{"id":"https://openalex.org/W2931925025","doi":"https://doi.org/10.1145/3323873.3325015","title":"Feature Pyramid Hashing","display_name":"Feature Pyramid Hashing","publication_year":2019,"publication_date":"2019-06-05","ids":{"openalex":"https://openalex.org/W2931925025","doi":"https://doi.org/10.1145/3323873.3325015","mag":"2931925025"},"language":"en","primary_location":{"id":"doi:10.1145/3323873.3325015","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3323873.3325015","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 on International Conference on Multimedia Retrieval","raw_type":"proceedings-article"},"type":"preprint","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1904.02325","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100629259","display_name":"Yifan Yang","orcid":"https://orcid.org/0000-0002-8176-3792"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yifan Yang","raw_affiliation_strings":["Sun Yat-Sen University, Guangzhou, China","Sun-Yat-Sen University"],"affiliations":[{"raw_affiliation_string":"Sun Yat-Sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]},{"raw_affiliation_string":"Sun-Yat-Sen University","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5051266997","display_name":"Libing Geng","orcid":null},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Libing Geng","raw_affiliation_strings":["Sun Yat-Sen University, Guangzhou, China","Sun-Yat-Sen University"],"affiliations":[{"raw_affiliation_string":"Sun Yat-Sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]},{"raw_affiliation_string":"Sun-Yat-Sen University","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5076868018","display_name":"Hanjiang Lai","orcid":"https://orcid.org/0000-0001-8057-6744"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hanjiang Lai","raw_affiliation_strings":["Sun Yat-Sen University & Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, China","Sun Yat-sen University & Guangdong Key Laboratory of Big Data Analysis and Processing,, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"Sun Yat-Sen University & Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]},{"raw_affiliation_string":"Sun Yat-sen University & Guangdong Key Laboratory of Big Data Analysis and Processing,, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087294951","display_name":"Yan Pan","orcid":"https://orcid.org/0000-0002-0466-3763"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yan Pan","raw_affiliation_strings":["Sun Yat-Sen University, Guangzhou, China","\u2002Sun Yat\u2010sen University, Guangzhou, China"],"affiliations":[{"raw_affiliation_string":"Sun Yat-Sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]},{"raw_affiliation_string":"\u2002Sun Yat\u2010sen University, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5017205177","display_name":"Jian Yin","orcid":"https://orcid.org/0000-0002-1214-5384"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jian Yin","raw_affiliation_strings":["Sun Yat-Sen University & Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, China","Sun-Yat-Sen University"],"affiliations":[{"raw_affiliation_string":"Sun Yat-Sen University & Guangdong Key Laboratory of Big Data Analysis and Processing, Guangzhou, China","institution_ids":["https://openalex.org/I157773358"]},{"raw_affiliation_string":"Sun-Yat-Sen University","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5100629259"],"corresponding_institution_ids":["https://openalex.org/I157773358"],"apc_list":null,"apc_paid":null,"fwci":0.10688576,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.4062046,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"114","last_page":"122"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10627","display_name":"Advanced Image and Video Retrieval Techniques","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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","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/T10824","display_name":"Image Retrieval and Classification Techniques","score":0.9904999732971191,"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":0.9858999848365784,"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":0.8023595809936523},{"id":"https://openalex.org/keywords/pyramid","display_name":"Pyramid (geometry)","score":0.794864296913147},{"id":"https://openalex.org/keywords/hash-function","display_name":"Hash function","score":0.7442245483398438},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6730831861495972},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6455385684967041},{"id":"https://openalex.org/keywords/layer","display_name":"Layer (electronics)","score":0.5563075542449951},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5511040687561035},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5283190608024597},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5093687772750854},{"id":"https://openalex.org/keywords/image-retrieval","display_name":"Image retrieval","score":0.4792356789112091},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.44738179445266724},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.411251962184906}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8023595809936523},{"id":"https://openalex.org/C142575187","wikidata":"https://www.wikidata.org/wiki/Q3358290","display_name":"Pyramid (geometry)","level":2,"score":0.794864296913147},{"id":"https://openalex.org/C99138194","wikidata":"https://www.wikidata.org/wiki/Q183427","display_name":"Hash function","level":2,"score":0.7442245483398438},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6730831861495972},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6455385684967041},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.5563075542449951},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5511040687561035},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5283190608024597},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5093687772750854},{"id":"https://openalex.org/C1667742","wikidata":"https://www.wikidata.org/wiki/Q10927554","display_name":"Image retrieval","level":3,"score":0.4792356789112091},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.44738179445266724},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.411251962184906},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"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/C178790620","wikidata":"https://www.wikidata.org/wiki/Q11351","display_name":"Organic chemistry","level":1,"score":0.0},{"id":"https://openalex.org/C185592680","wikidata":"https://www.wikidata.org/wiki/Q2329","display_name":"Chemistry","level":0,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"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/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C120665830","wikidata":"https://www.wikidata.org/wiki/Q14620","display_name":"Optics","level":1,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1145/3323873.3325015","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3323873.3325015","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2019 on International Conference on Multimedia Retrieval","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1904.02325","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1904.02325","pdf_url":"https://arxiv.org/pdf/1904.02325","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":"mag:2931925025","is_oa":true,"landing_page_url":"http://export.arxiv.org/pdf/1904.02325","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":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.1904.02325","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1904.02325","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":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"article"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1904.02325","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1904.02325","pdf_url":"https://arxiv.org/pdf/1904.02325","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":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W205159212","https://openalex.org/W1502916507","https://openalex.org/W1686810756","https://openalex.org/W1901129140","https://openalex.org/W1939575207","https://openalex.org/W1974647172","https://openalex.org/W1992371516","https://openalex.org/W2044195942","https://openalex.org/W2121713321","https://openalex.org/W2143321506","https://openalex.org/W2149623757","https://openalex.org/W2164338181","https://openalex.org/W2194775991","https://openalex.org/W2221852422","https://openalex.org/W2251864938","https://openalex.org/W2293597654","https://openalex.org/W2293824885","https://openalex.org/W2403689645","https://openalex.org/W2411707397","https://openalex.org/W2461086877","https://openalex.org/W2464915613","https://openalex.org/W2565639579","https://openalex.org/W2595774333","https://openalex.org/W2733548594","https://openalex.org/W2781821509","https://openalex.org/W2794292174","https://openalex.org/W2798834175","https://openalex.org/W2810887794","https://openalex.org/W2888728082","https://openalex.org/W2895249671","https://openalex.org/W2963398644","https://openalex.org/W2964076257","https://openalex.org/W2964221239","https://openalex.org/W2964280870","https://openalex.org/W2964643916","https://openalex.org/W6629956336"],"related_works":["https://openalex.org/W2949710344","https://openalex.org/W3108682246","https://openalex.org/W3005728763","https://openalex.org/W3021521120","https://openalex.org/W2935160886","https://openalex.org/W2742121874","https://openalex.org/W3141336300","https://openalex.org/W3039219199","https://openalex.org/W3206688619","https://openalex.org/W2903970395","https://openalex.org/W2791260229","https://openalex.org/W2948171447","https://openalex.org/W2567001798","https://openalex.org/W2789272402","https://openalex.org/W3156246736","https://openalex.org/W2962941391","https://openalex.org/W3127076115","https://openalex.org/W3033587904","https://openalex.org/W3096806833","https://openalex.org/W3204812414"],"abstract_inverted_index":{"In":[0],"recent":[1],"years,":[2],"deep-networks-based":[3],"hashing":[4,16,62],"has":[5],"become":[6],"a":[7,59,88,99,119],"leading":[8],"approach":[9],"for":[10,34,75,136],"large-scale":[11],"image":[12,36,77],"retrieval.":[13,138],"Most":[14],"deep":[15],"approaches":[17],"use":[18],"the":[19,24,39,44,51,67,71,81,95,111,116,151,159],"high":[20,45],"layer":[21,46],"to":[22,64,93,109,128],"extract":[23],"powerful":[25],"semantic":[26,40,68],"representations.":[27],"However,":[28],"these":[29],"methods":[30],"have":[31],"limited":[32],"ability":[33],"fine-grained":[35,76,143],"retrieval":[37],"because":[38],"features":[41,97,105],"extracted":[42],"from":[43,133],"are":[47],"difficult":[48],"in":[49],"capturing":[50],"subtle":[52,72,112,131],"differences.":[53,113],"To":[54,114],"this":[55],"end,":[56],"we":[57],"propose":[58],"novel":[60,120],"two-pyramid":[61],"architecture":[63],"learn":[65],"both":[66],"information":[69,108,132],"and":[70,98,146],"appearance":[73],"details":[74],"search.":[78],"Inspired":[79],"by":[80],"feature":[82],"pyramids":[83],"of":[84],"convolutional":[85],"neural":[86],"network,":[87],"vertical":[89],"pyramid":[90,101],"is":[91,126],"proposed":[92,127,152],"capture":[94,110,129],"high-layer":[96],"horizontal":[100],"combines":[102],"multiple":[103],"low-layer":[104],"with":[106,158],"structural":[107],"fuse":[115],"low-level":[117],"features,":[118],"combination":[121],"strategy,":[122],"called":[123],"consensus":[124],"fusion,":[125],"all":[130],"several":[134],"low-layers":[135],"finer":[137],"Extensive":[139],"evaluation":[140],"on":[141],"two":[142],"datasets":[144],"CUB-200-2011":[145],"Stanford":[147],"Dogs":[148],"demonstrate":[149],"that":[150],"method":[153],"achieves":[154],"significant":[155],"performance":[156],"compared":[157],"state-of-art":[160],"baselines.":[161]},"counts_by_year":[{"year":2019,"cited_by_count":1}],"updated_date":"2026-02-09T09:26:11.010843","created_date":"2025-10-10T00:00:00"}