{"id":"https://openalex.org/W4214718130","doi":"https://doi.org/10.1109/iccv48922.2021.01039","title":"Space-Time Crop & Attend: Improving Cross-modal Video Representation Learning","display_name":"Space-Time Crop & Attend: Improving Cross-modal Video Representation Learning","publication_year":2021,"publication_date":"2021-10-01","ids":{"openalex":"https://openalex.org/W4214718130","doi":"https://doi.org/10.1109/iccv48922.2021.01039","mag":"3139264293"},"language":"en","primary_location":{"id":"doi:10.1109/iccv48922.2021.01039","doi":"https://doi.org/10.1109/iccv48922.2021.01039","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccv48922.2021.01039","pdf_url":null,"source":{"id":"https://openalex.org/S4363607764","display_name":"IEEE/CVF International Conference on Computer Vision (ICCV)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE/CVF International Conference on Computer Vision (ICCV)","raw_type":"proceedings-article"},"volume":null,"type":"conference-paper","is_traditional":true,"indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2103.10211","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5057806352","display_name":"Mandela Patrick","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4210114444","display_name":"Meta (United States)","ror":"https://ror.org/01zbnvs85","country_code":"US","type":"company","lineage":["https://openalex.org/I4210114444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mandela Patrick","raw_affiliation_strings":["Facebook AI Research"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Facebook AI Research","institution_ids":["https://openalex.org/I4210114444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5063149046","display_name":"Po-Yao Huang","orcid":"https://orcid.org/0000-0002-3319-5145","observed_orcids":["https://orcid.org/0000-0002-3319-5145"]},"institutions":[{"id":"https://openalex.org/I4210114444","display_name":"Meta (United States)","ror":"https://ror.org/01zbnvs85","country_code":"US","type":"company","lineage":["https://openalex.org/I4210114444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Po-Yao Huang","raw_affiliation_strings":["Facebook AI Research"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Facebook AI Research","institution_ids":["https://openalex.org/I4210114444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000623592","display_name":"Ishan Misra","orcid":"https://orcid.org/0000-0001-7708-7261","observed_orcids":["https://orcid.org/0000-0001-7708-7261"]},"institutions":[{"id":"https://openalex.org/I4210114444","display_name":"Meta (United States)","ror":"https://ror.org/01zbnvs85","country_code":"US","type":"company","lineage":["https://openalex.org/I4210114444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Ishan Misra","raw_affiliation_strings":["Facebook AI Research"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Facebook AI Research","institution_ids":["https://openalex.org/I4210114444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085262529","display_name":"Florian Metze","orcid":"https://orcid.org/0000-0002-6663-8600","observed_orcids":["https://orcid.org/0000-0002-6663-8600"]},"institutions":[{"id":"https://openalex.org/I4210114444","display_name":"Meta (United States)","ror":"https://ror.org/01zbnvs85","country_code":"US","type":"company","lineage":["https://openalex.org/I4210114444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Florian Metze","raw_affiliation_strings":["Facebook AI Research"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Facebook AI Research","institution_ids":["https://openalex.org/I4210114444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060511349","display_name":"Andrea Vedaldi","orcid":"https://orcid.org/0000-0003-1374-2858","observed_orcids":["https://orcid.org/0000-0003-1374-2858"]},"institutions":[{"id":"https://openalex.org/I4210114444","display_name":"Meta (United States)","ror":"https://ror.org/01zbnvs85","country_code":"US","type":"company","lineage":["https://openalex.org/I4210114444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Andrea Vedaldi","raw_affiliation_strings":["Facebook AI Research"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Facebook AI Research","institution_ids":["https://openalex.org/I4210114444"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102985946","display_name":"Yuki M. Asano","orcid":"https://orcid.org/0000-0002-8533-4020","observed_orcids":["https://orcid.org/0000-0002-8533-4020","https://orcid.org/0000-0003-2115-1417"]},"institutions":[{"id":"https://openalex.org/I40120149","display_name":"University of Oxford","ror":"https://ror.org/052gg0110","country_code":"GB","type":"education","lineage":["https://openalex.org/I40120149"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Yuki M. Asano","raw_affiliation_strings":["Oxford University"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Oxford University","institution_ids":["https://openalex.org/I40120149"]}]},{"author_position":"last","author":{"id":null,"display_name":"Joao Henriques","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I4210114444","display_name":"Meta (United States)","ror":"https://ror.org/01zbnvs85","country_code":"US","type":"company","lineage":["https://openalex.org/I4210114444"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Joao Henriques","raw_affiliation_strings":["Facebook AI Research"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Facebook AI Research","institution_ids":["https://openalex.org/I4210114444"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":2.1702,"has_fulltext":true,"cited_by_count":28,"citation_normalized_percentile":{"value":0.91969807,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":87,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"10540","last_page":"10552"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.8560000061988831,"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":0.8560000061988831,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.06629999727010727,"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":0.04650000110268593,"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/self-supervised-learning","display_name":"self-supervised learning","score":0.8450000286102295},{"id":"https://openalex.org/keywords/audio-video-fusion","display_name":"audio-video fusion","score":0.6389999985694885},{"id":"https://openalex.org/keywords/hmdb51","display_name":"HMDB51","score":0.8180000185966492},{"id":"https://openalex.org/keywords/ucf101","display_name":"UCF101","score":1.0}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7440471053123474},{"id":"https://openalex.org/C106251023","wikidata":"https://www.wikidata.org/wiki/Q851989","display_name":"Porting","level":3,"score":0.6379053592681885},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.5308911204338074},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.5148598551750183},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.505821704864502},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.49638205766677856},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4918699264526367},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.4433005750179291},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.43577489256858826},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4236745834350586},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.334919273853302},{"id":"https://openalex.org/C2777904410","wikidata":"https://www.wikidata.org/wiki/Q7397","display_name":"Software","level":2,"score":0.10508865118026733},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":6,"locations":[{"id":"doi:10.1109/iccv48922.2021.01039","doi":"https://doi.org/10.1109/iccv48922.2021.01039","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iccv48922.2021.01039","pdf_url":null,"source":{"id":"https://openalex.org/S4363607764","display_name":"IEEE/CVF International Conference on Computer Vision (ICCV)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE/CVF International Conference on Computer Vision (ICCV)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:2103.10211","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2103.10211","pdf_url":"https://arxiv.org/pdf/2103.10211","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,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"mag:3139264293","doi":null,"is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2103.10211.pdf","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,"listed_in":[],"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":"arXiv (Cornell University)","raw_type":null},{"id":"pmh:oai:ora.ox.ac.uk:uuid:eb0cf133-9ad6-4362-ade2-917075ebb727","doi":null,"is_oa":false,"landing_page_url":"https://ora.ox.ac.uk/objects/uuid:eb0cf133-9ad6-4362-ade2-917075ebb727/files/sqf85nc45s","pdf_url":null,"source":{"id":"https://openalex.org/S4306402636","display_name":"Oxford University Research Archive (ORA) (University of Oxford)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I40120149","host_organization_name":"University of Oxford","host_organization_lineage":["https://openalex.org/I40120149"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Symplectic Elements","raw_type":"http://purl.org/coar/resource_type/c_5794"},{"id":"doi:10.48550/arxiv.2103.10211","doi":"https://doi.org/10.48550/arxiv.2103.10211","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2103.10211","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,"listed_in":[],"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"},{"id":"mag:3207869058","doi":null,"is_oa":false,"landing_page_url":"https://openaccess.thecvf.com/content/ICCV2021/papers/Patrick_Space-Time_Crop__Attend_Improving_Cross-Modal_Video_Representation_Learning_ICCV_2021_paper.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4363607764","display_name":"IEEE/CVF International Conference on Computer Vision (ICCV)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"conference"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":"International Conference on Computer Vision","raw_type":null}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2103.10211","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2103.10211","pdf_url":"https://arxiv.org/pdf/2103.10211","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,"listed_in":[],"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"study_designs":[],"awards":[{"id":"https://openalex.org/G1484394898","display_name":"EPSRC Centre for Doctoral Training in Autonomous Intelligent Machines and Systems (AIMS)","funder_award_id":"EP/L015897/1","funder_id":"https://openalex.org/F4320334627","funder_display_name":"Engineering and Physical Sciences Research Council"},{"id":"https://openalex.org/G4713059963","display_name":null,"funder_award_id":"FA8750","funder_id":"https://openalex.org/F4320332180","funder_display_name":"Defense Advanced Research Projects Agency"},{"id":"https://openalex.org/G740863221","display_name":null,"funder_award_id":"FA8750-18-2-0018","funder_id":"https://openalex.org/F4320332180","funder_display_name":"Defense Advanced Research Projects Agency"},{"id":"https://openalex.org/G8444588739","display_name":null,"funder_award_id":"HR00111990063","funder_id":"https://openalex.org/F4320332180","funder_display_name":"Defense Advanced Research Projects Agency"}],"funders":[{"id":"https://openalex.org/F4320320005","display_name":"Royal Academy of Engineering","ror":"https://ror.org/0526snb40"},{"id":"https://openalex.org/F4320332180","display_name":"Defense Advanced Research Projects Agency","ror":"https://ror.org/02caytj08"},{"id":"https://openalex.org/F4320334627","display_name":"Engineering and Physical Sciences Research Council","ror":"https://ror.org/0439y7842"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4214718130.pdf","grobid_xml":"https://content.openalex.org/works/W4214718130.grobid-xml"},"referenced_works_count":171,"referenced_works":["https://openalex.org/W24089286","https://openalex.org/W343636949","https://openalex.org/W639708223","https://openalex.org/W1522301498","https://openalex.org/W1522734439","https://openalex.org/W1899185266","https://openalex.org/W1947481528","https://openalex.org/W1972567154","https://openalex.org/W2016053056","https://openalex.org/W2052666245","https://openalex.org/W2086384421","https://openalex.org/W2096391593","https://openalex.org/W2108598243","https://openalex.org/W2124386111","https://openalex.org/W2126579184","https://openalex.org/W2138621090","https://openalex.org/W2144796873","https://openalex.org/W2152790380","https://openalex.org/W2156303437","https://openalex.org/W2157498370","https://openalex.org/W2161969291","https://openalex.org/W2163605009","https://openalex.org/W2194775991","https://openalex.org/W2235034809","https://openalex.org/W2321533354","https://openalex.org/W2326925005","https://openalex.org/W2342877626","https://openalex.org/W2487442924","https://openalex.org/W2507009361","https://openalex.org/W2544224704","https://openalex.org/W2551572271","https://openalex.org/W2556967412","https://openalex.org/W2558661413","https://openalex.org/W2593116425","https://openalex.org/W2594477595","https://openalex.org/W2604379605","https://openalex.org/W2608988379","https://openalex.org/W2619082050","https://openalex.org/W2619697695","https://openalex.org/W2619947201","https://openalex.org/W2626778328","https://openalex.org/W2740238936","https://openalex.org/W2745357708","https://openalex.org/W2770804203","https://openalex.org/W2773514261","https://openalex.org/W2785325870","https://openalex.org/W2796315435","https://openalex.org/W2796992393","https://openalex.org/W2797090057","https://openalex.org/W2798991696","https://openalex.org/W2799087757","https://openalex.org/W2808195542","https://openalex.org/W2883451034","https://openalex.org/W2883725317","https://openalex.org/W2890052321","https://openalex.org/W2890952074","https://openalex.org/W2896457183","https://openalex.org/W2902016181","https://openalex.org/W2902199720","https://openalex.org/W2903192803","https://openalex.org/W2924027593","https://openalex.org/W2926645869","https://openalex.org/W2935917634","https://openalex.org/W2936774411","https://openalex.org/W2938698191","https://openalex.org/W2941505483","https://openalex.org/W2942642798","https://openalex.org/W2948242301","https://openalex.org/W2948859046","https://openalex.org/W2949178656","https://openalex.org/W2949736877","https://openalex.org/W2950180292","https://openalex.org/W2952746495","https://openalex.org/W2962756039","https://openalex.org/W2962865004","https://openalex.org/W2963091558","https://openalex.org/W2963115079","https://openalex.org/W2963155035","https://openalex.org/W2963341956","https://openalex.org/W2963563276","https://openalex.org/W2963616706","https://openalex.org/W2963631366","https://openalex.org/W2963814513","https://openalex.org/W2963902314","https://openalex.org/W2963996492","https://openalex.org/W2964037671","https://openalex.org/W2964121744","https://openalex.org/W2964171275","https://openalex.org/W2967065585","https://openalex.org/W2968124245","https://openalex.org/W2969613290","https://openalex.org/W2969876226","https://openalex.org/W2970231061","https://openalex.org/W2970608575","https://openalex.org/W2971155163","https://openalex.org/W2975357369","https://openalex.org/W2975761646","https://openalex.org/W2981851019","https://openalex.org/W2981851635","https://openalex.org/W2981852735","https://openalex.org/W2982399380","https://openalex.org/W2986405467","https://openalex.org/W2990408345","https://openalex.org/W2990500698","https://openalex.org/W2990503944","https://openalex.org/W2990616379","https://openalex.org/W2992308087","https://openalex.org/W2994536315","https://openalex.org/W2995181141","https://openalex.org/W2995460200","https://openalex.org/W2995489995","https://openalex.org/W2999004587","https://openalex.org/W3002552512","https://openalex.org/W3005680577","https://openalex.org/W3008930492","https://openalex.org/W3010094231","https://openalex.org/W3010874390","https://openalex.org/W3013845856","https://openalex.org/W3017094594","https://openalex.org/W3020933450","https://openalex.org/W3022061250","https://openalex.org/W3026092005","https://openalex.org/W3030520226","https://openalex.org/W3033210410","https://openalex.org/W3034445277","https://openalex.org/W3034556939","https://openalex.org/W3034588855","https://openalex.org/W3034658206","https://openalex.org/W3034978746","https://openalex.org/W3035060554","https://openalex.org/W3035119608","https://openalex.org/W3035265375","https://openalex.org/W3035524453","https://openalex.org/W3035635319","https://openalex.org/W3036224891","https://openalex.org/W3037195468","https://openalex.org/W3043840704","https://openalex.org/W3044220283","https://openalex.org/W3045355015","https://openalex.org/W3045687178","https://openalex.org/W3047398678","https://openalex.org/W3048065599","https://openalex.org/W3082274269","https://openalex.org/W3087305966","https://openalex.org/W3092470998","https://openalex.org/W3094502228","https://openalex.org/W3098608318","https://openalex.org/W3099495704","https://openalex.org/W3100177202","https://openalex.org/W3101065397","https://openalex.org/W3101537861","https://openalex.org/W3102696055","https://openalex.org/W3103215803","https://openalex.org/W3106485021","https://openalex.org/W3108332675","https://openalex.org/W3108655343","https://openalex.org/W3110190397","https://openalex.org/W3115964123","https://openalex.org/W3116529608","https://openalex.org/W3121735241","https://openalex.org/W3123226376","https://openalex.org/W3126337037","https://openalex.org/W3126721948","https://openalex.org/W3145385912","https://openalex.org/W3164175516","https://openalex.org/W4287605490","https://openalex.org/W4295224299","https://openalex.org/W4297808394","https://openalex.org/W6750599028","https://openalex.org/W6782288879","https://openalex.org/W6955071965"],"related_works":["https://openalex.org/W4390872158","https://openalex.org/W3145385912","https://openalex.org/W4390696496","https://openalex.org/W4383873653","https://openalex.org/W4382466151","https://openalex.org/W3200037705","https://openalex.org/W3206818284","https://openalex.org/W2997907976","https://openalex.org/W4406754280","https://openalex.org/W4225985153"],"abstract_inverted_index":{"The":[0],"quality":[1],"of":[2,15,84,162,178,206],"the":[3,13,20,81,109,160],"image":[4],"representations":[5],"obtained":[6],"from":[7,29],"self-supervised":[8],"learning":[9,21,197],"depends":[10],"strongly":[11],"on":[12,208,212,216],"type":[14],"data":[16],"augmentations":[17,53,93,142],"used":[18],"in":[19,76,147],"formulation.":[22],"Recent":[23],"papers":[24],"have":[25],"ported":[26],"these":[27,74],"methods":[28],"still":[30,62],"images":[31],"to":[32,80,108,125,139,156],"videos":[33,101],"and":[34,40,112,168,210,219],"found":[35],"that":[36,51,91,104,153],"leveraging":[37],"both":[38,177],"audio":[39],"video":[41],"signals":[42],"yields":[43],"strong":[44],"gains;":[45],"however,":[46],"they":[47],"did":[48],"not":[49,116],"find":[50],"spatial":[52,92],"such":[54,94,141],"as":[55,65,95,154],"cropping,":[56],"which":[57],"are":[58,222],"very":[59],"important":[60],"for":[61,67,87,100,123,172],"images,":[63],"work":[64,98,126],"well":[66,99,170],"videos.":[68,85],"In":[69,199],"this":[70,118,130],"paper,":[71],"we":[72,89,132,151,190,201],"improve":[73],"formulations":[75],"two":[77],"ways":[78],"unique":[79],"spatio-temporal":[82],"aspect":[83],"First,":[86],"space,":[88],"show":[90,152],"cropping":[96],"do":[97,117],"too,":[102],"but":[103],"previous":[105],"implementations,":[106],"due":[107],"high":[110],"processing":[111,173],"memory":[113],"cost,":[114],"could":[115],"at":[119],"a":[120,137,182],"scale":[121],"sufficient":[122],"it":[124],"well.":[127],"To":[128],"address":[129],"issue,":[131],"first":[133],"introduce":[134],"Feature":[135],"Crop,":[136],"method":[138],"simulate":[140],"much":[143],"more":[144],"efficiently":[145],"directly":[146],"feature":[148,174],"space.":[149],"Second,":[150],"opposed":[155],"na\u00efve":[157],"average":[158],"pooling,":[159],"use":[161],"transformer-based":[163],"attention":[164],"improves":[165],"performance":[166,193],"significantly,":[167],"is":[169],"suited":[171],"crops.":[175],"Combining":[176],"our":[179],"discoveries":[180],"into":[181],"new":[183,203],"method,":[184],"Space-Time":[185],"Crop":[186],"&":[187],"Attend":[188],"(STiCA)":[189],"achieve":[191,202],"state-of-the-art":[192,204],"across":[194],"multiple":[195],"video-representation":[196],"benchmarks.":[198],"particular,":[200],"accuracies":[205],"67.0%":[207],"HMDB-51":[209],"93.1%":[211],"UCF-101":[213],"when":[214],"pre-training":[215],"Kinetics-400.":[217],"Code":[218],"pretrained":[220],"models":[221],"available1.":[223]},"counts_by_year":[{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":9},{"year":2022,"cited_by_count":11},{"year":2021,"cited_by_count":1}],"updated_date":"2026-11-10T11:39:43.021295","created_date":"2022-03-02T00:00:00"}