{"id":"https://openalex.org/W7166125259","doi":"https://doi.org/10.48550/arxiv.2606.26455","title":"Active Adversarial Perturbation-driven Associative Memory Retrieval for RGB-Event Visual Object Tracking","display_name":"Active Adversarial Perturbation-driven Associative Memory Retrieval for RGB-Event Visual Object Tracking","publication_year":2026,"publication_date":"2026-06-24","ids":{"openalex":"https://openalex.org/W7166125259","doi":"https://doi.org/10.48550/arxiv.2606.26455"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2606.26455","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.26455","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2606.26455","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5139423250","display_name":"Xiao Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Xiao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5126117003","display_name":"Xufeng Lou","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lou, Xufeng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5113325374","display_name":"Zikang Yan","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Yan, Zikang","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139463428","display_name":"Lan Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Lan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135101022","display_name":"Sibao Chen","orcid":"https://orcid.org/0000-0003-1481-0162"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Sibao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5139429586","display_name":"Yaowei Wang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yaowei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100600477","display_name":"Yi Tian","orcid":"https://orcid.org/0000-0002-0831-6275"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tian, Yonghong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5139428096","display_name":"Jin Tang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tang, Jin","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"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":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.5088000297546387,"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":0.5088000297546387,"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":0.16249999403953552,"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/T10502","display_name":"Advanced Memory and Neural Computing","score":0.03629999980330467,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/robustness","display_name":"Robustness (evolution)","score":0.5299999713897705},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4108999967575073},{"id":"https://openalex.org/keywords/rgb-color-model","display_name":"RGB color model","score":0.38370001316070557},{"id":"https://openalex.org/keywords/search-engine-indexing","display_name":"Search engine indexing","score":0.35359999537467957},{"id":"https://openalex.org/keywords/feature-vector","display_name":"Feature vector","score":0.35109999775886536},{"id":"https://openalex.org/keywords/optical-flow","display_name":"Optical flow","score":0.34630000591278076},{"id":"https://openalex.org/keywords/visualization","display_name":"Visualization","score":0.33899998664855957},{"id":"https://openalex.org/keywords/traffic-sign-recognition","display_name":"Traffic sign recognition","score":0.3386000096797943},{"id":"https://openalex.org/keywords/landmark","display_name":"Landmark","score":0.33149999380111694}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7574999928474426},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6825000047683716},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.5376999974250793},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.5299999713897705},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4108999967575073},{"id":"https://openalex.org/C82990744","wikidata":"https://www.wikidata.org/wiki/Q166194","display_name":"RGB color model","level":2,"score":0.38370001316070557},{"id":"https://openalex.org/C75165309","wikidata":"https://www.wikidata.org/wiki/Q2258979","display_name":"Search engine indexing","level":2,"score":0.35359999537467957},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.35109999775886536},{"id":"https://openalex.org/C155542232","wikidata":"https://www.wikidata.org/wiki/Q736111","display_name":"Optical flow","level":3,"score":0.34630000591278076},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.33899998664855957},{"id":"https://openalex.org/C6528762","wikidata":"https://www.wikidata.org/wiki/Q1574298","display_name":"Traffic sign recognition","level":4,"score":0.3386000096797943},{"id":"https://openalex.org/C2780297707","wikidata":"https://www.wikidata.org/wiki/Q4895393","display_name":"Landmark","level":2,"score":0.33149999380111694},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.32820001244544983},{"id":"https://openalex.org/C56461940","wikidata":"https://www.wikidata.org/wiki/Q970687","display_name":"Eye tracking","level":2,"score":0.32170000672340393},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.3206999897956848},{"id":"https://openalex.org/C1667742","wikidata":"https://www.wikidata.org/wiki/Q10927554","display_name":"Image retrieval","level":3,"score":0.29739999771118164},{"id":"https://openalex.org/C202474056","wikidata":"https://www.wikidata.org/wiki/Q1931635","display_name":"Video tracking","level":3,"score":0.29339998960494995},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.2865999937057495},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.28360000252723694},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.27549999952316284},{"id":"https://openalex.org/C34388435","wikidata":"https://www.wikidata.org/wiki/Q2267362","display_name":"Bounded function","level":2,"score":0.274399995803833},{"id":"https://openalex.org/C55282118","wikidata":"https://www.wikidata.org/wiki/Q252683","display_name":"Snapshot (computer storage)","level":2,"score":0.26739999651908875},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.2644999921321869},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.26030001044273376},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.2558000087738037},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.2549000084400177}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2606.26455","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.26455","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2606.26455","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2606.26455","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.8115584254264832,"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"RGB-Event":[0,73,205],"tracking":[1,23,74],"improves":[2],"localization":[3],"robustness":[4,76],"by":[5,137],"fusing":[6],"RGB":[7],"appearance":[8],"textures":[9],"and":[10,46,56,68,81,103,111,165,168,193,212],"dense":[11],"temporal":[12],"motion":[13],"cues":[14],"from":[15],"event":[16],"sensors.":[17],"While":[18],"this":[19],"multi-modal":[20,35],"scheme":[21],"broadens":[22],"applicability,":[24],"real-world":[25,88],"scenes":[26],"suffer":[27],"diverse":[28],"structured":[29,93],"signal":[30,89],"degradations":[31],"that":[32],"hinder":[33],"traditional":[34],"fusion.":[36],"In":[37],"harsh":[38],"environments,":[39],"either":[40],"modality":[41,102],"can":[42],"lose":[43],"reliability":[44],"drastically,":[45],"targets":[47],"frequently":[48],"appear":[49],"incomplete":[50],"due":[51],"to":[52,122,175,184],"occlusion,":[53],"edge":[54],"truncation":[55],"foreground":[57],"clutter.To":[58],"tackle":[59],"the":[60,101,124,128,170,197,204],"above":[61],"challenges,":[62],"we":[63,142],"present":[64],"a":[65],"hierarchical":[66,117],"perturbation":[67,98,130],"retrieval":[69,157,171],"framework":[70],"tailored":[71],"for":[72,149,203],"with":[75],"against":[77],"partial":[78],"target":[79,113,185],"missing":[80],"modal":[82],"degradation,":[83],"termed":[84],"APRTrack.":[85],"To":[86],"mimic":[87],"corruption,":[90],"APRTrack":[91],"constructs":[92],"degradation":[94,139],"via":[95],"two":[96,129],"adversarial":[97],"branches":[99],"at":[100],"spatial":[104],"levels,":[105],"which":[106],"separately":[107],"simulate":[108],"full-modal":[109],"failure":[110],"localized":[112],"region":[114],"absence.":[115],"A":[116],"routing":[118],"mechanism":[119],"is":[120],"designed":[121],"disentangle":[123],"training":[125],"pipelines":[126],"of":[127,199],"types,":[131],"effectively":[132],"eliminating":[133],"feature":[134,181],"collapse":[135],"induced":[136],"superimposed":[138],"constraints.":[140],"Furthermore,":[141],"devise":[143],"Footprint-guided":[144],"Channel-calibrated":[145],"Hopfield":[146,176],"Retrieval":[147],"(FCHR)":[148],"reliable":[150],"historical":[151,180],"information":[152],"compensation.":[153],"This":[154],"module":[155],"evaluates":[156],"confidence":[158],"based":[159],"on":[160,189,218],"association":[161],"footprints":[162],"between":[163],"queries":[164],"memory":[166],"banks,":[167],"calibrates":[169],"metric":[172],"space":[173],"prior":[174],"matching,":[177],"realizing":[178],"controllable":[179],"compensation":[182],"bounded":[183],"regions.":[186],"Extensive":[187],"experiments":[188],"FE108,":[190],"COESOT,":[191],"VisEvent,":[192],"FELT":[194],"datasets":[195],"demonstrate":[196],"effectiveness":[198],"our":[200],"proposed":[201],"strategies":[202],"visual":[206],"object":[207],"tracking.":[208],"The":[209],"source":[210],"code":[211],"pre-trained":[213],"models":[214],"will":[215],"be":[216],"released":[217],"https://github.com/Event-AHU/OpenEvTracking":[219]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-27T00:00:00"}