{"id":"https://openalex.org/W7126114712","doi":"https://doi.org/10.1109/icfsp67350.2025.11353712","title":"Cross-Frame Semantic Alignment Network: Towards Zero-Shot Video Action Recognition","display_name":"Cross-Frame Semantic Alignment Network: Towards Zero-Shot Video Action Recognition","publication_year":2025,"publication_date":"2025-09-10","ids":{"openalex":"https://openalex.org/W7126114712","doi":"https://doi.org/10.1109/icfsp67350.2025.11353712"},"language":null,"primary_location":{"id":"doi:10.1109/icfsp67350.2025.11353712","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icfsp67350.2025.11353712","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 10th International Conference on Frontiers of Signal Processing (ICFSP)","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/A5003223361","display_name":"Yue Huang","orcid":"https://orcid.org/0009-0006-9282-2125"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yue Huang","raw_affiliation_strings":["Fudan University,Department of Electronic Engineering,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fudan University,Department of Electronic Engineering,China","institution_ids":["https://openalex.org/I24943067"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101893294","display_name":"Xiaodong Gu","orcid":"https://orcid.org/0000-0003-2515-8050"},"institutions":[{"id":"https://openalex.org/I24943067","display_name":"Fudan University","ror":"https://ror.org/013q1eq08","country_code":"CN","type":"education","lineage":["https://openalex.org/I24943067"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaodong Gu","raw_affiliation_strings":["Fudan University,Department of Electronic Engineering,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Fudan University,Department of Electronic Engineering,China","institution_ids":["https://openalex.org/I24943067"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I24943067"],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"11","last_page":"15"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10812","display_name":"Human Pose and Action Recognition","score":0.8995000123977661,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.8995000123977661,"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":0.0560000017285347,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.007000000216066837,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.5216000080108643},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.5070000290870667},{"id":"https://openalex.org/keywords/action-recognition","display_name":"Action recognition","score":0.48919999599456787},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.46860000491142273},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.42329999804496765},{"id":"https://openalex.org/keywords/inter-frame","display_name":"Inter frame","score":0.41190001368522644},{"id":"https://openalex.org/keywords/action","display_name":"Action (physics)","score":0.3603000044822693},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.357699990272522}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7964000105857849},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6226999759674072},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.5216000080108643},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.5070000290870667},{"id":"https://openalex.org/C2987834672","wikidata":"https://www.wikidata.org/wiki/Q4677630","display_name":"Action recognition","level":3,"score":0.48919999599456787},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.46860000491142273},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.42329999804496765},{"id":"https://openalex.org/C39394851","wikidata":"https://www.wikidata.org/wiki/Q921594","display_name":"Inter frame","level":4,"score":0.41190001368522644},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.3603000044822693},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.357699990272522},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.33799999952316284},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.33320000767707825},{"id":"https://openalex.org/C2778180026","wikidata":"https://www.wikidata.org/wiki/Q18378163","display_name":"Semantic heterogeneity","level":4,"score":0.33320000767707825},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.30090001225471497},{"id":"https://openalex.org/C2775955345","wikidata":"https://www.wikidata.org/wiki/Q7449071","display_name":"Semantic mapping","level":2,"score":0.30079999566078186},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.290800005197525},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2831999957561493},{"id":"https://openalex.org/C90312973","wikidata":"https://www.wikidata.org/wiki/Q7449052","display_name":"Semantic data model","level":2,"score":0.27639999985694885},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.27549999952316284},{"id":"https://openalex.org/C197914299","wikidata":"https://www.wikidata.org/wiki/Q18650","display_name":"Semantic memory","level":3,"score":0.2655999958515167},{"id":"https://openalex.org/C198942812","wikidata":"https://www.wikidata.org/wiki/Q496618","display_name":"Semantic property","level":2,"score":0.2655999958515167},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.2563999891281128},{"id":"https://openalex.org/C130318100","wikidata":"https://www.wikidata.org/wiki/Q2268914","display_name":"Semantic similarity","level":2,"score":0.25609999895095825},{"id":"https://openalex.org/C86034646","wikidata":"https://www.wikidata.org/wiki/Q474311","display_name":"Semantic gap","level":4,"score":0.2542000114917755}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icfsp67350.2025.11353712","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icfsp67350.2025.11353712","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 10th International Conference on Frontiers of Signal Processing (ICFSP)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"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":13,"referenced_works":["https://openalex.org/W1522734439","https://openalex.org/W2126579184","https://openalex.org/W2507009361","https://openalex.org/W2963524571","https://openalex.org/W2963689837","https://openalex.org/W2963731876","https://openalex.org/W2990503944","https://openalex.org/W4285606530","https://openalex.org/W4304014690","https://openalex.org/W4312254032","https://openalex.org/W4382467086","https://openalex.org/W4386065852","https://openalex.org/W4387967959"],"related_works":[],"abstract_inverted_index":{"Recent":[0],"advances":[1],"in":[2],"vision-language":[3],"models":[4,71],"(VLMs)":[5],"like":[6],"CLIP":[7],"have":[8],"revolutionized":[9],"zero-shot":[10,24,149],"image":[11],"understanding":[12],"by":[13,43,52,94],"aligning":[14],"visual":[15],"and":[16,76,119,156,159],"textual":[17],"semantics.":[18],"However,":[19],"extending":[20],"this":[21,57],"success":[22],"to":[23,31,89,113,125,134],"video":[25,150],"action":[26,74,131,151],"recognition":[27,152],"remains":[28],"challenging":[29],"due":[30],"the":[32,104,136,143,164],"inherent":[33],"temporal":[34],"complexity":[35],"of":[36,139],"videos.":[37],"Critical":[38],"actions":[39],"are":[40,50],"often":[41],"diluted":[42],"redundant":[44],"frames,":[45],"while":[46],"subtle":[47],"interframe":[48],"variations":[49],"overlooked":[51],"global":[53,77,144],"pooling-based":[54],"methods.":[55],"In":[56,102],"paper,":[58],"we":[59],"propose":[60],"Cross-Frame":[61],"Semantic":[62,86,106],"Alignment":[63,87],"Network":[64],"(CFSAN),":[65],"a":[66,82,120,148],"novel":[67],"framework":[68],"that":[69,163],"jointly":[70],"local":[72],"fine-grained":[73],"dynamics":[75],"semantic":[78,92,97,117],"consistency.":[79],"We":[80,146],"develop":[81],"Local":[83],"Adjacent":[84],"Frame":[85],"Module":[88,110],"capture":[90],"inter-frame":[91],"changes":[93],"aggregating":[95],"multi-scale":[96],"differences":[98],"between":[99],"adjacent":[100],"frames.":[101],"addition,":[103],"Global":[105],"Aware":[107],"Spectral":[108],"Cluster":[109],"is":[111,123],"designed":[112],"divide":[114],"frames":[115,141],"into":[116],"clusters,":[118],"gate":[121],"mechanism":[122],"employed":[124],"filter":[126],"noise,":[127],"which":[128],"retains":[129],"only":[130],"key":[132],"segments,":[133],"reduce":[135],"dilution":[137],"effect":[138],"irrelevant":[140],"on":[142,154],"pool.":[145],"conduct":[147],"evaluation":[153],"HMDB51":[155],"UCF101":[157],"datasets":[158],"experimental":[160],"results":[161],"show":[162],"CFSAN":[165],"model":[166],"achieves":[167],"competitive":[168],"results.":[169]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-01-30T00:00:00"}