{"id":"https://openalex.org/W3159414615","doi":"https://doi.org/10.1145/3444685.3446314","title":"Multiplicative angular margin loss for text-based person search","display_name":"Multiplicative angular margin loss for text-based person search","publication_year":2021,"publication_date":"2021-03-07","ids":{"openalex":"https://openalex.org/W3159414615","doi":"https://doi.org/10.1145/3444685.3446314","mag":"3159414615"},"language":"en","primary_location":{"id":"doi:10.1145/3444685.3446314","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3444685.3446314","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2nd ACM International Conference on Multimedia in Asia","raw_type":"proceedings-article"},"type":"conference-paper","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/A5100364026","display_name":"Peng Zhang","orcid":"https://orcid.org/0000-0001-6794-7352"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Peng Zhang","raw_affiliation_strings":["University of Electronic Science and Technology of China, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5065413088","display_name":"Deqiang Ouyang","orcid":"https://orcid.org/0000-0003-2259-886X"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Deqiang Ouyang","raw_affiliation_strings":["University of Electronic Science and Technology of China, Chengdu, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China, Chengdu, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100643582","display_name":"Feiyu Chen","orcid":"https://orcid.org/0000-0002-0928-6899"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Feiyu Chen","raw_affiliation_strings":["University of Electronic Science and Technology of China, Chengdu, China and Sichuan Artificial Intelligence Research Institute, Yibin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China, Chengdu, China and Sichuan Artificial Intelligence Research Institute, Yibin, China","institution_ids":["https://openalex.org/I150229711"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5072350518","display_name":"Jie Shao","orcid":"https://orcid.org/0000-0003-2615-1555"},"institutions":[{"id":"https://openalex.org/I150229711","display_name":"University of Electronic Science and Technology of China","ror":"https://ror.org/04qr3zq92","country_code":"CN","type":"education","lineage":["https://openalex.org/I150229711"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jie Shao","raw_affiliation_strings":["University of Electronic Science and Technology of China, Chengdu, China and Sichuan Artificial Intelligence Research Institute, Yibin, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Electronic Science and Technology of China, Chengdu, China and Sichuan Artificial Intelligence Research Institute, Yibin, China","institution_ids":["https://openalex.org/I150229711"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I150229711"],"apc_list":null,"apc_paid":null,"fwci":0.2864,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.57894401,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"7"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","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/T10331","display_name":"Video Surveillance and Tracking Methods","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/T10812","display_name":"Human Pose and Action Recognition","score":0.9987999796867371,"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":0.9968000054359436,"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/softmax-function","display_name":"Softmax function","score":0.8302067518234253},{"id":"https://openalex.org/keywords/discriminative-model","display_name":"Discriminative model","score":0.7466014623641968},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6609861850738525},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6596953272819519},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6414783000946045},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5587643384933472},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.5389993190765381},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.532898485660553},{"id":"https://openalex.org/keywords/margin","display_name":"Margin (machine learning)","score":0.524286687374115},{"id":"https://openalex.org/keywords/multiplicative-function","display_name":"Multiplicative function","score":0.4899658262729645},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.4567048251628876},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.4343239963054657},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.42075228691101074},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.41243356466293335},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.2503747344017029},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.2434985637664795},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.20587888360023499}],"concepts":[{"id":"https://openalex.org/C188441871","wikidata":"https://www.wikidata.org/wiki/Q7554146","display_name":"Softmax function","level":3,"score":0.8302067518234253},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.7466014623641968},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6609861850738525},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6596953272819519},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6414783000946045},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5587643384933472},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.5389993190765381},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.532898485660553},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.524286687374115},{"id":"https://openalex.org/C42747912","wikidata":"https://www.wikidata.org/wiki/Q1048447","display_name":"Multiplicative function","level":2,"score":0.4899658262729645},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.4567048251628876},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.4343239963054657},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.42075228691101074},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.41243356466293335},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.2503747344017029},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2434985637664795},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.20587888360023499},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","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},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","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/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.0},{"id":"https://openalex.org/C126838900","wikidata":"https://www.wikidata.org/wiki/Q77604","display_name":"Radiology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3444685.3446314","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3444685.3446314","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 2nd ACM International Conference on Multimedia in Asia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Reduced inequalities","score":0.7599999904632568,"id":"https://metadata.un.org/sdg/10"}],"awards":[{"id":"https://openalex.org/G5175124256","display_name":null,"funder_award_id":"61832001, 61672133","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":19,"referenced_works":["https://openalex.org/W1933349210","https://openalex.org/W2096733369","https://openalex.org/W2138621090","https://openalex.org/W2517194566","https://openalex.org/W2604463754","https://openalex.org/W2784163702","https://openalex.org/W2788988323","https://openalex.org/W2801814609","https://openalex.org/W2883311563","https://openalex.org/W2885709146","https://openalex.org/W2963026686","https://openalex.org/W2963744743","https://openalex.org/W2981384185","https://openalex.org/W3029678209","https://openalex.org/W3099206234","https://openalex.org/W3099899804","https://openalex.org/W3103152812","https://openalex.org/W4247950230","https://openalex.org/W4299988922"],"related_works":["https://openalex.org/W3095152779","https://openalex.org/W3119773509","https://openalex.org/W3128220219","https://openalex.org/W2982889384","https://openalex.org/W4226227567","https://openalex.org/W2971218105","https://openalex.org/W4287113729","https://openalex.org/W3173314472","https://openalex.org/W3103152812","https://openalex.org/W156213964"],"abstract_inverted_index":{"Text-based":[0],"person":[1],"search":[2],"aims":[3],"at":[4,177],"retrieving":[5],"the":[6,41,69,84,114,158,168],"most":[7],"relevant":[8],"pedestrian":[9],"images":[10],"from":[11,46],"database":[12],"in":[13,18,68],"response":[14],"to":[15,58,100,124,130,144,148],"a":[16,35,65],"query":[17],"form":[19],"of":[20,44,170],"natural":[21],"language":[22],"description.":[23],"Existing":[24],"algorithms":[25],"mainly":[26],"focus":[27,131],"on":[28,133,157],"embedding":[29,71],"textual":[30,60],"and":[31,61,87,119],"visual":[32,62],"features":[33,45,63,80,104],"into":[34,64],"common":[36],"semantic":[37],"space":[38],"so":[39],"that":[40],"similarity":[42,140],"score":[43],"different":[47],"modalities":[48],"can":[49,76],"be":[50],"computed":[51],"directly.":[52],"Softmax":[53],"loss":[54,75,99,112,143],"is":[55,175],"widely":[56],"adopted":[57],"classify":[59,79],"correct":[66],"category":[67],"joint":[70],"space.":[72],"However,":[73],"softmax":[74],"only":[77],"help":[78],"but":[81],"not":[82],"increase":[83],"intra-class":[85],"compactness":[86],"inter-class":[88],"discrepancy.":[89],"To":[90],"this":[91],"end,":[92],"we":[93,137],"propose":[94,138],"multiplicative":[95,109],"angular":[96,110],"margin":[97,111],"(MAM)":[98],"learn":[101,125],"angularly":[102],"discriminative":[103,127],"for":[105],"each":[106],"identity.":[107],"The":[108,165],"penalizes":[113],"angle":[115],"between":[116],"feature":[117],"vector":[118,123],"its":[120],"corresponding":[121],"classifier":[122],"more":[126,132],"feature.":[128],"Moreover,":[129],"informative":[134,149],"image-text":[135],"pair,":[136],"pairwise":[139],"weighting":[141],"(PSW)":[142],"assign":[145],"higher":[146],"weight":[147],"pairs.":[150],"Extensive":[151],"experimental":[152],"evaluations":[153],"have":[154],"been":[155],"conducted":[156],"CUHK-PEDES":[159],"dataset":[160],"over":[161],"our":[162,171],"proposed":[163,172],"losses.":[164],"results":[166],"show":[167],"superiority":[169],"method.":[173],"Code":[174],"available":[176],"https://github.com/pengzhanguestc/MAM_loss.":[178]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1}],"updated_date":"2026-07-30T17:31:21.811387","created_date":"2025-10-10T00:00:00"}