{"id":"https://openalex.org/W7150900513","doi":"https://doi.org/10.48550/arxiv.2604.02860","title":"A Paradigm Shift: Fully End-to-End Training for Temporal Sentence Grounding in Videos","display_name":"A Paradigm Shift: Fully End-to-End Training for Temporal Sentence Grounding in Videos","publication_year":2026,"publication_date":"2026-04-03","ids":{"openalex":"https://openalex.org/W7150900513","doi":"https://doi.org/10.48550/arxiv.2604.02860"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2604.02860","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.02860","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.2604.02860","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5110400611","display_name":"Allen He","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"He, Allen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133022751","display_name":"Qi Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Qi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5133060301","display_name":"Kun Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Kun","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5030704926","display_name":"Xinchen Liu","orcid":"https://orcid.org/0000-0003-4931-8821"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Xinchen","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5133050317","display_name":"Wu Liu","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liu, Wu","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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9794999957084656,"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/T11714","display_name":"Multimodal Machine Learning Applications","score":0.9794999957084656,"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":0.005200000014156103,"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":0.00279999990016222,"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/sentence","display_name":"Sentence","score":0.7124999761581421},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.6053000092506409},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5544999837875366},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5066999793052673},{"id":"https://openalex.org/keywords/adapter","display_name":"Adapter (computing)","score":0.42989999055862427},{"id":"https://openalex.org/keywords/bridge","display_name":"Bridge (graph theory)","score":0.4246000051498413},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.41749998927116394},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.3833000063896179},{"id":"https://openalex.org/keywords/feature-learning","display_name":"Feature learning","score":0.34439998865127563}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8373000025749207},{"id":"https://openalex.org/C2777530160","wikidata":"https://www.wikidata.org/wiki/Q41796","display_name":"Sentence","level":2,"score":0.7124999761581421},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.6053000092506409},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5978999733924866},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5544999837875366},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5066999793052673},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.45750001072883606},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.4546000063419342},{"id":"https://openalex.org/C177284502","wikidata":"https://www.wikidata.org/wiki/Q1005390","display_name":"Adapter (computing)","level":2,"score":0.42989999055862427},{"id":"https://openalex.org/C100776233","wikidata":"https://www.wikidata.org/wiki/Q2532492","display_name":"Bridge (graph theory)","level":2,"score":0.4246000051498413},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.41749998927116394},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.3833000063896179},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.34439998865127563},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3287000060081482},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.31700000166893005},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.31679999828338623},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.3068999946117401},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.3043000102043152},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.2962999939918518},{"id":"https://openalex.org/C175154964","wikidata":"https://www.wikidata.org/wiki/Q380077","display_name":"Task analysis","level":3,"score":0.2816999852657318},{"id":"https://openalex.org/C122783720","wikidata":"https://www.wikidata.org/wiki/Q183065","display_name":"Interpreter","level":2,"score":0.2793000042438507},{"id":"https://openalex.org/C88796919","wikidata":"https://www.wikidata.org/wiki/Q1142907","display_name":"Backbone network","level":2,"score":0.27649998664855957},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.27160000801086426},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.26809999346733093},{"id":"https://openalex.org/C83665646","wikidata":"https://www.wikidata.org/wiki/Q42139305","display_name":"Feature vector","level":2,"score":0.2680000066757202},{"id":"https://openalex.org/C2776224158","wikidata":"https://www.wikidata.org/wiki/Q187931","display_name":"Phrase","level":2,"score":0.26510000228881836},{"id":"https://openalex.org/C2779960059","wikidata":"https://www.wikidata.org/wiki/Q7113681","display_name":"Overhead (engineering)","level":2,"score":0.25870001316070557},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.25450000166893005}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2604.02860","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.02860","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.2604.02860","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2604.02860","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":[{"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education","score":0.40385398268699646}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Temporal":[0],"sentence":[1,17,113],"grounding":[2],"in":[3],"videos":[4],"(TSGV)":[5],"aims":[6],"to":[7,15,48,115],"localize":[8],"a":[9,16,49,71,106,117],"temporal":[10],"segment":[11],"that":[12,75,156],"semantically":[13],"corresponds":[14],"query":[18],"from":[19],"an":[20,87],"untrimmed":[21],"video.":[22],"Most":[23],"current":[24],"methods":[25],"adopt":[26],"pre-trained":[27],"query-agnostic":[28],"visual":[29,59,139],"encoders":[30],"for":[31,44,53,58,63],"offline":[32],"feature":[33,143],"extraction,":[34],"and":[35,41,81,136,164],"the":[36,54,78,91,127],"video":[37,55,79,121],"backbones":[38,132],"are":[39],"frozen":[40,97],"not":[42],"optimized":[43],"TSGV.":[45,64],"This":[46],"leads":[47],"task":[50],"discrepancy":[51],"issue":[52],"backbone":[56,80,122],"trained":[57],"classification,":[60],"but":[61],"utilized":[62],"To":[65],"bridge":[66],"this":[67],"gap,":[68],"we":[69,104],"propose":[70],"fully":[72],"end-to-end":[73,94],"paradigm":[74],"jointly":[76],"optimizes":[77],"localization":[82],"head.":[83],"We":[84],"first":[85],"conduct":[86],"empirical":[88],"study":[89],"validating":[90],"effectiveness":[92],"of":[93,120,129,148],"learning":[95],"over":[96],"baselines":[98],"across":[99],"different":[100],"model":[101],"scales.":[102],"Furthermore,":[103],"introduce":[105],"Sentence":[107],"Conditioned":[108],"Adapter":[109],"(SCADA),":[110],"which":[111],"leverages":[112],"features":[114],"train":[116],"small":[118],"portion":[119],"parameters":[123],"adaptively.":[124],"SCADA":[125],"facilitates":[126],"deployment":[128],"deeper":[130],"network":[131],"with":[133],"reduced":[134],"memory":[135],"significantly":[137],"enhances":[138],"representation":[140],"by":[141],"modulating":[142],"maps":[144],"through":[145],"precise":[146],"integration":[147],"linguistic":[149],"embeddings.":[150],"Experiments":[151],"on":[152],"two":[153],"benchmarks":[154],"show":[155],"our":[157],"method":[158],"outperforms":[159],"state-of-the-art":[160],"approaches.":[161],"The":[162],"code":[163],"models":[165],"will":[166],"be":[167],"released.":[168]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-04-07T00:00:00"}