[200],"tool":[201],"advance":[203],"subject-conditioned":[204],"reasoning":[205],"in":[206],"multimodal":[207],"systems.":[208]},"counts_by_year":[],"updated_date":"2026-07-01T06:29:00.853634","created_date":"2026-07-01T00:00:00"}
