{"id":"https://openalex.org/W4294659041","doi":"https://doi.org/10.18293/seke2022-012","title":"Zero-Shot Object Detection with Multi-label Context","display_name":"Zero-Shot Object Detection with Multi-label Context","publication_year":2022,"publication_date":"2022-07-01","ids":{"openalex":"https://openalex.org/W4294659041","doi":"https://doi.org/10.18293/seke2022-012"},"language":"en","primary_location":{"id":"doi:10.18293/seke2022-012","is_oa":true,"landing_page_url":"https://doi.org/10.18293/seke2022-012","pdf_url":"https://doi.org/10.18293/seke2022-012","source":{"id":"https://openalex.org/S4220650826","display_name":"Proceedings/Proceedings of the ... International Conference on Software Engineering and Knowledge Engineering","issn_l":"2325-9000","issn":["2325-9000","2325-9086"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Conferences on Software Engineering and Knowledge Engineering","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://doi.org/10.18293/seke2022-012","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5077761339","display_name":"Yongxian Wei","orcid":"https://orcid.org/0009-0001-4356-7360"},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yongxian Wei","raw_affiliation_strings":["School of Computer Science and Engineering, Nanjing University of Science and Technology Nanjing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Nanjing University of Science and Technology Nanjing, China","institution_ids":["https://openalex.org/I36399199"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100443236","display_name":"Yong Ma","orcid":"https://orcid.org/0000-0002-4139-9711"},"institutions":[{"id":"https://openalex.org/I36399199","display_name":"Nanjing University of Science and Technology","ror":"https://ror.org/00xp9wg62","country_code":"CN","type":"education","lineage":["https://openalex.org/I36399199"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yong Ma","raw_affiliation_strings":["School of Computer Science and Engineering, Nanjing University of Science and Technology Nanjing, China"],"affiliations":[{"raw_affiliation_string":"School of Computer Science and Engineering, Nanjing University of Science and Technology Nanjing, China","institution_ids":["https://openalex.org/I36399199"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5100443236"],"corresponding_institution_ids":["https://openalex.org/I36399199"],"apc_list":null,"apc_paid":null,"fwci":0.2651,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.6114594,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":95,"max":96},"biblio":{"volume":"2022","issue":null,"first_page":"142","last_page":"146"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9994999766349792,"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"}},"topics":[{"id":"https://openalex.org/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9994999766349792,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.9991999864578247,"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":0.9897000193595886,"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":0.7886927127838135},{"id":"https://openalex.org/keywords/disjoint-sets","display_name":"Disjoint sets","score":0.6816928386688232},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.659650981426239},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.6520620584487915},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.6410430073738098},{"id":"https://openalex.org/keywords/confusion","display_name":"Confusion","score":0.5237479209899902},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.48493555188179016},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4838491976261139},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.4745762348175049},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.46871083974838257},{"id":"https://openalex.org/keywords/generator","display_name":"Generator (circuit theory)","score":0.45211753249168396},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.407749742269516},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11799708008766174}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7886927127838135},{"id":"https://openalex.org/C45340560","wikidata":"https://www.wikidata.org/wiki/Q215382","display_name":"Disjoint sets","level":2,"score":0.6816928386688232},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.659650981426239},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.6520620584487915},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.6410430073738098},{"id":"https://openalex.org/C2781140086","wikidata":"https://www.wikidata.org/wiki/Q557945","display_name":"Confusion","level":2,"score":0.5237479209899902},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.48493555188179016},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4838491976261139},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.4745762348175049},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.46871083974838257},{"id":"https://openalex.org/C2780992000","wikidata":"https://www.wikidata.org/wiki/Q17016113","display_name":"Generator (circuit theory)","level":3,"score":0.45211753249168396},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.407749742269516},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11799708008766174},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C11171543","wikidata":"https://www.wikidata.org/wiki/Q41630","display_name":"Psychoanalysis","level":1,"score":0.0},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0},{"id":"https://openalex.org/C114614502","wikidata":"https://www.wikidata.org/wiki/Q76592","display_name":"Combinatorics","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18293/seke2022-012","is_oa":true,"landing_page_url":"https://doi.org/10.18293/seke2022-012","pdf_url":"https://doi.org/10.18293/seke2022-012","source":{"id":"https://openalex.org/S4220650826","display_name":"Proceedings/Proceedings of the ... International Conference on Software Engineering and Knowledge Engineering","issn_l":"2325-9000","issn":["2325-9000","2325-9086"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Conferences on Software Engineering and Knowledge Engineering","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18293/seke2022-012","is_oa":true,"landing_page_url":"https://doi.org/10.18293/seke2022-012","pdf_url":"https://doi.org/10.18293/seke2022-012","source":{"id":"https://openalex.org/S4220650826","display_name":"Proceedings/Proceedings of the ... International Conference on Software Engineering and Knowledge Engineering","issn_l":"2325-9000","issn":["2325-9000","2325-9086"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"International Conferences on Software Engineering and Knowledge Engineering","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Reduced inequalities","id":"https://metadata.un.org/sdg/10","score":0.4000000059604645}],"awards":[],"funders":[],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4294659041.pdf","grobid_xml":"https://content.openalex.org/works/W4294659041.grobid-xml"},"referenced_works_count":23,"referenced_works":["https://openalex.org/W639708223","https://openalex.org/W1483870316","https://openalex.org/W2117539524","https://openalex.org/W2272331516","https://openalex.org/W2405223529","https://openalex.org/W2556967412","https://openalex.org/W2565639579","https://openalex.org/W2570343428","https://openalex.org/W2771617895","https://openalex.org/W2786802517","https://openalex.org/W2791890924","https://openalex.org/W2795108883","https://openalex.org/W2884585870","https://openalex.org/W2949846184","https://openalex.org/W2962941647","https://openalex.org/W2963037989","https://openalex.org/W2963854535","https://openalex.org/W2963936013","https://openalex.org/W2964094751","https://openalex.org/W2997795356","https://openalex.org/W2997998901","https://openalex.org/W3088820803","https://openalex.org/W3092529717"],"related_works":["https://openalex.org/W4256429076","https://openalex.org/W1971174658","https://openalex.org/W2099195351","https://openalex.org/W2964976023","https://openalex.org/W1982477181","https://openalex.org/W2403083015","https://openalex.org/W4285488523","https://openalex.org/W2011367623","https://openalex.org/W2734065904","https://openalex.org/W2570625548"],"abstract_inverted_index":{"Zero-shot":[0],"detection":[1,8,64],"(ZSD)":[2],",":[3],"the":[4,21,63,83,115,129],"problem":[5],"of":[6,20],"object":[7],"when":[9],"training":[10,18],"and":[11,36,69,105,119],"test":[12],"objects":[13,71],"are":[14,24,95],"disjoint,":[15],"i.e.":[16],"no":[17],"examples":[19],"target":[22],"classes":[23],"available.":[25],"ZSD":[26],"increasingly":[27],"gains":[28],"importance":[29],"for":[30,66],"large":[31],"scale":[32],"applications":[33],"because":[34],"collecting":[35],"labeling":[37],"sufficient":[38],"data":[39],"is":[40],"extremely":[41],"hard.":[42],"In":[43],"this":[44],"paper,":[45],"inspired":[46],"from":[47],"human":[48],"cognitive":[49],"experience,":[50],"we":[51],"propose":[52],"a":[53,78],"simple":[54],"but":[55],"effective":[56],"Multi-label":[57],"Context":[58],"(MLC)":[59],"framework":[60],"to":[61,87],"facilitate":[62],"ability":[65],"both":[67,102],"seen":[68],"unseen":[70,120],"by":[72,97],"mining":[73],"contextual":[74],"cues.":[75],"We":[76],"design":[77],"multilabel":[79],"classifier":[80],"which":[81],"leverages":[82],"holistic":[84],"image-level":[85],"context":[86,99],"learn":[88],"object-level":[89],"concepts.":[90],"Then,":[91],"novel":[92],"RoI":[93],"features":[94],"generated":[96],"exploiting":[98],"information":[100],"beneath":[101],"whole":[103],"images":[104],"interested":[106],"regions.":[107],"Moreover,":[108],"background":[109,118],"dynamic":[110],"generator":[111],"(BDG)":[112],"can":[113],"reduce":[114],"confusion":[116],"between":[117],"classes.":[121],"Our":[122],"extensive":[123],"experiments":[124],"show":[125],"that":[126],"MLC":[127],"outperforms":[128],"current":[130],"state-of-the-art":[131],"methods":[132],"on":[133],"MS-COCO.":[134]},"counts_by_year":[{"year":2025,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
