{"id":"https://openalex.org/W4417172200","doi":"https://doi.org/10.5753/jbcs.2026.5893","title":"FLIM-based Salient Object Detection Networks with Adaptive Decoders","display_name":"FLIM-based Salient Object Detection Networks with Adaptive Decoders","publication_year":2026,"publication_date":"2026-04-16","ids":{"openalex":"https://openalex.org/W4417172200","doi":"https://doi.org/10.5753/jbcs.2026.5893"},"language":"en","primary_location":{"id":"doi:10.5753/jbcs.2026.5893","is_oa":true,"landing_page_url":"https://doi.org/10.5753/jbcs.2026.5893","pdf_url":null,"source":{"id":"https://openalex.org/S69801987","display_name":"Journal of the Brazilian Computer Society","issn_l":"0104-6500","issn":["0104-6500","1678-4804"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of the Brazilian Computer Society","raw_type":"journal-article"},"type":"article","indexed_in":["arxiv","crossref","datacite","doaj"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://doi.org/10.5753/jbcs.2026.5893","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5109743144","display_name":"Gilson Junior Soares","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Gilson Junior Soares","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0001-8805-5644","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5003748418","display_name":"Matheus Abrantes Cerqueira","orcid":"https://orcid.org/0000-0003-3655-3435"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Matheus Abrantes Cerqueira","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0003-3655-3435","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5000839360","display_name":"Jancarlo Ferreira Gomes","orcid":"https://orcid.org/0000-0002-2466-7315"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jancarlo F. Gomes","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-2466-7315","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5052368829","display_name":"Laurent Najman","orcid":"https://orcid.org/0000-0002-6190-0235"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Laurent Najman","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-6190-0235","affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070629635","display_name":"Silvio Jamil F. Guimar\u00e3es","orcid":"https://orcid.org/0000-0001-8522-2056"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Silvio Jamil F. Guimar\u00e3es","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0001-8522-2056","affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5015267493","display_name":"Alexandre X. Falc\u00e3o","orcid":"https://orcid.org/0000-0002-2914-5380"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alexandre X. Falc\u00e3o","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0000-0002-2914-5380","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5109743144"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.01283946,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"32","issue":"1","first_page":"750","last_page":"765"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9775000214576721,"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/T11605","display_name":"Visual Attention and Saliency Detection","score":0.9775000214576721,"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":0.014499999582767487,"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/T11659","display_name":"Advanced Image Fusion Techniques","score":0.0013000000035390258,"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/pixel","display_name":"Pixel","score":0.7062000036239624},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.6165000200271606},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.6047000288963318},{"id":"https://openalex.org/keywords/salient","display_name":"Salient","score":0.5871999859809875},{"id":"https://openalex.org/keywords/heuristic","display_name":"Heuristic","score":0.52920001745224},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5095999836921692},{"id":"https://openalex.org/keywords/decodes","display_name":"Decodes","score":0.5054000020027161},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5034999847412109},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.5001999735832214}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7621999979019165},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7588000297546387},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.7062000036239624},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.6165000200271606},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6047000288963318},{"id":"https://openalex.org/C2780719617","wikidata":"https://www.wikidata.org/wiki/Q1030752","display_name":"Salient","level":2,"score":0.5871999859809875},{"id":"https://openalex.org/C173801870","wikidata":"https://www.wikidata.org/wiki/Q201413","display_name":"Heuristic","level":2,"score":0.52920001745224},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5095999836921692},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.508899986743927},{"id":"https://openalex.org/C2778858076","wikidata":"https://www.wikidata.org/wiki/Q5249539","display_name":"Decodes","level":3,"score":0.5054000020027161},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5034999847412109},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.5001999735832214},{"id":"https://openalex.org/C97931131","wikidata":"https://www.wikidata.org/wiki/Q5282087","display_name":"Discriminative model","level":2,"score":0.4652999937534332},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.44040000438690186},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4113999903202057},{"id":"https://openalex.org/C57273362","wikidata":"https://www.wikidata.org/wiki/Q576722","display_name":"Decoding methods","level":2,"score":0.3127000033855438},{"id":"https://openalex.org/C66746571","wikidata":"https://www.wikidata.org/wiki/Q1134833","display_name":"ENCODE","level":3,"score":0.3091999888420105},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3075999915599823},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.30169999599456787},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2992999851703644},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.2939000129699707},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.2800999879837036},{"id":"https://openalex.org/C2781238097","wikidata":"https://www.wikidata.org/wiki/Q175026","display_name":"Object (grammar)","level":2,"score":0.27959999442100525},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.2671999931335449},{"id":"https://openalex.org/C64876066","wikidata":"https://www.wikidata.org/wiki/Q5141226","display_name":"Cognitive neuroscience of visual object recognition","level":3,"score":0.26440000534057617},{"id":"https://openalex.org/C2776145597","wikidata":"https://www.wikidata.org/wiki/Q25339462","display_name":"Dropout (neural networks)","level":2,"score":0.25450000166893005}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.5753/jbcs.2026.5893","is_oa":true,"landing_page_url":"https://doi.org/10.5753/jbcs.2026.5893","pdf_url":null,"source":{"id":"https://openalex.org/S69801987","display_name":"Journal of the Brazilian Computer Society","issn_l":"0104-6500","issn":["0104-6500","1678-4804"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of the Brazilian Computer Society","raw_type":"journal-article"},{"id":"pmh:oai:arXiv.org:2504.20872","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2504.20872","pdf_url":"https://arxiv.org/pdf/2504.20872","source":{"id":"https://openalex.org/S4393918464","display_name":"ArXiv.org","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc-sa","license_id":"https://openalex.org/licenses/cc-by-nc-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"pmh:oai:HAL:hal-05607918v1","is_oa":true,"landing_page_url":"https://hal.science/hal-05607918","pdf_url":null,"source":{"id":"https://openalex.org/S4306402512","display_name":"HAL (Le Centre pour la Communication Scientifique Directe)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1294671590","host_organization_name":"Centre National de la Recherche Scientifique","host_organization_lineage":["https://openalex.org/I1294671590"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Journal of the Brazilian Computer Society, 2026, 32 (1), pp.750-765. ⟨10.5753/jbcs.202X.XXXXXX⟩","raw_type":"Journal articles"},{"id":"doi:10.48550/arxiv.2504.20872","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2504.20872","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":"article"}],"best_oa_location":{"id":"doi:10.5753/jbcs.2026.5893","is_oa":true,"landing_page_url":"https://doi.org/10.5753/jbcs.2026.5893","pdf_url":null,"source":{"id":"https://openalex.org/S69801987","display_name":"Journal of the Brazilian Computer Society","issn_l":"0104-6500","issn":["0104-6500","1678-4804"],"is_oa":true,"is_in_doaj":true,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Journal of the Brazilian Computer Society","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G2486662831","display_name":null,"funder_award_id":"2013/07375-","funder_id":"https://openalex.org/F4320320997","funder_display_name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de S\u00e3o Paulo"},{"id":"https://openalex.org/G5867257752","display_name":null,"funder_award_id":"2023/14427-8","funder_id":"https://openalex.org/F4320320997","funder_display_name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de S\u00e3o Paulo"},{"id":"https://openalex.org/G5990746028","display_name":null,"funder_award_id":"2013/07375-0","funder_id":"https://openalex.org/F4320320997","funder_display_name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de S\u00e3o Paulo"}],"funders":[{"id":"https://openalex.org/F4320320997","display_name":"Funda\u00e7\u00e3o de Amparo \u00e0 Pesquisa do Estado de S\u00e3o Paulo","ror":"https://ror.org/02ddkpn78"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Salient":[0],"Object":[1],"Detection":[2],"(SOD)":[3],"methods":[4,274],"can":[5,81],"locate":[6],"objects":[7],"that":[8,77,174],"stand":[9],"out":[10],"in":[11,20,47,275],"an":[12,115],"image,":[13],"assign":[14],"higher":[15],"values":[16],"to":[17,37,138,251],"their":[18],"pixels":[19,86],"a":[21,29,66,91,111,127],"saliency":[22],"map,":[23],"and":[24,143,227],"binarize":[25],"the":[26,78,147,171,184,188,193,217,235,241,245,249,258,261,265,268],"map":[27],"outputting":[28],"predicted":[30],"segmentation":[31],"mask.":[32],"A":[33],"recent":[34],"tendency":[35],"is":[36],"investigate":[38],"pre-trained":[39],"lightweight":[40,63,105,214],"models":[41,148,203],"rather":[42],"than":[43,104],"deep":[44],"neural":[45],"networks":[46,64,133,215,221,250,263],"SOD":[48,108,210],"tasks,":[49],"coping":[50],"with":[51,114,181,204,212,222],"applications":[52,151],"under":[53,152],"limited":[54],"computational":[55],"resources.":[56],"In":[57],"this":[58],"context,":[59],"we":[60,243],"have":[61],"investigated":[62],"using":[65],"methodology":[67],"named":[68],"Feature":[69],"Learning":[70],"from":[71,84,136,170,216],"Image":[72],"Markers":[73],"(FLIM),":[74],"which":[75],"assumes":[76],"encoder's":[79],"kernels":[80],"be":[82],"estimated":[83,121],"marker":[85],"on":[87,176],"discriminative":[88],"regions":[89],"of":[90,101,164,187,195,240,248,260,270],"few":[92],"representative":[93,140],"images.":[94],"This":[95],"work":[96],"proposes":[97],"flyweight":[98],"networks,":[99],"hundreds":[100],"times":[102],"lighter":[103],"models,":[106],"for":[107,122,150,207],"by":[109,126,225],"combining":[110],"FLIM":[112,132,202,220,229],"encoder":[113],"adaptive":[116,161,190,205],"decoder,":[117],"whose":[118,231],"weights":[119,194],"are":[120,134,166],"each":[123,196],"input":[124],"image":[125],"given":[128],"heuristic":[129,185],"function.":[130],"Such":[131],"trained":[135,224],"three":[137,213],"four":[139],"images":[141],"only":[142],"without":[144],"backpropagation,":[145,226],"making":[146],"suitable":[149],"labeled":[153,232],"data":[154],"constraints":[155],"as":[156],"well.":[157],"We":[158,200],"study":[159],"five":[160],"decoders;":[162],"two":[163,208,219],"them":[165],"introduced":[167],"here.":[168],"Differently":[169],"previous":[172],"ones":[173],"rely":[175],"one":[177,228,239],"neuron":[178,197],"per":[179,198],"pixel":[180],"shared":[182],"weights,":[183],"functions":[186],"new":[189,276],"decoders":[191,206,223],"estimate":[192],"pixel.":[199],"compare":[201],"challenging":[209],"tasks":[211],"state-of-the-art,":[218],"network":[230],"markers":[233],"define":[234],"decoder's":[236],"weights.":[237],"For":[238],"applications,":[242],"evaluate":[244],"generalization":[246],"ability":[247],"six":[252],"different":[253],"datasets.":[254],"The":[255],"experiments":[256],"demonstrate":[257],"advantages":[259],"proposed":[262],"over":[264],"baselines,":[266],"revealing":[267],"importance":[269],"further":[271],"investigating":[272],"such":[273],"applications.":[277]},"counts_by_year":[],"updated_date":"2026-05-21T06:26:12.895304","created_date":"2025-10-10T00:00:00"}