{"id":"https://openalex.org/W4415708572","doi":"https://doi.org/10.1109/icme59968.2025.11209020","title":"VADMamba: Exploring State Space Models for Fast Video Anomaly Detection","display_name":"VADMamba: Exploring State Space Models for Fast Video Anomaly Detection","publication_year":2025,"publication_date":"2025-06-30","ids":{"openalex":"https://openalex.org/W4415708572","doi":"https://doi.org/10.1109/icme59968.2025.11209020"},"language":null,"primary_location":{"id":"doi:10.1109/icme59968.2025.11209020","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme59968.2025.11209020","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Multimedia and Expo (ICME)","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/A5077828258","display_name":"Jiahao Lyu","orcid":"https://orcid.org/0000-0003-2051-8045"},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Jiahao Lyu","raw_affiliation_strings":["Xi&#x2019;an University of Technology,School of Computer Science and Engineering,Xi&#x2019;an,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xi&#x2019;an University of Technology,School of Computer Science and Engineering,Xi&#x2019;an,China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101641270","display_name":"Minghua Zhao","orcid":"https://orcid.org/0000-0001-9732-4869"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Minghua Zhao","raw_affiliation_strings":["Xi&#x2019;an University of Technology,School of Computer Science and Engineering,Xi&#x2019;an,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xi&#x2019;an University of Technology,School of Computer Science and Engineering,Xi&#x2019;an,China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5116094489","display_name":"Jing Hu","orcid":"https://orcid.org/0000-0001-5586-9609"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jing Hu","raw_affiliation_strings":["Xi&#x2019;an University of Technology,School of Computer Science and Engineering,Xi&#x2019;an,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xi&#x2019;an University of Technology,School of Computer Science and Engineering,Xi&#x2019;an,China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103094961","display_name":"Xuewen Huang","orcid":"https://orcid.org/0000-0003-1169-424X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xuewen Huang","raw_affiliation_strings":["Xi&#x2019;an University of Technology,School of Computer Science and Engineering,Xi&#x2019;an,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xi&#x2019;an University of Technology,School of Computer Science and Engineering,Xi&#x2019;an,China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5022792986","display_name":"Yifei Chen","orcid":"https://orcid.org/0009-0001-6625-5765"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yifei Chen","raw_affiliation_strings":["Xi&#x2019;an University of Technology,School of Computer Science and Engineering,Xi&#x2019;an,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xi&#x2019;an University of Technology,School of Computer Science and Engineering,Xi&#x2019;an,China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010696821","display_name":"Shuangli Du","orcid":"https://orcid.org/0000-0002-8897-0778"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shuangli Du","raw_affiliation_strings":["Xi&#x2019;an University of Technology,School of Computer Science and Engineering,Xi&#x2019;an,China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Xi&#x2019;an University of Technology,School of Computer Science and Engineering,Xi&#x2019;an,China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5077828258"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":8.3806,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.97403588,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.980400025844574,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.980400025844574,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.006200000178068876,"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/T11689","display_name":"Adversarial Robustness in Machine Learning","score":0.002899999963119626,"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/anomaly-detection","display_name":"Anomaly detection","score":0.7145000100135803},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.7092000246047974},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.5781000256538391},{"id":"https://openalex.org/keywords/optical-flow","display_name":"Optical flow","score":0.48820000886917114},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.4855000078678131},{"id":"https://openalex.org/keywords/quantization","display_name":"Quantization (signal processing)","score":0.39250001311302185},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.38659998774528503},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.367000013589859}],"concepts":[{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.7145000100135803},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.7092000246047974},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6988000273704529},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.63919997215271},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5781000256538391},{"id":"https://openalex.org/C155542232","wikidata":"https://www.wikidata.org/wiki/Q736111","display_name":"Optical flow","level":3,"score":0.48820000886917114},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.4855000078678131},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.44589999318122864},{"id":"https://openalex.org/C28855332","wikidata":"https://www.wikidata.org/wiki/Q198099","display_name":"Quantization (signal processing)","level":2,"score":0.39250001311302185},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.38659998774528503},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.367000013589859},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3662000000476837},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3538999855518341},{"id":"https://openalex.org/C199833920","wikidata":"https://www.wikidata.org/wiki/Q612536","display_name":"Vector quantization","level":2,"score":0.34880000352859497},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.34389999508857727},{"id":"https://openalex.org/C72434380","wikidata":"https://www.wikidata.org/wiki/Q230930","display_name":"State space","level":2,"score":0.296099990606308},{"id":"https://openalex.org/C48103436","wikidata":"https://www.wikidata.org/wiki/Q599031","display_name":"State (computer science)","level":2,"score":0.28850001096725464},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.2851000130176544},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.2800999879837036},{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.27480000257492065},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.27399998903274536},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.26919999718666077}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icme59968.2025.11209020","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme59968.2025.11209020","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on Multimedia and Expo (ICME)","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"},{"id":"https://openalex.org/F4320324173","display_name":"Natural Science Foundation of Shaanxi Province","ror":null}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":29,"referenced_works":["https://openalex.org/W1931450083","https://openalex.org/W2163612318","https://openalex.org/W2302255633","https://openalex.org/W2341058432","https://openalex.org/W2560474170","https://openalex.org/W2777342313","https://openalex.org/W2889915804","https://openalex.org/W2963610939","https://openalex.org/W2981650061","https://openalex.org/W2987228832","https://openalex.org/W3035240825","https://openalex.org/W3081430643","https://openalex.org/W3138516171","https://openalex.org/W4293317991","https://openalex.org/W4293518753","https://openalex.org/W4293519096","https://openalex.org/W4316259280","https://openalex.org/W4321232185","https://openalex.org/W4372259841","https://openalex.org/W4372342566","https://openalex.org/W4386076578","https://openalex.org/W4387757641","https://openalex.org/W4390848180","https://openalex.org/W4391256162","https://openalex.org/W4391614403","https://openalex.org/W4395044614","https://openalex.org/W4396669743","https://openalex.org/W4411554026","https://openalex.org/W4414243662"],"related_works":[],"abstract_inverted_index":{"Video":[0],"anomaly":[1],"detection":[2,17],"(VAD)":[3],"methods":[4],"are":[5],"mostly":[6],"CNN-based":[7],"or":[8],"Transformer-based,":[9],"achieving":[10],"impressive":[11],"results,":[12],"but":[13],"the":[14,22,38,50,59,83,128,131],"focus":[15],"on":[16,70],"accuracy":[18,118],"often":[19],"comes":[20],"at":[21,151],"expense":[23],"of":[24,29,61,130],"inference":[25,142],"speed.":[26],"The":[27],"emergence":[28],"state":[30],"space":[31],"models":[32],"in":[33,141],"computer":[34],"vision,":[35],"exemplified":[36],"by":[37],"Mamba":[39,62],"model,":[40],"demonstrates":[41],"improved":[42],"computational":[43],"efficiency":[44],"through":[45,119],"selective":[46],"scans":[47],"and":[48,76,95,111],"showcases":[49],"great":[51],"potential":[52],"for":[53,73],"long-range":[54],"modeling.":[55],"Our":[56],"study":[57],"pioneers":[58],"application":[60],"to":[63,145],"VAD,":[64],"dubbed":[65],"VADMamba,":[66],"which":[67,88],"is":[68,149],"based":[69],"multi-task":[71],"learning":[72],"frame":[74],"prediction":[75],"optical":[77,114],"flow":[78],"reconstruction.":[79],"Specifically,":[80],"we":[81],"propose":[82],"VQ-Mamba":[84],"Unet":[85],"(VQ-MaU)":[86],"framework,":[87],"incorporates":[89],"a":[90,120],"Vector":[91],"Quantization":[92],"(VQ)":[93],"layer":[94],"Mamba-based":[96],"Non-negative":[97],"Visual":[98],"State":[99],"Space":[100],"(NVSS)":[101],"block.":[102],"Furthermore,":[103],"two":[104],"individual":[105],"VQ-MaU":[106],"networks":[107],"separately":[108],"predict":[109],"frames":[110],"reconstruct":[112],"corresponding":[113],"flows,":[115],"further":[116],"boosting":[117],"clip-level":[121],"fusion":[122],"evaluation":[123],"strategy.":[124],"Experimental":[125],"results":[126],"validate":[127],"efficacy":[129],"proposed":[132],"VADMamba":[133],"across":[134],"three":[135],"benchmark":[136],"datasets,":[137],"demonstrating":[138],"superior":[139],"performance":[140],"speed":[143],"compared":[144],"previous":[146],"work.":[147],"Code":[148],"available":[150],"https://github.com/jLooo/VADMamba.":[152]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":3}],"updated_date":"2026-05-05T08:41:31.759640","created_date":"2025-10-30T00:00:00"}
