{"id":"https://openalex.org/W4407737746","doi":"https://doi.org/10.1109/whispers65427.2024.10876540","title":"Incorporating Prompt Learning and Adaptive Dropping Hyperspectral Information Tracker for Hyperspectral Object Tracking","display_name":"Incorporating Prompt Learning and Adaptive Dropping Hyperspectral Information Tracker for Hyperspectral Object Tracking","publication_year":2024,"publication_date":"2024-12-09","ids":{"openalex":"https://openalex.org/W4407737746","doi":"https://doi.org/10.1109/whispers65427.2024.10876540"},"language":"en","primary_location":{"id":"doi:10.1109/whispers65427.2024.10876540","is_oa":false,"landing_page_url":"https://doi.org/10.1109/whispers65427.2024.10876540","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 14th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)","raw_type":"proceedings-article"},"type":"article","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/A5101805947","display_name":"Jiawei Zhou","orcid":"https://orcid.org/0000-0001-5590-6270"},"institutions":[{"id":"https://openalex.org/I3124059619","display_name":"China University of Geosciences","ror":"https://ror.org/04gcegc37","country_code":"CN","type":"education","lineage":["https://openalex.org/I3124059619"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Jiawei Zhou","raw_affiliation_strings":["China University of Geosciences, School of Geophysics and Geomatics,Wuhan,China,430074"],"affiliations":[{"raw_affiliation_string":"China University of Geosciences, School of Geophysics and Geomatics,Wuhan,China,430074","institution_ids":["https://openalex.org/I3124059619"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100349810","display_name":"Jiaqi Zhang","orcid":"https://orcid.org/0000-0001-7040-704X"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]},{"id":"https://openalex.org/I4210118728","display_name":"State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing","ror":"https://ror.org/02bpap860","country_code":"CN","type":"facility","lineage":["https://openalex.org/I4210118728"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jiaqi Zhang","raw_affiliation_strings":["Wuhan University,State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing,Wuhan,China,430079"],"affiliations":[{"raw_affiliation_string":"Wuhan University,State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing,Wuhan,China,430079","institution_ids":["https://openalex.org/I4210118728","https://openalex.org/I37461747"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084097095","display_name":"Yanni Dong","orcid":"https://orcid.org/0000-0003-0592-7887"},"institutions":[{"id":"https://openalex.org/I37461747","display_name":"Wuhan University","ror":"https://ror.org/033vjfk17","country_code":"CN","type":"education","lineage":["https://openalex.org/I37461747"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanni Dong","raw_affiliation_strings":["Wuhan University, School of Resource and Environmental Sciences,Wuhan,China,430079"],"affiliations":[{"raw_affiliation_string":"Wuhan University, School of Resource and Environmental Sciences,Wuhan,China,430079","institution_ids":["https://openalex.org/I37461747"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5101805947"],"corresponding_institution_ids":["https://openalex.org/I3124059619"],"apc_list":null,"apc_paid":null,"fwci":0.7548,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.82137337,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"5"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12389","display_name":"Infrared Target Detection Methodologies","score":0.6876000165939331,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T12389","display_name":"Infrared Target Detection Methodologies","score":0.6876000165939331,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10689","display_name":"Remote-Sensing Image Classification","score":0.6107000112533569,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/hyperspectral-imaging","display_name":"Hyperspectral imaging","score":0.9701195359230042},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6646833419799805},{"id":"https://openalex.org/keywords/tracking","display_name":"Tracking (education)","score":0.6309927701950073},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.62876296043396},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6256121397018433},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.5255245566368103},{"id":"https://openalex.org/keywords/object-detection","display_name":"Object detection","score":0.42636823654174805},{"id":"https://openalex.org/keywords/full-spectral-imaging","display_name":"Full spectral imaging","score":0.420244038105011},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.36478155851364136},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.18233877420425415},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.1279703974723816}],"concepts":[{"id":"https://openalex.org/C159078339","wikidata":"https://www.wikidata.org/wiki/Q959005","display_name":"Hyperspectral imaging","level":2,"score":0.9701195359230042},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6646833419799805},{"id":"https://openalex.org/C2775936607","wikidata":"https://www.wikidata.org/wiki/Q466845","display_name":"Tracking (education)","level":2,"score":0.6309927701950073},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.62876296043396},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6256121397018433},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.5255245566368103},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.42636823654174805},{"id":"https://openalex.org/C78660771","wikidata":"https://www.wikidata.org/wiki/Q5508206","display_name":"Full spectral imaging","level":3,"score":0.420244038105011},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.36478155851364136},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.18233877420425415},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.1279703974723816},{"id":"https://openalex.org/C19417346","wikidata":"https://www.wikidata.org/wiki/Q7922","display_name":"Pedagogy","level":1,"score":0.0},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/whispers65427.2024.10876540","is_oa":false,"landing_page_url":"https://doi.org/10.1109/whispers65427.2024.10876540","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 14th Workshop on Hyperspectral Imaging and Signal Processing: Evolution in Remote Sensing (WHISPERS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G6567680006","display_name":null,"funder_award_id":"62222116,62171417","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":15,"referenced_works":["https://openalex.org/W1510906150","https://openalex.org/W2898567522","https://openalex.org/W3035511673","https://openalex.org/W3089799197","https://openalex.org/W4289535600","https://openalex.org/W4307772628","https://openalex.org/W4312716506","https://openalex.org/W4312751983","https://openalex.org/W4312805142","https://openalex.org/W4312961843","https://openalex.org/W4321376273","https://openalex.org/W4391930232","https://openalex.org/W4400073491","https://openalex.org/W4402702990","https://openalex.org/W6746052068"],"related_works":["https://openalex.org/W2911259277","https://openalex.org/W2800956885","https://openalex.org/W2533019003","https://openalex.org/W1788560349","https://openalex.org/W2626158795","https://openalex.org/W2078656815","https://openalex.org/W2391021239","https://openalex.org/W2560525382","https://openalex.org/W2126575155","https://openalex.org/W2095129185"],"abstract_inverted_index":{"Hyperspectral":[0],"images":[1,104],"have":[2],"been":[3],"widely":[4],"used":[5],"in":[6,32],"fields":[7],"such":[8],"as":[9,106],"image":[10,13],"classification":[11],"and":[12,59,109,140,152,168],"segmentation.":[14],"The":[15],"spectral":[16],"information":[17,62,83,116,142],"allows":[18],"for":[19,84],"the":[20,33,53,93,100,110,121,125,136],"differentiation":[21],"between":[22],"similar":[23],"targets,":[24],"leading":[25],"to":[26,44,52,91],"an":[27],"increasing":[28],"interest":[29],"from":[30,63],"researchers":[31],"area":[34],"of":[35,55,102,113,127,138,149],"hyperspectral":[36,48,65,82,85,103,115,141],"object":[37,49,86],"tracking":[38,87],"(HOT).":[39],"However,":[40],"it":[41],"is":[42],"difficult":[43],"train":[45],"a":[46,73],"powerful":[47,94],"tracker":[50,74,129],"due":[51],"lack":[54,137],"large-scale":[56],"HSI-based":[57],"datasets":[58,139],"get":[60],"useful":[61],"redundant":[64],"information.":[66],"To":[67],"address":[68,135],"this":[69],"problem,":[70],"We":[71],"propose":[72],"based":[75],"on":[76],"prompt":[77],"learning":[78],"that":[79],"adaptive":[80],"dropping":[81,114],"(ADTrack-HOT).":[88],"In":[89],"order":[90],"leverage":[92],"rgb-based":[95,128],"tracker,":[96,151],"our":[97,150],"method":[98,133],"involves":[99],"incorporation":[101],"(HSIs)":[105],"visual":[107],"prompts":[108],"innovative":[111],"strategy":[112],"with":[117],"rgb":[118],"prompts.":[119],"During":[120],"whole":[122],"traning":[123],"phase,":[124],"parameters":[126],"are":[130],"frozen.":[131],"Proposed":[132],"effectively":[134],"redundancy":[143],"issues.":[144],"Extensive":[145],"experiments":[146],"demonstrate":[147],"effectiveness":[148],"ADTrack-HOT":[153],"achieves":[154],"superior":[155],"performance":[156],"across":[157],"diverse":[158],"downstream":[159],"tasks,":[160],"spanning":[161],"VIS":[162],"(16":[163],"bands),":[164,167],"RedNIR":[165],"(15":[166],"NIR":[169],"(25":[170],"bands).":[171]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2025-12-28T23:10:05.387466","created_date":"2025-10-10T00:00:00"}