{"id":"https://openalex.org/W7160966405","doi":"https://doi.org/10.48550/arxiv.2605.09963","title":"Learning to Perceive \"Where\": Spatial Pretext Tasks for Robust Self-Supervised Learning","display_name":"Learning to Perceive \"Where\": Spatial Pretext Tasks for Robust Self-Supervised Learning","publication_year":2026,"publication_date":"2026-05-11","ids":{"openalex":"https://openalex.org/W7160966405","doi":"https://doi.org/10.48550/arxiv.2605.09963"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.09963","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.09963","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2605.09963","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135942065","display_name":"Yang Shen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shen, Yang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135981926","display_name":"Yusen Cai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cai, Yusen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5040649166","display_name":"Weronika Hryniewska-Guzik","orcid":"https://orcid.org/0000-0003-2903-6050"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hryniewska-Guzik, Weronika","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135933900","display_name":"Qing Lin","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lin, Qing","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135942982","display_name":"Mengmi Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Mengmi","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/T14339","display_name":"Image Processing and 3D Reconstruction","score":0.9556000232696533,"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/T14339","display_name":"Image Processing and 3D Reconstruction","score":0.9556000232696533,"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/T10751","display_name":"Forensic and Genetic Research","score":0.004100000020116568,"subfield":{"id":"https://openalex.org/subfields/1311","display_name":"Genetics"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T10992","display_name":"Forensic Anthropology and Bioarchaeology Studies","score":0.003800000064074993,"subfield":{"id":"https://openalex.org/subfields/1204","display_name":"Archeology"},"field":{"id":"https://openalex.org/fields/12","display_name":"Arts and Humanities"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/multi-task-learning","display_name":"Multi-task learning","score":0.5097000002861023},{"id":"https://openalex.org/keywords/robustness","display_name":"Robustness (evolution)","score":0.4851999878883362},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.48410001397132874},{"id":"https://openalex.org/keywords/spatial-relation","display_name":"Spatial relation","score":0.4327000081539154},{"id":"https://openalex.org/keywords/categorical-variable","display_name":"Categorical variable","score":0.4088999927043915},{"id":"https://openalex.org/keywords/cognitive-neuroscience-of-visual-object-recognition","display_name":"Cognitive neuroscience of visual object recognition","score":0.3937000036239624},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.38909998536109924},{"id":"https://openalex.org/keywords/categorization","display_name":"Categorization","score":0.3781000077724457},{"id":"https://openalex.org/keywords/task-analysis","display_name":"Task analysis","score":0.37619999051094055}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6894999742507935},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6536999940872192},{"id":"https://openalex.org/C28006648","wikidata":"https://www.wikidata.org/wiki/Q6934509","display_name":"Multi-task learning","level":3,"score":0.5097000002861023},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.4851999878883362},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.48410001397132874},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45019999146461487},{"id":"https://openalex.org/C27511587","wikidata":"https://www.wikidata.org/wiki/Q2178623","display_name":"Spatial relation","level":2,"score":0.4327000081539154},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.4088999927043915},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.3937000036239624},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.38909998536109924},{"id":"https://openalex.org/C94124525","wikidata":"https://www.wikidata.org/wiki/Q912550","display_name":"Categorization","level":2,"score":0.3781000077724457},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.37619999051094055},{"id":"https://openalex.org/C197352929","wikidata":"https://www.wikidata.org/wiki/Q1074074","display_name":"Inductive bias","level":4,"score":0.36899998784065247},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.36820000410079956},{"id":"https://openalex.org/C2779627259","wikidata":"https://www.wikidata.org/wiki/Q779763","display_name":"Pretext","level":3,"score":0.3637000024318695},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3269999921321869},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.32499998807907104},{"id":"https://openalex.org/C190470478","wikidata":"https://www.wikidata.org/wiki/Q2370229","display_name":"Invariant (physics)","level":2,"score":0.32429999113082886},{"id":"https://openalex.org/C64754055","wikidata":"https://www.wikidata.org/wiki/Q7574053","display_name":"Spatial contextual awareness","level":2,"score":0.31209999322891235},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.31200000643730164},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.3075000047683716},{"id":"https://openalex.org/C153938966","wikidata":"https://www.wikidata.org/wiki/Q3348148","display_name":"Object-based spatial database","level":4,"score":0.3012999892234802},{"id":"https://openalex.org/C2779405079","wikidata":"https://www.wikidata.org/wiki/Q356040","display_name":"Jigsaw","level":2,"score":0.2806999981403351},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.27630001306533813},{"id":"https://openalex.org/C155911833","wikidata":"https://www.wikidata.org/wiki/Q3817354","display_name":"Spatial intelligence","level":2,"score":0.26829999685287476},{"id":"https://openalex.org/C2985665543","wikidata":"https://www.wikidata.org/wiki/Q3560550","display_name":"Spatial learning","level":3,"score":0.25769999623298645},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.2565999925136566},{"id":"https://openalex.org/C198082294","wikidata":"https://www.wikidata.org/wiki/Q3399648","display_name":"Position (finance)","level":2,"score":0.25540000200271606},{"id":"https://openalex.org/C2780103172","wikidata":"https://www.wikidata.org/wiki/Q1309721","display_name":"Visual Objects","level":3,"score":0.2547999918460846}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.09963","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.09963","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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.2605.09963","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.09963","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":[{"id":"https://metadata.un.org/sdg/10","score":0.4508450925350189,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Existing":[0],"self-supervised":[1],"learning":[2,75],"(SSL)":[3],"methods":[4],"primarily":[5],"learn":[6],"object-invariant":[7],"representations":[8,64,182],"but":[9],"often":[10],"neglect":[11],"the":[12,37,50,76],"spatial":[13,68,126,161,169],"structure":[14,78,162],"and":[15,40,89,110,133,141,150,163,183,187],"relationships":[16,56],"among":[17],"object":[18,122],"parts.":[19],"To":[20,124],"address":[21],"this":[22],"limitation,":[23],"we":[24,128],"introduce":[25,129],"Spatial":[26],"Prediction":[27],"(SP),":[28],"a":[29,43,58,86,131,143],"spatially":[30],"aware":[31],"pretext":[32],"regression":[33],"task":[34,136,146],"that":[35],"predicts":[36],"relative":[38],"position":[39,132],"scale":[41,134],"between":[42],"pair":[44],"of":[45,79],"disentangled":[46],"local":[47],"views":[48],"from":[49],"same":[51],"image.":[52],"By":[53],"modeling":[54,168],"part-to-part":[55],"in":[57,118],"continuous":[59],"geometric":[60,164],"space,":[61],"SP":[62,82],"encourages":[63],"to":[65,179],"capture":[66],"fine-grained":[67,106],"dependencies":[69],"beyond":[70],"invariant":[71],"categorical":[72],"semantics,":[73],"thereby":[74],"compositional":[77],"visual":[80],"scenes.":[81],"is":[83],"implemented":[84],"as":[85,113,115],"decoupled":[87],"plug-in":[88],"can":[90],"be":[91,190],"seamlessly":[92],"integrated":[93],"into":[94],"diverse":[95],"SSL":[96],"frameworks.":[97],"Extensive":[98],"experiments":[99],"show":[100],"consistent":[101],"improvements":[102],"across":[103],"image":[104,138],"recognition,":[105],"classification,":[107],"semantic":[108],"segmentation,":[109],"depth":[111],"estimation,":[112],"well":[114],"substantial":[116],"gains":[117],"out-of-distribution":[119],"robustness":[120],"for":[121,176],"recognition.":[123],"evaluate":[125],"reasoning,":[127],"(1)":[130],"prediction":[135],"on":[137,156],"patch":[139,148],"pairs":[140],"(2)":[142],"jigsaw":[144],"understanding":[145],"requiring":[147],"reordering":[149],"recognition":[151],"after":[152],"reconstruction.":[153],"Strong":[154],"performance":[155],"these":[157],"tasks":[158],"indicates":[159],"improved":[160],"awareness.":[165],"Overall,":[166],"explicitly":[167],"information":[170],"provides":[171],"an":[172],"effective":[173],"inductive":[174],"bias":[175],"SSL,":[177],"leading":[178],"more":[180],"structured":[181],"better":[184],"generalization.":[185],"Code":[186],"models":[188],"will":[189],"released.":[191]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-13T00:00:00"}
