{"id":"https://openalex.org/W7163036159","doi":"https://doi.org/10.48550/arxiv.2605.31597","title":"SOCO: Benchmarking Semantic Object Correspondence in Vision Foundation Models","display_name":"SOCO: Benchmarking Semantic Object Correspondence in Vision Foundation Models","publication_year":2026,"publication_date":"2026-05-29","ids":{"openalex":"https://openalex.org/W7163036159","doi":"https://doi.org/10.48550/arxiv.2605.31597"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.31597","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.31597","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.2605.31597","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137559989","display_name":"Olaf D\u00fcnkel","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"D\u00fcnkel, Olaf","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5107490232","display_name":"Basavaraj Sunagad","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sunagad, Basavaraj","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137555911","display_name":"Haoran Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Haoran","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030083876","display_name":"David T. Hoffmann","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hoffmann, David T.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020664641","display_name":"Christian Theobalt","orcid":"https://orcid.org/0000-0001-6104-6625"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Theobalt, Christian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5084928022","display_name":"Adam Kortylewski","orcid":"https://orcid.org/0000-0002-9146-4403"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kortylewski, Adam","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":0.9506000280380249,"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":0.9506000280380249,"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":0.018699999898672104,"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":0.009700000286102295,"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/benchmarking","display_name":"Benchmarking","score":0.6657999753952026},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5688999891281128},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4925000071525574},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.47519999742507935},{"id":"https://openalex.org/keywords/encode","display_name":"ENCODE","score":0.46470001339912415},{"id":"https://openalex.org/keywords/taxonomy","display_name":"Taxonomy (biology)","score":0.4587000012397766},{"id":"https://openalex.org/keywords/foundation","display_name":"Foundation (evidence)","score":0.43860000371932983},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.3921000063419342}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7215999960899353},{"id":"https://openalex.org/C86251818","wikidata":"https://www.wikidata.org/wiki/Q816754","display_name":"Benchmarking","level":2,"score":0.6657999753952026},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6477000117301941},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5688999891281128},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.5241000056266785},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4925000071525574},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.47519999742507935},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.46470001339912415},{"id":"https://openalex.org/C58642233","wikidata":"https://www.wikidata.org/wiki/Q8269924","display_name":"Taxonomy (biology)","level":2,"score":0.4587000012397766},{"id":"https://openalex.org/C2780966255","wikidata":"https://www.wikidata.org/wiki/Q5474306","display_name":"Foundation (evidence)","level":2,"score":0.43860000371932983},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3921000063419342},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.33079999685287476},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.32989999651908875},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.326200008392334},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.32420000433921814},{"id":"https://openalex.org/C2779530757","wikidata":"https://www.wikidata.org/wiki/Q1207505","display_name":"Quality (philosophy)","level":2,"score":0.320499986410141},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.32019999623298645},{"id":"https://openalex.org/C90312973","wikidata":"https://www.wikidata.org/wiki/Q7449052","display_name":"Semantic data model","level":2,"score":0.3095000088214874},{"id":"https://openalex.org/C78780964","wikidata":"https://www.wikidata.org/wiki/Q7233193","display_name":"Position paper","level":2,"score":0.2793000042438507},{"id":"https://openalex.org/C198082294","wikidata":"https://www.wikidata.org/wiki/Q3399648","display_name":"Position (finance)","level":2,"score":0.26930001378059387},{"id":"https://openalex.org/C52102323","wikidata":"https://www.wikidata.org/wiki/Q1671968","display_name":"Pose","level":2,"score":0.2502000033855438}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.31597","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.31597","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.2605.31597","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.31597","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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Measuring":[0],"structured":[1],"object":[2,28],"understanding":[3],"in":[4,40,188],"vision":[5,108,189],"foundation":[6,109,192],"models":[7,96],"remains":[8],"challenging":[9],"due":[10],"to":[11],"inconsistent":[12],"evaluation":[13,92],"protocols":[14],"and":[15,35,43,68,78,98,122,147,151,167,190],"limited":[16],"part-level":[17,101,185],"supervision.":[18],"Semantic":[19,58],"correspondence":[20,66,81,153],"(SC)":[21],"evaluates":[22],"this":[23],"capability":[24],"by":[25],"testing":[26],"whether":[27],"parts":[29],"can":[30],"be":[31],"matched":[32],"across":[33,75,119],"instances":[34],"categories":[36,77,121],"under":[37],"large":[38,94],"variations":[39],"appearance,":[41],"viewpoint,":[42],"geometry.":[44],"To":[45],"enable":[46],"a":[47,54,63,142,181],"systematic":[48],"SC":[49],"evaluation,":[50],"we":[51],"introduce":[52],"SOCO,":[53],"new":[55],"benchmark":[56,182],"for":[57,183],"Object":[59],"Correspondence":[60],"that":[61,106],"introduces":[62],"taxonomy":[64],"of":[65,93],"types":[67],"provides":[69],"consistent,":[70],"functionally":[71],"meaningful":[72],"keypoint":[73,87],"annotations":[74],"100":[76],"over":[79],"1M":[80],"pairs.":[82],"In":[83],"addition,":[84],"SOCO":[85,179],"includes":[86],"language":[88],"descriptions,":[89],"enabling":[90],"the":[91],"vision-language":[95],"(LVLMs)":[97],"their":[99],"fine-grained":[100,148],"understanding.":[102],"Comprehensive":[103],"experiments":[104],"reveal":[105],"(i)":[107],"backbones":[110],"encode":[111],"strong":[112],"semantic":[113],"structure":[114],"but":[115],"transfer":[116],"correspondences":[117],"poorly":[118],"related":[120],"only":[123],"partially":[124],"capture":[125],"object-part":[126],"position,":[127],"(ii)":[128],"LVLMs":[129],"are":[130],"stronger":[131],"at":[132,137],"text-prompted":[133],"part":[134],"localization":[135,146],"than":[136,172],"visual-reference":[138],"cross-image":[139],"matching,":[140],"exposing":[141],"gap":[143],"between":[144],"language-grounded":[145],"visual":[149],"correspondence,":[150],"(iii)":[152],"performance":[154,156],"predicts":[155],"on":[157],"dense":[158],"downstream":[159],"tasks,":[160],"including":[161],"segmentation,":[162],"tracking,":[163],"3D":[164,168],"pose":[165],"estimation,":[166],"detection,":[169],"more":[170],"strongly":[171],"ImageNet":[173],"classification.":[174],"Together,":[175],"these":[176],"findings":[177],"position":[178],"as":[180],"structured,":[184],"representation":[186],"quality":[187],"multimodal":[191],"models.":[193]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-02T00:00:00"}
