{"id":"https://openalex.org/W4416749208","doi":"https://doi.org/10.1109/iros60139.2025.11247710","title":"HFDNet: High-Frequency Divergence Attention Network for Underwater Segmentation","display_name":"HFDNet: High-Frequency Divergence Attention Network for Underwater Segmentation","publication_year":2025,"publication_date":"2025-10-19","ids":{"openalex":"https://openalex.org/W4416749208","doi":"https://doi.org/10.1109/iros60139.2025.11247710"},"language":null,"primary_location":{"id":"doi:10.1109/iros60139.2025.11247710","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros60139.2025.11247710","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","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/A5022681541","display_name":"Hongbo Xie","orcid":null},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Hongbo Xie","raw_affiliation_strings":["Beihang University,Department of Electronics and Information Engineering,Beijing,China,100191"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beihang University,Department of Electronics and Information Engineering,Beijing,China,100191","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028355034","display_name":"Qi Zhao","orcid":"https://orcid.org/0000-0002-5969-6407"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qi Zhao","raw_affiliation_strings":["Beihang University,Department of Electronics and Information Engineering,Beijing,China,100191"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beihang University,Department of Electronics and Information Engineering,Beijing,China,100191","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052052722","display_name":"Binghao Liu","orcid":"https://orcid.org/0000-0001-6590-0016"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Binghao Liu","raw_affiliation_strings":["Beihang University,Department of Electronics and Information Engineering,Beijing,China,100191"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beihang University,Department of Electronics and Information Engineering,Beijing,China,100191","institution_ids":["https://openalex.org/I82880672"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100710371","display_name":"Chunlei Wang","orcid":"https://orcid.org/0000-0002-8955-9964"},"institutions":[{"id":"https://openalex.org/I82880672","display_name":"Beihang University","ror":"https://ror.org/00wk2mp56","country_code":"CN","type":"education","lineage":["https://openalex.org/I82880672"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chunlei Wang","raw_affiliation_strings":["Beihang University,Department of Electronics and Information Engineering,Beijing,China,100191"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Beihang University,Department of Electronics and Information Engineering,Beijing,China,100191","institution_ids":["https://openalex.org/I82880672"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I82880672"],"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":"11524","last_page":"11530"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11019","display_name":"Image Enhancement Techniques","score":0.6759999990463257,"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/T11019","display_name":"Image Enhancement Techniques","score":0.6759999990463257,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.22360000014305115,"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":0.018400000408291817,"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/segmentation","display_name":"Segmentation","score":0.7857999801635742},{"id":"https://openalex.org/keywords/underwater","display_name":"Underwater","score":0.6687999963760376},{"id":"https://openalex.org/keywords/weighting","display_name":"Weighting","score":0.5927000045776367},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.5888000130653381},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5030999779701233},{"id":"https://openalex.org/keywords/divergence","display_name":"Divergence (linguistics)","score":0.5012000203132629},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4648999869823456},{"id":"https://openalex.org/keywords/object","display_name":"Object (grammar)","score":0.4307999908924103},{"id":"https://openalex.org/keywords/boundary","display_name":"Boundary (topology)","score":0.4205999970436096}],"concepts":[{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.7857999801635742},{"id":"https://openalex.org/C98083399","wikidata":"https://www.wikidata.org/wiki/Q3246517","display_name":"Underwater","level":2,"score":0.6687999963760376},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6654000282287598},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6380000114440918},{"id":"https://openalex.org/C183115368","wikidata":"https://www.wikidata.org/wiki/Q856577","display_name":"Weighting","level":2,"score":0.5927000045776367},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.5888000130653381},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5030999779701233},{"id":"https://openalex.org/C207390915","wikidata":"https://www.wikidata.org/wiki/Q1230525","display_name":"Divergence (linguistics)","level":2,"score":0.5012000203132629},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4648999869823456},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.4602000117301941},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.4307999908924103},{"id":"https://openalex.org/C62354387","wikidata":"https://www.wikidata.org/wiki/Q875399","display_name":"Boundary (topology)","level":2,"score":0.4205999970436096},{"id":"https://openalex.org/C65885262","wikidata":"https://www.wikidata.org/wiki/Q7429708","display_name":"Scale-space segmentation","level":4,"score":0.39590001106262207},{"id":"https://openalex.org/C19118579","wikidata":"https://www.wikidata.org/wiki/Q786423","display_name":"Frequency domain","level":2,"score":0.3709999918937683},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.35600000619888306},{"id":"https://openalex.org/C67561299","wikidata":"https://www.wikidata.org/wiki/Q7292710","display_name":"Range segmentation","level":5,"score":0.31949999928474426},{"id":"https://openalex.org/C12713177","wikidata":"https://www.wikidata.org/wiki/Q1900281","display_name":"Perspective (graphical)","level":2,"score":0.3142000138759613},{"id":"https://openalex.org/C25694479","wikidata":"https://www.wikidata.org/wiki/Q7446278","display_name":"Segmentation-based object categorization","level":5,"score":0.29649999737739563},{"id":"https://openalex.org/C100921725","wikidata":"https://www.wikidata.org/wiki/Q1650811","display_name":"Spatial frequency","level":2,"score":0.29429998993873596},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.2903999984264374},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.28600001335144043},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.28290000557899475},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.2757999897003174},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.2538999915122986}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iros60139.2025.11247710","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros60139.2025.11247710","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W1861492603","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W2167222293","https://openalex.org/W2340897893","https://openalex.org/W2412782625","https://openalex.org/W2560023338","https://openalex.org/W2565639579","https://openalex.org/W2884822772","https://openalex.org/W2895340641","https://openalex.org/W2955058313","https://openalex.org/W2963872524","https://openalex.org/W2980175935","https://openalex.org/W3003508462","https://openalex.org/W3006777311","https://openalex.org/W3039108591","https://openalex.org/W3109503058","https://openalex.org/W3132413796","https://openalex.org/W3138516171","https://openalex.org/W3170841864","https://openalex.org/W3211490618","https://openalex.org/W4206693420","https://openalex.org/W4224931296","https://openalex.org/W4312815172","https://openalex.org/W4319939156","https://openalex.org/W4389665914","https://openalex.org/W4400229522","https://openalex.org/W4401416093","https://openalex.org/W4401765299","https://openalex.org/W4402753969","https://openalex.org/W4402754178","https://openalex.org/W4402916449","https://openalex.org/W4405270558"],"related_works":[],"abstract_inverted_index":{"Currently,":[0],"most":[1],"underwater":[2,58,203,227],"operations":[3],"are":[4,28,69],"conducted":[5],"in":[6,15,31,45,57,130,202],"deep":[7],"water,":[8],"and":[9,33,107,179,206,232],"there":[10],"is":[11,35,251],"usually":[12],"insufficient":[13,200],"illumination":[14],"these":[16],"areas.":[17,166],"At":[18],"this":[19],"time,":[20],"the":[21,39,49,63,80,100,104,110,123,131,142,151,156,161,164,171,175,191,195,208,245],"local":[22,128,135,180,215],"texture":[23],"features":[24,216],"of":[25,48,54,62,103,114,153,193],"some":[26],"objects":[27,212],"highly":[29],"similar":[30,214],"images,":[32],"it":[34],"difficult":[36],"to":[37,72,74,149,155,163,210],"distinguish":[38],"inter-class":[40],"boundaries.":[41],"This":[42,167],"typically":[43],"results":[44,235],"poor":[46],"performance":[47,243],"current":[50],"semantic":[51,75,87],"segmentation":[52,76,88,228],"models":[53],"terrestrial":[55],"images":[56],"scenes.":[59],"Taking":[60],"advantage":[61],"general":[64],"characteristic":[65],"that":[66,237],"high-frequency":[67,81,165],"regions":[68],"more":[70],"likely":[71],"correspond":[73],"boundaries,":[77],"we":[78],"introduce":[79],"Divergence":[82],"Attention":[83],"Network":[84],"(HFDNet),":[85],"a":[86,146],"model":[89,188],"based":[90],"on":[91,174,225,244],"transformer.":[92],"HFDNet":[93,239],"extracts":[94],"its":[95,119,127],"frequency":[96,101,120,132,136],"distribution":[97],"by":[98,117,199],"analyzing":[99],"domain":[102],"feature":[105],"map,":[106],"then":[108],"calculates":[109],"relative":[111],"spectral":[112],"magnitude":[113],"each":[115,184],"component":[116],"comparing":[118],"amplitude":[121,125],"against":[122],"average":[124],"within":[126],"neighborhood":[129],"domain.":[133],"The":[134,234,248],"map":[137],"can":[138,169,189],"be":[139],"incorporated":[140],"into":[141],"attention":[143,154,162],"matrix":[144],"as":[145],"weighting":[147],"factor":[148],"realize":[150],"divergence":[152],"surrounding":[157],"areas,":[158],"which":[159],"improves":[160],"operation":[168],"enhance":[170,207],"model\u2019s":[172],"focus":[173],"object":[176,196],"boundary":[177,197],"region":[178],"neigh-borhood":[181],"categories":[182],"for":[183],"component.":[185],"Therefore,":[186],"our":[187,238],"alleviate":[190],"problem":[192],"determining":[194],"caused":[198],"light":[201,219],"image":[204],"segmentation,":[205],"ability":[209],"segment":[211],"with":[213],"under":[217],"low":[218],"conditions.":[220],"We":[221],"conduct":[222],"comprehensive":[223],"experiments":[224],"three":[226],"datasets:":[229],"Caveseg,":[230],"SUIM":[231],"UWS.":[233],"show":[236],"achieves":[240],"state-of-the-art":[241],"(SOTA)":[242],"testing":[246],"datasets.":[247],"source":[249],"code":[250],"available":[252],"at":[253],"https://github.com/cv516Buaa/HongboXie/tree/main/HFDNet.":[254]},"counts_by_year":[],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-11-28T00:00:00"}
