{"id":"https://openalex.org/W7136141188","doi":"https://doi.org/10.1109/itsc60802.2025.11423248","title":"MS-VLMDet: Multi-Scale Feature Enhanced Vision-Language Model for Pedestrian Detection","display_name":"MS-VLMDet: Multi-Scale Feature Enhanced Vision-Language Model for Pedestrian Detection","publication_year":2025,"publication_date":"2025-11-18","ids":{"openalex":"https://openalex.org/W7136141188","doi":"https://doi.org/10.1109/itsc60802.2025.11423248"},"language":null,"primary_location":{"id":"doi:10.1109/itsc60802.2025.11423248","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc60802.2025.11423248","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC)","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/A5104310463","display_name":"Zekai Dai","orcid":null},"institutions":[{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"government","lineage":["https://openalex.org/I19820366"]},{"id":"https://openalex.org/I4210112150","display_name":"Institute of Automation","ror":"https://ror.org/022c3hy66","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Zekai Dai","raw_affiliation_strings":["Institute of Automation, Chinese Academy of Sciences,State Key Laboratory of Multimodal Artificial Intelligence Systems,Beijing,China,100190"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Automation, Chinese Academy of Sciences,State Key Laboratory of Multimodal Artificial Intelligence Systems,Beijing,China,100190","institution_ids":["https://openalex.org/I4210112150","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057269411","display_name":"Xingyuan Dai","orcid":"https://orcid.org/0000-0001-7517-5049"},"institutions":[{"id":"https://openalex.org/I4210100255","display_name":"Beijing Academy of Artificial Intelligence","ror":"https://ror.org/016a74861","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210100255"]},{"id":"https://openalex.org/I4391012619","display_name":"Shanghai Artificial Intelligence Laboratory","ror":"https://ror.org/03wkvpx79","country_code":null,"type":"facility","lineage":["https://openalex.org/I4391012619"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xingyuan Dai","raw_affiliation_strings":["Shanghai Artificial Intelligence Laboratory,Shanghai,China,200232"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Shanghai Artificial Intelligence Laboratory,Shanghai,China,200232","institution_ids":["https://openalex.org/I4210100255","https://openalex.org/I4391012619"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112239296","display_name":"Yisheng L.V.","orcid":null},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yisheng Lv","raw_affiliation_strings":["School of Artificial Intelligence, University of Chinese Academy of Sciences,Beijing,China,100049"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, University of Chinese Academy of Sciences,Beijing,China,100049","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129490095","display_name":"Xin Pei","orcid":null},"institutions":[{"id":"https://openalex.org/I4210165038","display_name":"University of Chinese Academy of Sciences","ror":"https://ror.org/05qbk4x57","country_code":"CN","type":"education","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210165038"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xin Pei","raw_affiliation_strings":["School of Artificial Intelligence, University of Chinese Academy of Sciences,Beijing,China,100049"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Artificial Intelligence, University of Chinese Academy of Sciences,Beijing,China,100049","institution_ids":["https://openalex.org/I4210165038"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129589716","display_name":"Xu Wang","orcid":null},"institutions":[{"id":"https://openalex.org/I99065089","display_name":"Tsinghua University","ror":"https://ror.org/03cve4549","country_code":"CN","type":"education","lineage":["https://openalex.org/I99065089"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xu Wang","raw_affiliation_strings":["BNRist, Tsinghua University,Department of Automation,Beijing,China,100084"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"BNRist, Tsinghua University,Department of Automation,Beijing,China,100084","institution_ids":["https://openalex.org/I99065089"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5129397720","display_name":"Xiaoyan Gong","orcid":null},"institutions":[{"id":"https://openalex.org/I4210141776","display_name":"China XD Group (China)","ror":"https://ror.org/04ceqst84","country_code":"CN","type":"company","lineage":["https://openalex.org/I4210141776"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoyan Gong","raw_affiliation_strings":["Xiong&#x0027;an Institute of Innovation,Hebei Key Laboratory of Cognitive Intelligence,Xiong&#x0027;an New Area,China,071702"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xiong&#x0027;an Institute of Innovation,Hebei Key Laboratory of Cognitive Intelligence,Xiong&#x0027;an New Area,China,071702","institution_ids":["https://openalex.org/I4210141776"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100389396","display_name":"Yuliang Liu","orcid":"https://orcid.org/0000-0002-3037-173X"},"institutions":[{"id":"https://openalex.org/I2250955327","display_name":"Huawei Technologies (China)","ror":"https://ror.org/00cmhce21","country_code":"CN","type":"company","lineage":["https://openalex.org/I2250955327"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yu-Liang Liu","raw_affiliation_strings":["HUAWEI AMS Data and AI Team"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"HUAWEI AMS Data and AI Team","institution_ids":["https://openalex.org/I2250955327"]}]},{"author_position":"last","author":{"id":null,"display_name":"Wuling Huang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210100255","display_name":"Beijing Academy of Artificial Intelligence","ror":"https://ror.org/016a74861","country_code":"CN","type":"other","lineage":["https://openalex.org/I4210100255"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wuling Huang","raw_affiliation_strings":["Institute of Artificial Intelligence for Industries, Chinese Academy of Sciences,Nanjing,China,211135"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Institute of Artificial Intelligence for Industries, Chinese Academy of Sciences,Nanjing,China,211135","institution_ids":["https://openalex.org/I4210100255"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":8,"corresponding_author_ids":["https://openalex.org/A5104310463"],"corresponding_institution_ids":["https://openalex.org/I19820366","https://openalex.org/I4210112150"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.70975398,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"151","last_page":"156"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.7010999917984009,"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":0.7010999917984009,"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":0.1193000003695488,"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/T11099","display_name":"Autonomous Vehicle Technology and Safety","score":0.03920000046491623,"subfield":{"id":"https://openalex.org/subfields/2203","display_name":"Automotive Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4837000072002411},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4025999903678894},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.3125999867916107},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.30329999327659607},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.29420000314712524}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6320000290870667},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5637999773025513},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5410000085830688},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4837000072002411},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4025999903678894},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.3125999867916107},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.30329999327659607},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.29420000314712524},{"id":"https://openalex.org/C2780156472","wikidata":"https://www.wikidata.org/wiki/Q2355550","display_name":"Pedestrian detection","level":3,"score":0.29179999232292175},{"id":"https://openalex.org/C2777113093","wikidata":"https://www.wikidata.org/wiki/Q221488","display_name":"Pedestrian","level":2,"score":0.27970001101493835}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/itsc60802.2025.11423248","is_oa":false,"landing_page_url":"https://doi.org/10.1109/itsc60802.2025.11423248","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE 28th International Conference on Intelligent Transportation Systems (ITSC)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Sustainable cities and communities","score":0.43237751722335815,"id":"https://metadata.un.org/sdg/11"}],"awards":[{"id":"https://openalex.org/G1584437437","display_name":null,"funder_award_id":"2022ZD0162200,62303462,E3H0012D01","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":30,"referenced_works":["https://openalex.org/W1773149199","https://openalex.org/W2115579991","https://openalex.org/W2161969291","https://openalex.org/W2497039038","https://openalex.org/W2565639579","https://openalex.org/W2594507094","https://openalex.org/W2896726780","https://openalex.org/W2962811161","https://openalex.org/W3094502228","https://openalex.org/W3096609285","https://openalex.org/W3106914721","https://openalex.org/W3159619744","https://openalex.org/W4312939270","https://openalex.org/W4312956471","https://openalex.org/W4382568144","https://openalex.org/W4385569741","https://openalex.org/W4386558753","https://openalex.org/W4396753519","https://openalex.org/W4398184793","https://openalex.org/W4402713111","https://openalex.org/W4402727764","https://openalex.org/W4402774413","https://openalex.org/W4403990523","https://openalex.org/W4404612908","https://openalex.org/W4405140008","https://openalex.org/W4409061141","https://openalex.org/W4409361147","https://openalex.org/W7133184875","https://openalex.org/W7133193597","https://openalex.org/W7133227958"],"related_works":[],"abstract_inverted_index":{"Pedestrian":[0,45],"detection,":[1],"as":[2,21],"a":[3,57,80,92,109],"critical":[4],"component":[5],"of":[6,31,155],"Intelligent":[7],"Transportation":[8],"Systems,":[9],"encounters":[10],"numerous":[11],"challenges.":[12],"Pedestrians":[13],"are":[14],"often":[15],"situated":[16],"against":[17],"complex":[18,147],"backgrounds,":[19],"appear":[20],"small":[22,73],"targets":[23],"in":[24,70,141],"images,":[25],"and":[26,105,108,138],"adopt":[27],"diverse":[28],"poses,":[29],"all":[30],"which":[32,47],"make":[33],"detection":[34,144],"challenging.":[35],"This":[36],"research":[37],"proposes":[38],"MS-VLMDet":[39,75,133],"(Multi-Scale":[40],"Feature-Enhanced":[41],"Vision-Language":[42],"Model":[43],"for":[44],"Detection),":[46],"fuses":[48],"multi-scale":[49,81],"features":[50,88,100,120],"by":[51],"integrating":[52],"multi-level":[53],"visual":[54],"representations":[55],"from":[56],"Feature":[58],"Pyramid":[59],"Network":[60],"into":[61],"large":[62],"vision-language":[63,112,139],"models":[64,140],"to":[65,121],"overcome":[66],"the":[67,98,102,118,123,159,164],"models'":[68],"limitations":[69],"precisely":[71],"localizing":[72],"objects.":[74],"contains":[76],"three":[77],"key":[78],"modules:":[79],"feature":[82,93,110],"extraction":[83],"module":[84,95,115],"that":[85,96,116,132],"captures":[86],"pedestrian":[87,127,143],"at":[89],"different":[90],"resolutions;":[91],"fusion":[94],"integrates":[97],"extracted":[99],"with":[101],"original":[103,160],"image":[104],"text":[106],"prompts;":[107],"enhanced":[111],"model":[113,162],"inference":[114],"uses":[117],"fused":[119],"guide":[122],"model's":[124],"attention":[125],"toward":[126],"regions.":[128],"Experimental":[129],"results":[130],"demonstrate":[131],"outperforms":[134],"existing":[135],"deep":[136],"learning":[137],"small-target":[142],"across":[145],"various":[146],"traffic":[148],"scenarios,":[149],"achieving":[150],"an":[151],"F1":[152],"score":[153],"improvement":[154],"4.6":[156],"times":[157],"over":[158],"Qwen2.5-VL":[161],"on":[163],"CityPersons":[165],"dataset.":[166]},"counts_by_year":[],"updated_date":"2026-05-05T08:41:31.759640","created_date":"2026-03-17T00:00:00"}
