{"id":"https://openalex.org/W7160863914","doi":"https://doi.org/10.48550/arxiv.2605.07064","title":"Learning to Track Instance from Single Nature Language Description","display_name":"Learning to Track Instance from Single Nature Language Description","publication_year":2026,"publication_date":"2026-05-08","ids":{"openalex":"https://openalex.org/W7160863914","doi":"https://doi.org/10.48550/arxiv.2605.07064"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.07064","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.07064","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.2605.07064","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5065943384","display_name":"Yaozong Zheng","orcid":"https://orcid.org/0009-0007-2664-0574"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zheng, Yaozong","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135830446","display_name":"Bineng Zhong","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhong, Bineng","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135880915","display_name":"Qihua Liang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Liang, Qihua","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135870936","display_name":"Shuimu Zeng","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zeng, Shuimu","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135860424","display_name":"Haiying Xia","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Xia, Haiying","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135903459","display_name":"Shuxiang Song","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Song, Shuxiang","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":0.8353000283241272,"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":0.8353000283241272,"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/T10331","display_name":"Video Surveillance and Tracking Methods","score":0.04580000042915344,"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/T11307","display_name":"Domain Adaptation and Few-Shot Learning","score":0.016499999910593033,"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/security-token","display_name":"Security token","score":0.7073000073432922},{"id":"https://openalex.org/keywords/minimum-bounding-box","display_name":"Minimum bounding box","score":0.6159999966621399},{"id":"https://openalex.org/keywords/eye-tracking","display_name":"Eye tracking","score":0.5593000054359436},{"id":"https://openalex.org/keywords/natural-language","display_name":"Natural language","score":0.5569999814033508},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.5453000068664551},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.47589999437332153},{"id":"https://openalex.org/keywords/video-tracking","display_name":"Video tracking","score":0.4537000060081482},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.4433000087738037},{"id":"https://openalex.org/keywords/semantics","display_name":"Semantics (computer science)","score":0.4336000084877014}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8359000086784363},{"id":"https://openalex.org/C48145219","wikidata":"https://www.wikidata.org/wiki/Q1335365","display_name":"Security token","level":2,"score":0.7073000073432922},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.641700029373169},{"id":"https://openalex.org/C147037132","wikidata":"https://www.wikidata.org/wiki/Q6865426","display_name":"Minimum bounding box","level":3,"score":0.6159999966621399},{"id":"https://openalex.org/C56461940","wikidata":"https://www.wikidata.org/wiki/Q970687","display_name":"Eye tracking","level":2,"score":0.5593000054359436},{"id":"https://openalex.org/C195324797","wikidata":"https://www.wikidata.org/wiki/Q33742","display_name":"Natural language","level":2,"score":0.5569999814033508},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.5453000068664551},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.47589999437332153},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.45419999957084656},{"id":"https://openalex.org/C202474056","wikidata":"https://www.wikidata.org/wiki/Q1931635","display_name":"Video tracking","level":3,"score":0.4537000060081482},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.4433000087738037},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.4336000084877014},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.4325999915599823},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.42329999804496765},{"id":"https://openalex.org/C137293760","wikidata":"https://www.wikidata.org/wiki/Q3621696","display_name":"Language model","level":2,"score":0.3799000084400177},{"id":"https://openalex.org/C174252522","wikidata":"https://www.wikidata.org/wiki/Q3816772","display_name":"Natural language user interface","level":3,"score":0.3675999939441681},{"id":"https://openalex.org/C124304363","wikidata":"https://www.wikidata.org/wiki/Q673661","display_name":"Abstraction","level":2,"score":0.3666999936103821},{"id":"https://openalex.org/C63584917","wikidata":"https://www.wikidata.org/wiki/Q333286","display_name":"Bounding overwatch","level":2,"score":0.3580999970436096},{"id":"https://openalex.org/C2779439875","wikidata":"https://www.wikidata.org/wiki/Q1078276","display_name":"Natural language understanding","level":3,"score":0.35569998621940613},{"id":"https://openalex.org/C129792486","wikidata":"https://www.wikidata.org/wiki/Q1050419","display_name":"Language identification","level":3,"score":0.3537999987602234},{"id":"https://openalex.org/C2778112365","wikidata":"https://www.wikidata.org/wiki/Q3511065","display_name":"Sequence (biology)","level":2,"score":0.35199999809265137},{"id":"https://openalex.org/C44291984","wikidata":"https://www.wikidata.org/wiki/Q1074173","display_name":"Question answering","level":2,"score":0.3407000005245209},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.325300008058548},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.319599986076355},{"id":"https://openalex.org/C36464697","wikidata":"https://www.wikidata.org/wiki/Q451553","display_name":"Visualization","level":2,"score":0.3151000142097473},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3052999973297119},{"id":"https://openalex.org/C107457646","wikidata":"https://www.wikidata.org/wiki/Q207434","display_name":"Human\u2013computer interaction","level":1,"score":0.2904999852180481},{"id":"https://openalex.org/C2779916870","wikidata":"https://www.wikidata.org/wiki/Q14467155","display_name":"Gaze","level":2,"score":0.28209999203681946},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.27059999108314514},{"id":"https://openalex.org/C55508974","wikidata":"https://www.wikidata.org/wiki/Q190763","display_name":"Venn diagram","level":2,"score":0.2603999972343445},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.25040000677108765}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.07064","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.07064","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.2605.07064","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.07064","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":[{"display_name":"Quality Education","score":0.6756175756454468,"id":"https://metadata.un.org/sdg/4"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"How":[0],"to":[1,35,120,150,162,172],"achieve":[2,25],"vision-language":[3],"(VL)":[4],"tracking":[5,37,56,176,191,205],"using":[6],"natural":[7,41],"language":[8,42,62,71,128,144],"descriptions":[9],"from":[10,108,155,177],"a":[11,47,61],"video":[12],"sequence":[13],"\\textbf{without":[14],"relying":[15],"on":[16,98,203],"any":[17,57],"bounding-box":[18],"ground":[19],"truth}?":[20],"In":[21],"this":[22,26],"work,":[23],"we":[24,75],"goal":[27],"by":[28,40,60],"tackling":[29],"\\textit{self-supervised":[30],"VL":[31,50,204],"tracking},":[32],"which":[33,83],"aims":[34],"evaluate":[36],"capabilities":[38],"guided":[39],"descriptions.":[43],"We":[44],"introduce":[45],"\\textbf{\\tracker},":[46],"novel":[48],"self-supervised":[49,187,212],"tracker":[51,171],"that":[52,67,208],"is":[53],"capable":[54],"of":[55,92,189],"referred":[58],"object":[59],"description.":[63],"Unlike":[64],"traditional":[65],"methods":[66],"equally":[68],"fuse":[69],"all":[70],"and":[72,124,136,159,168],"visual":[73,86,133],"tokens,":[74,129],"propose":[76],"an":[77,99],"efficient":[78],"Dynamic":[79],"Token":[80],"Aggregation":[81],"Module,":[82],"treats":[84],"each":[85],"token":[87,134],"\\textbf{unequally}.":[88],"The":[89,113],"module":[90],"consists":[91],"three":[93],"main":[94],"steps:":[95],"i)":[96],"Based":[97],"anchor":[100],"token,":[101],"it":[102],"selects":[103],"multiple":[104],"important":[105],"target":[106,115,153],"tokens":[107,116,145,154],"the":[109,127,142,156,170,185,194],"template":[110],"frame.":[111],"ii)":[112],"selected":[114],"are":[117],"merged":[118],"according":[119],"their":[121],"attention":[122],"scores":[123],"aggregated":[125],"into":[126],"thereby":[130],"eliminating":[131],"redundant":[132],"noise":[135],"enhancing":[137,165],"semantic":[138],"alignment.":[139],"iii)":[140],"Finally,":[141],"fused":[143],"serve":[146],"as":[147],"guiding":[148],"signals":[149],"extract":[151],"potential":[152],"search":[157],"frame":[158],"propagate":[160],"them":[161],"subsequent":[163],"frames,":[164],"temporal":[166],"prompts":[167],"encouraging":[169],"autonomously":[173],"learn":[174],"instance":[175],"unlabeled":[178],"videos.":[179],"This":[180],"new":[181],"modeling":[182],"approach":[183],"enables":[184],"effective":[186],"learning":[188],"language-guided":[190],"representations":[192],"without":[193],"need":[195],"for":[196],"large-scale":[197],"bounding":[198],"box":[199],"annotations.":[200],"Extensive":[201],"experiments":[202],"benchmarks":[206],"show":[207],"{\\tracker}":[209],"surpasses":[210],"SOTA":[211],"methods.":[213]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-12T00:00:00"}
