{"id":"https://openalex.org/W7128705174","doi":"https://doi.org/10.48550/arxiv.2602.11005","title":"Interpretable Vision Transformers in Monocular Depth Estimation via SVDA","display_name":"Interpretable Vision Transformers in Monocular Depth Estimation via SVDA","publication_year":2026,"publication_date":"2026-02-11","ids":{"openalex":"https://openalex.org/W7128705174","doi":"https://doi.org/10.48550/arxiv.2602.11005"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2602.11005","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.11005","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":"article"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2602.11005","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5069240991","display_name":"Vasileios Arampatzakis","orcid":"https://orcid.org/0000-0003-4320-3740"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Arampatzakis, Vasileios","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125761293","display_name":"George Pavlidis","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Pavlidis, George","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5064706177","display_name":"Nikolaos Mitianoudis","orcid":"https://orcid.org/0000-0003-0898-6102"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mitianoudis, Nikolaos","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5109991926","display_name":"Nikos Papamarkos","orcid":"https://orcid.org/0000-0003-2730-0006"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Papamarkos, Nikos","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5069240991"],"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/T10036","display_name":"Advanced Neural Network Applications","score":0.3061999976634979,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.3061999976634979,"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/T10531","display_name":"Advanced Vision and Imaging","score":0.26330000162124634,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.0681999996304512,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.8693000078201294},{"id":"https://openalex.org/keywords/monocular","display_name":"Monocular","score":0.7829999923706055},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.5002999901771545},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.49869999289512634},{"id":"https://openalex.org/keywords/diagonal","display_name":"Diagonal","score":0.3513999879360199},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3353999853134155},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.3352999985218048}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.8693000078201294},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.7829999923706055},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7074000239372253},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6187999844551086},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6101999878883362},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.5002999901771545},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.49869999289512634},{"id":"https://openalex.org/C130367717","wikidata":"https://www.wikidata.org/wiki/Q189791","display_name":"Diagonal","level":2,"score":0.3513999879360199},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3353999853134155},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.3352999985218048},{"id":"https://openalex.org/C158829959","wikidata":"https://www.wikidata.org/wiki/Q1640606","display_name":"Monocular vision","level":2,"score":0.3325999975204468},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.3149000108242035},{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.2881999909877777},{"id":"https://openalex.org/C185142706","wikidata":"https://www.wikidata.org/wiki/Q1134404","display_name":"Covariance matrix","level":2,"score":0.2858999967575073},{"id":"https://openalex.org/C5339829","wikidata":"https://www.wikidata.org/wiki/Q1425977","display_name":"Machine vision","level":2,"score":0.28529998660087585},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2526000142097473},{"id":"https://openalex.org/C205711294","wikidata":"https://www.wikidata.org/wiki/Q176953","display_name":"Rendering (computer graphics)","level":2,"score":0.2513999938964844}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2602.11005","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.11005","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":"doi:10.48550/arxiv.2602.11005","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2602.11005","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":"article"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Monocular":[0],"depth":[1,153],"estimation":[2,154],"is":[3],"a":[4,61,157],"central":[5],"problem":[6],"in":[7,12,123,133,151],"computer":[8],"vision":[9],"with":[10],"applications":[11],"robotics,":[13],"AR,":[14],"and":[15,83,114,120,155],"autonomous":[16],"driving,":[17],"yet":[18],"the":[19,35,41,138],"self-attention":[20],"mechanisms":[21],"that":[22,72,86,107,130],"drive":[23],"modern":[24],"Transformer":[25,38],"architectures":[26],"remain":[27,131],"opaque.":[28],"We":[29],"introduce":[30],"SVD-Inspired":[31],"Attention":[32],"(SVDA)":[33],"into":[34,65],"Dense":[36],"Prediction":[37],"(DPT),":[39],"providing":[40],"first":[42],"spectrally":[43],"structured":[44],"formulation":[45],"of":[46,140],"attention":[47,70,125,141],"for":[48],"dense":[49,162],"prediction":[50,163],"tasks.":[51],"SVDA":[52,87,102,148],"decouples":[53],"directional":[54],"alignment":[55],"from":[56,142],"spectral":[57,105],"modulation":[58],"by":[59],"embedding":[60],"learnable":[62],"diagonal":[63],"matrix":[64],"normalized":[66],"query-key":[67],"interactions,":[68],"enabling":[69],"maps":[71],"are":[73],"intrinsically":[74],"interpretable":[75],"rather":[76],"than":[77],"post-hoc":[78],"approximations.":[79],"Experiments":[80],"on":[81],"KITTI":[82],"NYU-v2":[84],"show":[85],"preserves":[88],"or":[89],"slightly":[90],"improves":[91],"predictive":[92],"accuracy":[93],"while":[94],"adding":[95],"only":[96],"minor":[97],"computational":[98],"overhead.":[99],"More":[100],"importantly,":[101],"unlocks":[103],"six":[104],"indicators":[106],"quantify":[108],"entropy,":[109],"rank,":[110],"sparsity,":[111],"alignment,":[112],"selectivity,":[113],"robustness.":[115],"These":[116],"reveal":[117],"consistent":[118],"cross-dataset":[119],"depth-wise":[121],"patterns":[122],"how":[124],"organizes":[126],"during":[127],"training,":[128],"insights":[129],"inaccessible":[132],"standard":[134],"Transformers.":[135],"By":[136],"shifting":[137],"role":[139],"opaque":[143],"mechanism":[144],"to":[145],"quantifiable":[146],"descriptor,":[147],"redefines":[149],"interpretability":[150],"monocular":[152],"opens":[156],"principled":[158],"avenue":[159],"toward":[160],"transparent":[161],"models.":[164]},"counts_by_year":[],"updated_date":"2026-02-13T13:40:29.240086","created_date":"2026-02-13T00:00:00"}