{"id":"https://openalex.org/W7163063215","doi":"https://doi.org/10.48550/arxiv.2605.31108","title":"Remembering by Reconstructing: Domain Incremental Learning With Test-Time Training on Video Streams","display_name":"Remembering by Reconstructing: Domain Incremental Learning With Test-Time Training on Video Streams","publication_year":2026,"publication_date":"2026-05-29","ids":{"openalex":"https://openalex.org/W7163063215","doi":"https://doi.org/10.48550/arxiv.2605.31108"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.31108","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.31108","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","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.2605.31108","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5137608147","display_name":"Jonathan Swinnen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Swinnen, Jonathan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5137578283","display_name":"Tinne Tuytelaars","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tuytelaars, Tinne","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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9230999946594238,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.9230999946594238,"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":0.03009999915957451,"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/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.010599999688565731,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/forgetting","display_name":"Forgetting","score":0.6089000105857849},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.607699990272522},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5374000072479248},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5227000117301941},{"id":"https://openalex.org/keywords/autoencoder","display_name":"Autoencoder","score":0.5002999901771545},{"id":"https://openalex.org/keywords/incremental-learning","display_name":"Incremental learning","score":0.48190000653266907},{"id":"https://openalex.org/keywords/training","display_name":"Training (meteorology)","score":0.4327000081539154},{"id":"https://openalex.org/keywords/training-set","display_name":"Training set","score":0.4302000105381012},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.39649999141693115}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8083000183105469},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6471999883651733},{"id":"https://openalex.org/C7149132","wikidata":"https://www.wikidata.org/wiki/Q1377840","display_name":"Forgetting","level":2,"score":0.6089000105857849},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.607699990272522},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5374000072479248},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5227000117301941},{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.5002999901771545},{"id":"https://openalex.org/C2780735816","wikidata":"https://www.wikidata.org/wiki/Q28324931","display_name":"Incremental learning","level":2,"score":0.48190000653266907},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45159998536109924},{"id":"https://openalex.org/C2777211547","wikidata":"https://www.wikidata.org/wiki/Q17141490","display_name":"Training (meteorology)","level":2,"score":0.4327000081539154},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.4302000105381012},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.39649999141693115},{"id":"https://openalex.org/C2780312720","wikidata":"https://www.wikidata.org/wiki/Q5689100","display_name":"Head (geology)","level":2,"score":0.3779999911785126},{"id":"https://openalex.org/C177284502","wikidata":"https://www.wikidata.org/wiki/Q1005390","display_name":"Adapter (computing)","level":2,"score":0.34709998965263367},{"id":"https://openalex.org/C2776502983","wikidata":"https://www.wikidata.org/wiki/Q690182","display_name":"Contrast (vision)","level":2,"score":0.3465000092983246},{"id":"https://openalex.org/C89198739","wikidata":"https://www.wikidata.org/wiki/Q3079880","display_name":"Data stream mining","level":2,"score":0.33709999918937683},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.3330000042915344},{"id":"https://openalex.org/C92548554","wikidata":"https://www.wikidata.org/wiki/Q2262868","display_name":"Domain model","level":3,"score":0.31690001487731934},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.31630000472068787},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.3149999976158142},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3122999966144562},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.3084999918937683},{"id":"https://openalex.org/C100660578","wikidata":"https://www.wikidata.org/wiki/Q18733","display_name":"Recall","level":2,"score":0.3005000054836273},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.29260000586509705},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.2872999906539917},{"id":"https://openalex.org/C2780791683","wikidata":"https://www.wikidata.org/wiki/Q846785","display_name":"Action (physics)","level":2,"score":0.28290000557899475},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.26809999346733093},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.2635999917984009},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.25029999017715454}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.31108","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.31108","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2605.31108","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.31108","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":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/10","score":0.43951550126075745,"display_name":"Reduced inequalities"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"In":[0,20],"this":[1],"work":[2],"we":[3,25,80],"introduce":[4],"a":[5,43,48],"novel":[6],"approach":[7],"to":[8,16,22,29,66,90,112],"domain":[9,105,126],"incremental":[10,61],"learning,":[11],"adapting":[12],"models":[13],"over":[14],"time":[15],"evolving,":[17],"non-stationary":[18],"data.":[19],"contrast":[21],"other":[23,73],"works,":[24],"do":[26],"not":[27],"attempt":[28],"avoid":[30],"catastrophic":[31],"forgetting,":[32],"but":[33],"rather":[34],"allow":[35],"it":[36],"and":[37,125,138],"exploit":[38],"it.":[39],"Our":[40,107],"model":[41,101],"combines":[42],"main":[44],"task":[45],"head":[46,89],"with":[47],"self-supervised":[49,87],"masked":[50],"autoencoder":[51],"(MAE)":[52],"head.":[53],"We":[54,130],"then":[55],"learn":[56],"domain-specific":[57],"LoRA":[58],"adapters":[59],"during":[60],"training.":[62],"Each":[63],"adapter":[64],"specializes":[65],"its":[67],"domain,":[68],"naturally":[69],"inducing":[70],"forgetting":[71],"on":[72,85,134],"domains":[74],"in":[75],"both":[76],"heads.":[77],"At":[78],"inference,":[79],"perform":[81],"online":[82],"test-time":[83],"training":[84],"the":[86,96,100,104],"MAE":[88],"identify":[91],"which":[92],"LoRAs":[93],"best":[94],"matches":[95],"current":[97],"input,":[98],"so":[99],"can":[102],"`remember'":[103],"again.":[106],"scheme":[108],"is":[109],"especially":[110],"well-suited":[111],"real-world":[113],"streaming":[114],"data,":[115],"such":[116],"as":[117],"video,":[118],"where":[119],"consecutive":[120],"samples":[121],"are":[122,128],"highly":[123],"correlated":[124],"shifts":[127],"gradual.":[129],"demonstrate":[131],"our":[132],"method":[133],"domain-incremental":[135],"action":[136],"recognition":[137],"semantic":[139],"segmentation":[140],"tasks.":[141]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-06-02T00:00:00"}
