{"id":"https:\/\/openalex.org\/W4286751018","doi":"https:\/\/doi.org\/10.48550\/arxiv.2207.10158","title":"GOCA: Guided Online Cluster Assignment for Self-Supervised Video Representation Learning","display_name":"GOCA: Guided Online Cluster Assignment for Self-Supervised Video Representation Learning","publication_year":2022,"publication_date":"2022-07-20","ids":{"openalex":"https:\/\/openalex.org\/W4286751018","doi":"https:\/\/doi.org\/10.48550\/arxiv.2207.10158"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2207.10158","is_oa":true,"landing_page_url":"http:\/\/arxiv.org\/abs\/2207.10158","pdf_url":"https:\/\/arxiv.org\/pdf\/2207.10158","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https:\/\/arxiv.org\/pdf\/2207.10158","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https:\/\/openalex.org\/A5101901549","display_name":"Huseyin Coskun","orcid":"https:\/\/orcid.org\/0000-0002-4669-2220"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Coskun, Huseyin","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https:\/\/openalex.org\/A5052579251","display_name":"Alireza Zareian","orcid":"https:\/\/orcid.org\/0000-0003-2983-9849"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zareian, Alireza","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https:\/\/openalex.org\/A5021016566","display_name":"Joshua L. Moore","orcid":"https:\/\/orcid.org\/0000-0003-2233-2527"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Moore, Joshua L.","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https:\/\/openalex.org\/A5041092666","display_name":"Federico Tombari","orcid":"https:\/\/orcid.org\/0000-0001-5598-5212"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tombari, Federico","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https:\/\/openalex.org\/A5100337582","display_name":"Chen Wang","orcid":"https:\/\/orcid.org\/0000-0002-4630-0805"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Chen","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":5,"corresponding_author_ids":["https:\/\/openalex.org\/A5101901549"],"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\/T10812","display_name":"Human Pose and Action Recognition","score":0.9997000098228455,"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\/T10812","display_name":"Human Pose and Action Recognition","score":0.9997000098228455,"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\/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.996399998664856,"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\/T11714","display_name":"Multimodal Machine Learning Applications","score":0.995199978351593,"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\/computer-science","display_name":"Computer science","score":0.8091565370559692},{"id":"https:\/\/openalex.org\/keywords\/artificial-intelligence","display_name":"Artificial intelligence","score":0.6608325242996216},{"id":"https:\/\/openalex.org\/keywords\/cluster-analysis","display_name":"Cluster analysis","score":0.6488699316978455},{"id":"https:\/\/openalex.org\/keywords\/feature-learning","display_name":"Feature learning","score":0.6108137369155884},{"id":"https:\/\/openalex.org\/keywords\/machine-learning","display_name":"Machine learning","score":0.5459812879562378},{"id":"https:\/\/openalex.org\/keywords\/representation","display_name":"Representation (politics)","score":0.5083412528038025},{"id":"https:\/\/openalex.org\/keywords\/optical-flow","display_name":"Optical flow","score":0.4812328815460205},{"id":"https:\/\/openalex.org\/keywords\/feature","display_name":"Feature (linguistics)","score":0.46665167808532715},{"id":"https:\/\/openalex.org\/keywords\/regularization","display_name":"Regularization (linguistics)","score":0.45961469411849976},{"id":"https:\/\/openalex.org\/keywords\/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.421726256608963},{"id":"https:\/\/openalex.org\/keywords\/rgb-color-model","display_name":"RGB color model","score":0.4209356904029846},{"id":"https:\/\/openalex.org\/keywords\/unsupervised-learning","display_name":"Unsupervised learning","score":0.41625383496284485},{"id":"https:\/\/openalex.org\/keywords\/image","display_name":"Image (mathematics)","score":0.2395002841949463}],"concepts":[{"id":"https:\/\/openalex.org\/C41008148","wikidata":"https:\/\/www.wikidata.org\/wiki\/Q21198","display_name":"Computer science","level":0,"score":0.8091565370559692},{"id":"https:\/\/openalex.org\/C154945302","wikidata":"https:\/\/www.wikidata.org\/wiki\/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6608325242996216},{"id":"https:\/\/openalex.org\/C73555534","wikidata":"https:\/\/www.wikidata.org\/wiki\/Q622825","display_name":"Cluster analysis","level":2,"score":0.6488699316978455},{"id":"https:\/\/openalex.org\/C59404180","wikidata":"https:\/\/www.wikidata.org\/wiki\/Q17013334","display_name":"Feature learning","level":2,"score":0.6108137369155884},{"id":"https:\/\/openalex.org\/C119857082","wikidata":"https:\/\/www.wikidata.org\/wiki\/Q2539","display_name":"Machine learning","level":1,"score":0.5459812879562378},{"id":"https:\/\/openalex.org\/C2776359362","wikidata":"https:\/\/www.wikidata.org\/wiki\/Q2145286","display_name":"Representation (politics)","level":3,"score":0.5083412528038025},{"id":"https:\/\/openalex.org\/C155542232","wikidata":"https:\/\/www.wikidata.org\/wiki\/Q736111","display_name":"Optical flow","level":3,"score":0.4812328815460205},{"id":"https:\/\/openalex.org\/C2776401178","wikidata":"https:\/\/www.wikidata.org\/wiki\/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.46665167808532715},{"id":"https:\/\/openalex.org\/C2776135515","wikidata":"https:\/\/www.wikidata.org\/wiki\/Q17143721","display_name":"Regularization (linguistics)","level":2,"score":0.45961469411849976},{"id":"https:\/\/openalex.org\/C153180895","wikidata":"https:\/\/www.wikidata.org\/wiki\/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.421726256608963},{"id":"https:\/\/openalex.org\/C82990744","wikidata":"https:\/\/www.wikidata.org\/wiki\/Q166194","display_name":"RGB color model","level":2,"score":0.4209356904029846},{"id":"https:\/\/openalex.org\/C8038995","wikidata":"https:\/\/www.wikidata.org\/wiki\/Q1152135","display_name":"Unsupervised learning","level":2,"score":0.41625383496284485},{"id":"https:\/\/openalex.org\/C115961682","wikidata":"https:\/\/www.wikidata.org\/wiki\/Q860623","display_name":"Image (mathematics)","level":2,"score":0.2395002841949463},{"id":"https:\/\/openalex.org\/C94625758","wikidata":"https:\/\/www.wikidata.org\/wiki\/Q7163","display_name":"Politics","level":2,"score":0},{"id":"https:\/\/openalex.org\/C199539241","wikidata":"https:\/\/www.wikidata.org\/wiki\/Q7748","display_name":"Law","level":1,"score":0},{"id":"https:\/\/openalex.org\/C138885662","wikidata":"https:\/\/www.wikidata.org\/wiki\/Q5891","display_name":"Philosophy","level":0,"score":0},{"id":"https:\/\/openalex.org\/C17744445","wikidata":"https:\/\/www.wikidata.org\/wiki\/Q36442","display_name":"Political science","level":0,"score":0},{"id":"https:\/\/openalex.org\/C41895202","wikidata":"https:\/\/www.wikidata.org\/wiki\/Q8162","display_name":"Linguistics","level":1,"score":0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2207.10158","is_oa":true,"landing_page_url":"http:\/\/arxiv.org\/abs\/2207.10158","pdf_url":"https:\/\/arxiv.org\/pdf\/2207.10158","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"doi:10.48550\/arxiv.2207.10158","is_oa":true,"landing_page_url":"https:\/\/doi.org\/10.48550\/arxiv.2207.10158","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":"pmh:oai:arXiv.org:2207.10158","is_oa":true,"landing_page_url":"http:\/\/arxiv.org\/abs\/2207.10158","pdf_url":"https:\/\/arxiv.org\/pdf\/2207.10158","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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},"sustainable_development_goals":[{"score":0.550000011920929,"id":"https:\/\/metadata.un.org\/sdg\/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https:\/\/openalex.org\/W3174759195","https:\/\/openalex.org\/W3167013339","https:\/\/openalex.org\/W4287121366","https:\/\/openalex.org\/W4282049427","https:\/\/openalex.org\/W60493759","https:\/\/openalex.org\/W4308619659","https:\/\/openalex.org\/W3213069564","https:\/\/openalex.org\/W4386437125","https:\/\/openalex.org\/W4378421684","https:\/\/openalex.org\/W4294203825"],"abstract_inverted_index":{"Clustering":[0],"is":[1,156],"a":[2,61,78,88,145],"ubiquitous":[3],"tool":[4],"in":[5,158],"unsupervised":[6],"learning.":[7],"Most":[8],"of":[9,64,99,110,169,187],"the":[10,65,95,106,111,125,151,167,185,188],"existing":[11],"self-supervised":[12,160],"representation":[13],"learning":[14,161],"methods":[15],"typically":[16],"cluster":[17,97,108,119],"samples":[18],"based":[19],"on":[20,43,173,192,196,203,207],"visually":[21],"dominant":[22],"features.":[23],"While":[24],"this":[25,55,74],"works":[26],"well":[27],"for":[28,34,121,198,209],"image-based":[29],"self-supervision,":[30],"it":[31],"often":[32],"fails":[33],"videos,":[35],"which":[36,155],"require":[37],"understanding":[38],"motion":[39],"rather":[40],"than":[41],"focusing":[42],"background.":[44],"Using":[45],"optical":[46],"flow":[47],"as":[48,102],"complementary":[49],"information":[50],"to":[51,81,104,134,149],"RGB":[52],"can":[53],"alleviate":[54],"problem.":[56],"However,":[57],"we":[58,76,86,93,143,183],"observe":[59],"that":[60],"naive":[62],"combination":[63],"two":[66,83],"views":[67],"does":[68],"not":[69],"provide":[70],"meaningful":[71],"gains.":[72],"In":[73],"paper,":[75],"propose":[77,87,144],"principled":[79],"way":[80],"combine":[82],"views.":[84],"Specifically,":[85,182],"novel":[89,146],"clustering":[90],"strategy":[91,148],"where":[92],"use":[94],"initial":[96],"assignment":[98,109],"each":[100,139],"view":[101],"prior":[103],"guide":[105],"final":[107],"other":[112],"view.":[113,141],"This":[114],"idea":[115],"will":[116,128],"enforce":[117],"similar":[118],"structures":[120],"both":[122],"views,":[123],"and":[124,132,179,194,201,205],"formed":[126],"clusters":[127],"be":[129],"semantically":[130],"abstract":[131],"robust":[133],"noisy":[135],"inputs":[136],"coming":[137],"from":[138],"individual":[140],"Additionally,":[142],"regularization":[147],"address":[150],"feature":[152],"collapse":[153],"problem,":[154],"common":[157],"cluster-based":[159],"methods.":[162],"Our":[163],"extensive":[164],"evaluation":[165],"shows":[166],"effectiveness":[168],"our":[170],"learned":[171],"representations":[172],"downstream":[174],"tasks,":[175],"e.g.,":[176],"video":[177,199,210],"retrieval":[178],"action":[180],"recognition.":[181],"outperform":[184],"state":[186],"art":[189],"by":[190],"7%":[191],"UCF":[193,204],"4%":[195],"HMDB":[197,208],"retrieval,":[200],"5%":[202],"6%":[206],"classification":[211]},"counts_by_year":[],"updated_date":"2026-02-09T09:26:11.010843","created_date":"2022-07-23T00:00:00"}