{"id":"https://openalex.org/W7160347864","doi":"https://doi.org/10.1109/wacv61042.2026.00604","title":"Denoise, Divide, Distill, and Predict D <sup>3</sup> P: Towards Forecasting Long-horizon Real-world Anomaly from Normalcy","display_name":"Denoise, Divide, Distill, and Predict D <sup>3</sup> P: Towards Forecasting Long-horizon Real-world Anomaly from Normalcy","publication_year":2026,"publication_date":"2026-03-06","ids":{"openalex":"https://openalex.org/W7160347864","doi":"https://doi.org/10.1109/wacv61042.2026.00604"},"language":null,"primary_location":{"id":"doi:10.1109/wacv61042.2026.00604","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv61042.2026.00604","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","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/A5092905500","display_name":"Quentin Merilleau","orcid":null},"institutions":[{"id":"https://openalex.org/I1326498283","display_name":"Institut national de recherche en sciences et technologies du num\u00e9rique","ror":"https://ror.org/02kvxyf05","country_code":"FR","type":"government","lineage":["https://openalex.org/I1326498283"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Quentin M\u00e9rilleau","raw_affiliation_strings":["INRIA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"INRIA","institution_ids":["https://openalex.org/I1326498283"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5015650149","display_name":"Snehashis Majhi","orcid":"https://orcid.org/0000-0002-9101-017X"},"institutions":[{"id":"https://openalex.org/I1326498283","display_name":"Institut national de recherche en sciences et technologies du num\u00e9rique","ror":"https://ror.org/02kvxyf05","country_code":"FR","type":"government","lineage":["https://openalex.org/I1326498283"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Snehashis Majhi","raw_affiliation_strings":["INRIA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"INRIA","institution_ids":["https://openalex.org/I1326498283"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135335305","display_name":"Antitza Dantcheva","orcid":null},"institutions":[{"id":"https://openalex.org/I1326498283","display_name":"Institut national de recherche en sciences et technologies du num\u00e9rique","ror":"https://ror.org/02kvxyf05","country_code":"FR","type":"government","lineage":["https://openalex.org/I1326498283"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Antitza Dantcheva","raw_affiliation_strings":["INRIA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"INRIA","institution_ids":["https://openalex.org/I1326498283"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101735183","display_name":"Quan Kong","orcid":"https://orcid.org/0000-0002-4511-4031"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Quan Kong","raw_affiliation_strings":["Woven by Toyota"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Woven by Toyota","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014520592","display_name":"Lorenzo Garattoni","orcid":"https://orcid.org/0000-0002-6382-0335"},"institutions":[{"id":"https://openalex.org/I1293612202","display_name":"Toyota Motor Corporation (Switzerland)","ror":"https://ror.org/05p0pbv75","country_code":"CH","type":"company","lineage":["https://openalex.org/I1293612202","https://openalex.org/I4210125472","https://openalex.org/I4210137853"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Lorenzo Garattoni","raw_affiliation_strings":["Toyota Motor Europe"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Toyota Motor Europe","institution_ids":["https://openalex.org/I1293612202"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5016401744","display_name":"Gianpiero Francesca","orcid":"https://orcid.org/0000-0001-6066-9345"},"institutions":[{"id":"https://openalex.org/I1293612202","display_name":"Toyota Motor Corporation (Switzerland)","ror":"https://ror.org/05p0pbv75","country_code":"CH","type":"company","lineage":["https://openalex.org/I1293612202","https://openalex.org/I4210125472","https://openalex.org/I4210137853"]}],"countries":["CH"],"is_corresponding":false,"raw_author_name":"Gianpiero Francesca","raw_affiliation_strings":["Toyota Motor Europe"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Toyota Motor Europe","institution_ids":["https://openalex.org/I1293612202"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5135369699","display_name":"Fran\u00e7ois Br\u00e9mond","orcid":null},"institutions":[{"id":"https://openalex.org/I1326498283","display_name":"Institut national de recherche en sciences et technologies du num\u00e9rique","ror":"https://ror.org/02kvxyf05","country_code":"FR","type":"government","lineage":["https://openalex.org/I1326498283"]}],"countries":["FR"],"is_corresponding":false,"raw_author_name":"Fran\u00e7ois Br\u00e9mond","raw_affiliation_strings":["INRIA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"INRIA","institution_ids":["https://openalex.org/I1326498283"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":2,"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":"6246","last_page":"6255"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10029","display_name":"Climate variability and models","score":0.14650000631809235,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10029","display_name":"Climate variability and models","score":0.14650000631809235,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11483","display_name":"Tropical and Extratropical Cyclones Research","score":0.08479999750852585,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12424","display_name":"Earthquake Detection and Analysis","score":0.08139999955892563,"subfield":{"id":"https://openalex.org/subfields/1908","display_name":"Geophysics"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/anomaly","display_name":"Anomaly (physics)","score":0.3763999938964844},{"id":"https://openalex.org/keywords/identification","display_name":"Identification (biology)","score":0.3025999963283539},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.29739999771118164},{"id":"https://openalex.org/keywords/anomaly-detection","display_name":"Anomaly detection","score":0.2953999936580658},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.25459998846054077}],"concepts":[{"id":"https://openalex.org/C12997251","wikidata":"https://www.wikidata.org/wiki/Q567560","display_name":"Anomaly (physics)","level":2,"score":0.3763999938964844},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.36230000853538513},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.3190999925136566},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.3025999963283539},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.29739999771118164},{"id":"https://openalex.org/C739882","wikidata":"https://www.wikidata.org/wiki/Q3560506","display_name":"Anomaly detection","level":2,"score":0.2953999936580658},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.29109999537467957},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.28459998965263367},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.25459998846054077},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.24140000343322754}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/wacv61042.2026.00604","is_oa":false,"landing_page_url":"https://doi.org/10.1109/wacv61042.2026.00604","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2026 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Climate action","id":"https://metadata.un.org/sdg/13","score":0.7896169424057007}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":49,"referenced_works":["https://openalex.org/W2163612318","https://openalex.org/W2422305492","https://openalex.org/W2510642588","https://openalex.org/W2894666173","https://openalex.org/W2921491036","https://openalex.org/W2963570630","https://openalex.org/W2963720581","https://openalex.org/W2963782415","https://openalex.org/W2963795951","https://openalex.org/W2970271202","https://openalex.org/W2989839235","https://openalex.org/W2990591396","https://openalex.org/W3021065276","https://openalex.org/W3024312292","https://openalex.org/W3035021504","https://openalex.org/W3080722105","https://openalex.org/W3089682612","https://openalex.org/W3093823737","https://openalex.org/W3133092047","https://openalex.org/W3136793533","https://openalex.org/W3193673868","https://openalex.org/W3205543164","https://openalex.org/W3205786327","https://openalex.org/W3207758636","https://openalex.org/W3208955650","https://openalex.org/W4206784731","https://openalex.org/W4206905560","https://openalex.org/W4214589115","https://openalex.org/W4312355936","https://openalex.org/W4312660844","https://openalex.org/W4312690503","https://openalex.org/W4317537389","https://openalex.org/W4319299969","https://openalex.org/W4319300049","https://openalex.org/W4365802590","https://openalex.org/W4382461973","https://openalex.org/W4386057765","https://openalex.org/W4386066487","https://openalex.org/W4386071599","https://openalex.org/W4386721843","https://openalex.org/W4394596846","https://openalex.org/W4394598082","https://openalex.org/W4402727280","https://openalex.org/W4402969043","https://openalex.org/W4404719524","https://openalex.org/W4409262407","https://openalex.org/W4413144299","https://openalex.org/W4413145996","https://openalex.org/W7160212287"],"related_works":[],"abstract_inverted_index":{"Forecasting":[0],"abnormal":[1,143],"human":[2],"behavior":[3,144],"(AHB)":[4],"in":[5,163],"unconstrained":[6],"real-world":[7],"environments":[8],"is":[9],"critical":[10],"for":[11,119],"enabling":[12],"proactive":[13],"safety":[14],"interventions.":[15],"Unlike":[16],"short-term":[17],"anomaly":[18],"detection,":[19],"long-horizon":[20],"forecasting":[21,165],"offers":[22],"a":[23,71,110],"vital":[24],"reaction":[25],"window":[26],"but":[27],"remains":[28],"underexplored":[29],"due":[30],"to":[31,125,142],"three":[32],"core":[33],"challenges:":[34],"(i)":[35],"noisy,":[36],"complex":[37],"human\u2013agent":[38],"interactions;":[39],"(ii)":[40],"weak":[41],"temporal":[42,117],"coupling":[43],"between":[44],"normal":[45],"observations":[46],"and":[47,50,69,90,99,128,146,167],"distant":[48],"anomalies;":[49],"(iii)":[51],"data":[52],"scarcity":[53],"limiting":[54],"the":[55,135,138],"scalability":[56],"of":[57],"autoregressive":[58,81],"models.":[59],"To":[60],"address":[61],"these,":[62],"we":[63],"propose":[64],"${{\\mathcal{D}}^3}{\\mathcal{P}}$":[65],"(Denoise,":[66],"Divide,":[67],"Distill,":[68],"Predict),":[70],"novel":[72],"encoder\u2013decoder":[73],"framework":[74,158],"that":[75],"bridges":[76],"denoised":[77],"pasts":[78],"with":[79,150],"distilled":[80],"futures.":[82],"Our":[83],"Differential":[84],"Past":[85],"Encoder":[86],"(DiPE)":[87],"disentangles":[88],"scene-level":[89],"object-level":[91],"dynamics":[92],"via":[93],"differential":[94],"attention,":[95],"suppressing":[96],"irrelevant":[97],"interactions":[98],"enhancing":[100],"discriminative":[101],"cues.":[102],"The":[103],"Distilled":[104],"Future":[105],"Auto-Regressive":[106],"Decoder":[107],"(D-FAD)":[108],"adopts":[109],"divide-and-conquer":[111],"strategy,":[112],"segmenting":[113],"future":[114],"queries":[115],"into":[116],"chunks":[118],"sequential":[120],"prediction,":[121],"while":[122],"leveraging":[123],"distillation":[124],"balance":[126],"robustness":[127],"latency.":[129],"We":[130],"validate":[131],"our":[132,157],"approach":[133],"on":[134],"AHB-F":[136],"benchmark,":[137],"only":[139],"dataset":[140],"dedicated":[141],"forecasting,":[145],"further":[147],"integrate":[148],"D-FAD":[149],"several":[151],"state-of-the-art":[152],"methods.":[153],"In":[154],"all":[155],"cases,":[156],"consistently":[159],"outperforms":[160],"prior":[161],"work":[162],"both":[164],"accuracy":[166],"computational":[168],"efficiency.":[169]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2026-05-06T00:00:00"}