{"id":"https://openalex.org/W2808254576","doi":"https://doi.org/10.1145/3206025.3206027","title":"Learning Multilevel Semantic Similarity for Large-Scale Multi-Label Image Retrieval","display_name":"Learning Multilevel Semantic Similarity for Large-Scale Multi-Label Image Retrieval","publication_year":2018,"publication_date":"2018-06-05","ids":{"openalex":"https://openalex.org/W2808254576","doi":"https://doi.org/10.1145/3206025.3206027","mag":"2808254576"},"language":"en","primary_location":{"id":"doi:10.1145/3206025.3206027","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3206025.3206027","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval","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/A5100619501","display_name":"Ge Song","orcid":"https://orcid.org/0000-0002-2159-8203"},"institutions":[{"id":"https://openalex.org/I9842412","display_name":"Nanjing University of Aeronautics and Astronautics","ror":"https://ror.org/01scyh794","country_code":"CN","type":"education","lineage":["https://openalex.org/I9842412"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ge Song","raw_affiliation_strings":["Nanjing University of Aeronautics and Astronautics, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University of Aeronautics and Astronautics, Nanjing, China","institution_ids":["https://openalex.org/I9842412"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5004478562","display_name":"Xiaoyang Tan","orcid":"https://orcid.org/0000-0002-2683-8667"},"institutions":[{"id":"https://openalex.org/I9842412","display_name":"Nanjing University of Aeronautics and Astronautics","ror":"https://ror.org/01scyh794","country_code":"CN","type":"education","lineage":["https://openalex.org/I9842412"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoyang Tan","raw_affiliation_strings":["Nanjing University of Aeronautics and Astronautics, Nanjing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Nanjing University of Aeronautics and Astronautics, Nanjing, China","institution_ids":["https://openalex.org/I9842412"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I9842412"],"apc_list":null,"apc_paid":null,"fwci":0.636,"has_fulltext":false,"cited_by_count":10,"citation_normalized_percentile":{"value":0.74137593,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"64","last_page":"72"},"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":1.0,"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":1.0,"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":0.9977999925613403,"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":0.9976999759674072,"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":0.8403599262237549},{"id":"https://openalex.org/keywords/image-retrieval","display_name":"Image retrieval","score":0.6741369962692261},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.6022726893424988},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5897680521011353},{"id":"https://openalex.org/keywords/hash-function","display_name":"Hash function","score":0.5774718523025513},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.537281334400177},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4924958646297455},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.4490419924259186},{"id":"https://openalex.org/keywords/semantic-similarity","display_name":"Semantic similarity","score":0.4481474757194519},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.4327501356601715},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.25534623861312866}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8403599262237549},{"id":"https://openalex.org/C1667742","wikidata":"https://www.wikidata.org/wiki/Q10927554","display_name":"Image retrieval","level":3,"score":0.6741369962692261},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.6022726893424988},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5897680521011353},{"id":"https://openalex.org/C99138194","wikidata":"https://www.wikidata.org/wiki/Q183427","display_name":"Hash function","level":2,"score":0.5774718523025513},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.537281334400177},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4924958646297455},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.4490419924259186},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.4481474757194519},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.4327501356601715},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.25534623861312866},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3206025.3206027","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3206025.3206027","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2018 ACM on International Conference on Multimedia Retrieval","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.5099999904632568}],"awards":[{"id":"https://openalex.org/G5529932689","display_name":null,"funder_award_id":"61672280,61373060,61732006","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":35,"referenced_works":["https://openalex.org/W1502916507","https://openalex.org/W1923967535","https://openalex.org/W1962480448","https://openalex.org/W1965894418","https://openalex.org/W1974647172","https://openalex.org/W1985554184","https://openalex.org/W2031489346","https://openalex.org/W2155803963","https://openalex.org/W2155893237","https://openalex.org/W2188351697","https://openalex.org/W2194775991","https://openalex.org/W2293597654","https://openalex.org/W2296447001","https://openalex.org/W2464915613","https://openalex.org/W2510312297","https://openalex.org/W2593378684","https://openalex.org/W2594105920","https://openalex.org/W2603445054","https://openalex.org/W2606271862","https://openalex.org/W2618604876","https://openalex.org/W2619098136","https://openalex.org/W2619432746","https://openalex.org/W2621176975","https://openalex.org/W2656999302","https://openalex.org/W2735001949","https://openalex.org/W2737041163","https://openalex.org/W2737652487","https://openalex.org/W2739130108","https://openalex.org/W2744926832","https://openalex.org/W2766471938","https://openalex.org/W2963398644","https://openalex.org/W2964280870","https://openalex.org/W3125882365","https://openalex.org/W4251560691","https://openalex.org/W6601258443"],"related_works":["https://openalex.org/W3125011624","https://openalex.org/W1508631387","https://openalex.org/W2370917603","https://openalex.org/W2952760143","https://openalex.org/W2017776670","https://openalex.org/W2347897961","https://openalex.org/W2114797768","https://openalex.org/W2380654781","https://openalex.org/W2176214140","https://openalex.org/W2516873349"],"abstract_inverted_index":{"We":[0],"present":[1,48],"a":[2,44,54,64,103],"novel":[3],"Deep":[4],"Supervised":[5],"Hashing":[6],"with":[7,22,93],"code":[8],"operation":[9],"(DSOH)":[10],"method":[11,42,141],"for":[12],"large-scale":[13],"multi-label":[14],"image":[15,90],"retrieval.":[16],"This":[17],"approach":[18],"is":[19,70,91,117,127,142],"in":[20,25,67,85,129],"contrast":[21],"existing":[23],"methods":[24],"that":[26,138],"we":[27,101],"respect":[28],"both":[29],"the":[30,34,75,86,110,139],"intention":[31,77],"gap":[32],"and":[33,62,133],"intrinsic":[35],"multilevel":[36,149],"similarity":[37,112],"of":[38,114],"multi-labels.":[39],"Particularly,":[40],"our":[41,68,134],"allows":[43],"user":[45],"to":[46,57,73,96,108,119,122,146],"simultaneously":[47],"multiple":[49,94],"query":[50,76],"images":[51],"rather":[52],"than":[53],"single":[55,81],"one":[56],"better":[58],"express":[59],"her":[60],"intention,":[61],"correspondingly":[63],"separate":[65],"sub-network":[66],"architecture":[69],"specifically":[71],"designed":[72],"fuse":[74],"represented":[78],"by":[79],"each":[80,89],"query.":[82],"Furthermore,":[83],"as":[84],"training":[87],"stage,":[88],"annotated":[92],"labels":[95],"enrich":[97],"its":[98],"semantic":[99,150],"representation,":[100],"propose":[102],"new":[104],"margin-adaptive":[105],"triplet":[106],"loss":[107],"learn":[109,147],"fine-grained":[111],"structure":[113],"multi-labels,":[115],"which":[116],"known":[118],"be":[120],"hard":[121],"capture.":[123],"The":[124],"whole":[125],"system":[126],"trained":[128],"an":[130],"end-to-end":[131],"manner,":[132],"experimental":[135],"results":[136],"demonstrate":[137],"proposed":[140],"not":[143],"only":[144],"able":[145],"useful":[148],"similarity-preserving":[151],"binary":[152],"codes":[153],"but":[154],"also":[155],"achieves":[156],"state-of-the-art":[157],"retrieval":[158],"performance":[159],"on":[160],"three":[161],"popular":[162],"datasets.":[163]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":3},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1}],"updated_date":"2026-06-26T08:34:08.712188","created_date":"2025-10-10T00:00:00"}