{"id":"https://openalex.org/W7140296555","doi":"https://doi.org/10.48550/arxiv.2603.22624","title":"Toward Faithful Segmentation Attribution via Benchmarking and Dual-Evidence Fusion","display_name":"Toward Faithful Segmentation Attribution via Benchmarking and Dual-Evidence Fusion","publication_year":2026,"publication_date":"2026-03-23","ids":{"openalex":"https://openalex.org/W7140296555","doi":"https://doi.org/10.48550/arxiv.2603.22624"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2603.22624","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.22624","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2603.22624","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5076388891","display_name":"Abu Noman Sakib","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Sakib, Abu Noman Md","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130610185","display_name":"OFM Riaz Rahman Aranya","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Aranya, OFM Riaz Rahman","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5130541562","display_name":"Kevin Desai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Desai, Kevin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5130588795","display_name":"Zijie Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Zijie","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5076388891"],"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.3465000092983246,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.3465000092983246,"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":0.20469999313354492,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.19869999587535858,"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/pascal","display_name":"Pascal (unit)","score":0.8805000185966492},{"id":"https://openalex.org/keywords/benchmarking","display_name":"Benchmarking","score":0.7491000294685364},{"id":"https://openalex.org/keywords/attribution","display_name":"Attribution","score":0.7443000078201294},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.7250999808311462},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6881999969482422},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.4805000126361847}],"concepts":[{"id":"https://openalex.org/C75608658","wikidata":"https://www.wikidata.org/wiki/Q44395","display_name":"Pascal (unit)","level":2,"score":0.8805000185966492},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.7491000294685364},{"id":"https://openalex.org/C143299363","wikidata":"https://www.wikidata.org/wiki/Q900584","display_name":"Attribution","level":2,"score":0.7443000078201294},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7250999808311462},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6881999969482422},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6686999797821045},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6270999908447266},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.4805000126361847},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4221000075340271},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.41190001368522644},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.33480000495910645},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.33090001344680786},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.3021000027656555},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.29670000076293945},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2802000045776367},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2685999870300293},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.25540000200271606},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.25360000133514404}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2603.22624","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.22624","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":"doi:10.48550/arxiv.2603.22624","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2603.22624","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":"article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.4163057208061218,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Attribution":[0,75],"maps":[1],"for":[2,151],"semantic":[3],"segmentation":[4,156],"are":[5,105],"almost":[6],"always":[7],"judged":[8],"by":[9],"visual":[10,146],"plausibility.":[11],"Yet":[12],"looking":[13],"convincing":[14],"does":[15],"not":[16],"guarantee":[17],"that":[18,28,48,80,141],"the":[19,24,33,70,124],"highlighted":[20],"pixels":[21],"actually":[22],"drive":[23],"model's":[25],"prediction,":[26],"nor":[27],"attribution":[29,139],"credit":[30],"stays":[31],"within":[32],"target":[34],"region.":[35],"These":[36],"questions":[37],"require":[38],"a":[39,45,77,135,149],"dedicated":[40],"evaluation":[41],"protocol.":[42],"We":[43],"introduce":[44],"reproducible":[46],"benchmark":[47,133],"tests":[49],"intervention-based":[50],"faithfulness,":[51],"off-target":[52],"leakage,":[53],"perturbation":[54],"robustness,":[55,122],"and":[56,61,98,119],"runtime":[57],"on":[58],"Pascal":[59],"VOC":[60],"SBD":[62],"across":[63],"three":[64],"pretrained":[65],"backbones.":[66],"To":[67],"further":[68],"demonstrate":[69],"benchmark,":[71],"we":[72],"propose":[73],"Dual-Evidence":[74],"(DEA),":[76],"lightweight":[78],"correction":[79],"fuses":[81],"gradient":[82,103],"evidence":[83],"with":[84],"region-level":[85],"intervention":[86,130],"signals":[87],"through":[88],"agreement-weighted":[89],"fusion.":[90],"DEA":[91,111],"increases":[92],"emphasis":[93],"where":[94],"both":[95],"sources":[96],"agree":[97],"retains":[99],"causal":[100],"support":[101],"when":[102],"responses":[104],"unstable.":[106],"Across":[107],"all":[108],"completed":[109],"runs,":[110],"consistently":[112],"improves":[113],"deletion-based":[114],"faithfulness":[115],"over":[116],"gradient-only":[117],"baselines":[118],"preserves":[120],"strong":[121],"at":[123,161],"cost":[125],"of":[126],"additional":[127],"compute":[128],"from":[129],"passes.":[131],"The":[132],"exposes":[134],"faithfulness-stability":[136],"tradeoff":[137],"among":[138],"families":[140],"is":[142,159],"entirely":[143],"hidden":[144],"under":[145],"evaluation,":[147],"providing":[148],"foundation":[150],"principled":[152],"method":[153],"selection":[154],"in":[155],"explainability.":[157],"Code":[158],"available":[160],"https://github.com/anmspro/DEA.":[162]},"counts_by_year":[],"updated_date":"2026-03-26T06:10:45.909354","created_date":"2026-03-26T00:00:00"}
