{"id":"https://openalex.org/W3194654168","doi":"https://doi.org/10.1145/3447548.3467239","title":"Extremely Compact Non-local Representation Learning","display_name":"Extremely Compact Non-local Representation Learning","publication_year":2021,"publication_date":"2021-08-13","ids":{"openalex":"https://openalex.org/W3194654168","doi":"https://doi.org/10.1145/3447548.3467239","mag":"3194654168"},"language":"en","primary_location":{"id":"doi:10.1145/3447548.3467239","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3447548.3467239","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5015521097","display_name":"Ansheng You","orcid":null},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Ansheng You","raw_affiliation_strings":["Alibaba Group, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Alibaba Group, Hangzhou, China","institution_ids":["https://openalex.org/I45928872"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068611007","display_name":"Xiangzeng Zhou","orcid":"https://orcid.org/0000-0003-3123-6561"},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiangzeng Zhou","raw_affiliation_strings":["Alibaba Group, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Alibaba Group, Beijing, China","institution_ids":["https://openalex.org/I45928872"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059194274","display_name":"Yingya Zhang","orcid":null},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yingya Zhang","raw_affiliation_strings":["Alibaba Group, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Alibaba Group, Beijing, China","institution_ids":["https://openalex.org/I45928872"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100667153","display_name":"Pan Pan","orcid":"https://orcid.org/0000-0001-5828-0234"},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Pan Pan","raw_affiliation_strings":["Alibaba Group, Beijing, China"],"affiliations":[{"raw_affiliation_string":"Alibaba Group, Beijing, China","institution_ids":["https://openalex.org/I45928872"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5110763379","display_name":"Yinghui Xu","orcid":null},"institutions":[{"id":"https://openalex.org/I45928872","display_name":"Alibaba Group (China)","ror":"https://ror.org/00k642b80","country_code":"CN","type":"company","lineage":["https://openalex.org/I45928872"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yinghui Xu","raw_affiliation_strings":["Alibaba Group, Hangzhou, China"],"affiliations":[{"raw_affiliation_string":"Alibaba Group, Hangzhou, China","institution_ids":["https://openalex.org/I45928872"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5015521097"],"corresponding_institution_ids":["https://openalex.org/I45928872"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.09919935,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2057","last_page":"2065"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998999834060669,"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":0.9998999834060669,"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":0.9991000294685364,"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":0.9976000189781189,"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":0.7220098972320557},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.4898969531059265},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.4842779040336609},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.4819481074810028},{"id":"https://openalex.org/keywords/pooling","display_name":"Pooling","score":0.45852676033973694},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.45768678188323975},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.3464592695236206},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3433595895767212},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3164646029472351}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7220098972320557},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.4898969531059265},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.4842779040336609},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.4819481074810028},{"id":"https://openalex.org/C70437156","wikidata":"https://www.wikidata.org/wiki/Q7228652","display_name":"Pooling","level":2,"score":0.45852676033973694},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.45768678188323975},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.3464592695236206},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3433595895767212},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3164646029472351}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3447548.3467239","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3447548.3467239","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 27th ACM SIGKDD Conference on Knowledge Discovery & Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W1903029394","https://openalex.org/W2104657103","https://openalex.org/W2340897893","https://openalex.org/W2519887557","https://openalex.org/W2549139847","https://openalex.org/W2556967412","https://openalex.org/W2560023338","https://openalex.org/W2601564443","https://openalex.org/W2752782242","https://openalex.org/W2776638780","https://openalex.org/W2780664485","https://openalex.org/W2799166040","https://openalex.org/W2799213142","https://openalex.org/W2886934227","https://openalex.org/W2889814224","https://openalex.org/W2892220259","https://openalex.org/W2948080074","https://openalex.org/W2955813853","https://openalex.org/W2962835968","https://openalex.org/W2963091558","https://openalex.org/W2963319519","https://openalex.org/W2963342403","https://openalex.org/W2963420686","https://openalex.org/W2963616706","https://openalex.org/W2963727650","https://openalex.org/W2963840672","https://openalex.org/W2964241181","https://openalex.org/W2965391153","https://openalex.org/W2966926453","https://openalex.org/W2981899103","https://openalex.org/W2989684653","https://openalex.org/W2990503944","https://openalex.org/W2991062542","https://openalex.org/W2993235622","https://openalex.org/W2999219213","https://openalex.org/W3109301572"],"related_works":["https://openalex.org/W2953234277","https://openalex.org/W2626256601","https://openalex.org/W147410782","https://openalex.org/W2900413183","https://openalex.org/W4390975304","https://openalex.org/W3022252430","https://openalex.org/W4287804464","https://openalex.org/W2351938575","https://openalex.org/W2388359778","https://openalex.org/W2477549100"],"abstract_inverted_index":{"In":[0,82,105],"contrast":[1],"to":[2,119,168,185],"regular":[3],"convolutions":[4],"with":[5,94,147],"local":[6],"receptive":[7],"fields,":[8],"non-local":[9,45,62,90,115,247],"operations":[10,46],"have":[11,26],"widely":[12],"proven":[13],"an":[14,87],"effective":[15,61],"method":[16],"for":[17],"modeling":[18],"long-range":[19],"dependencies.":[20],"Although":[21],"lots":[22],"of":[23,133,195,202],"prior":[24],"works":[25],"been":[27],"proposed,":[28],"prohibitive":[29],"computation":[30,52],"and":[31,76,164,200,225],"GPU":[32,254],"memory":[33,255],"occupation":[34],"are":[35],"still":[36],"the":[37,103,127,131,151,154,170,198],"major":[38],"concerns.":[39],"Different":[40],"from":[41,126],"that":[42,57],"carrying":[43],"out":[44],"pixel-wise":[47],"or":[48,211],"channel-wise":[49],"in":[50,243,253],"a":[51,65,99,108,114,121,134,174,192,230],"intensive":[53],"way,":[54],"we":[55,58,85],"argue":[56],"can":[59,71,208],"achieve":[60,209],"operation":[63,116],"using":[64],"more":[66,74],"compact":[67,89,122,142,155],"high-order":[68,95,141,156],"statistic,":[69],"which":[70],"be":[72],"computed":[73],"efficiently":[75],"may":[77],"convey":[78],"some":[79,160],"high-level":[80,148],"information.":[81],"this":[83,140],"paper,":[84],"propose":[86],"extremely":[88],"learning":[91],"module":[92,180],"(CoNL)":[93],"reasoning":[96],"based":[97],"on":[98,191,218],"graph":[100,136],"convolution":[101,137],"as":[102,113],"core.":[104],"our":[106,203,237],"CoNL,":[107],"global":[109,161],"Hadamard":[110],"pooling":[111],"(GHP)":[112],"is":[117,144,165,181],"used":[118],"extract":[120],"second-order":[123],"feature":[124],"vector":[125,143,157],"input":[128,171],"tensor.":[129],"With":[130],"help":[132],"light-weight":[135],"network":[138],"(GCN),":[139],"further":[145],"refined":[146],"reasoning.":[149],"After":[150],"GCN":[152],"refinement,":[153],"intuitively":[158],"indicates":[159],"semantic":[162,221],"characteristics,":[163],"eventually":[166],"applied":[167],"enhance":[169],"tensor":[172],"through":[173],"channel":[175],"scaling":[176],"operation.":[177],"The":[178,205],"CoNL":[179,207],"designed":[182],"easily":[183],"pluggable":[184],"upgrade":[186],"existing":[187],"networks.":[188],"Extensive":[189],"experiments":[190],"wide":[193],"range":[194],"tasks":[196],"demonstrate":[197],"effectiveness":[199],"efficiency":[201],"work.":[204],"proposed":[206],"comparable":[210],"superior":[212],"performance":[213],"over":[214],"previous":[215],"state-of-the-art":[216],"baselines":[217],"video":[219],"recognition,":[220],"segmentation,":[222],"object":[223],"detection":[224],"instance":[226],"segmentation":[227],"tasks.":[228],"For":[229],"96":[231,233],"x":[232,234,241,251],"2048":[235],"input,":[236],"block":[238,248],"consumes":[239],"13.6":[240],"less":[242],"computational":[244],"cost":[245],"than":[246],"while":[249],"7.6":[250],"smaller":[252],"occupation.":[256]},"counts_by_year":[],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}