{"id":"https://openalex.org/W7128491345","doi":"https://doi.org/10.18420/giljt2026_04","title":"Enhancing BlazeBlock with Convolutional Block Attention and self-attention on agricultural vision applications","display_name":"Enhancing BlazeBlock with Convolutional Block Attention and self-attention on agricultural vision applications","publication_year":2026,"publication_date":"2026-01-01","ids":{"openalex":"https://openalex.org/W7128491345","doi":"https://doi.org/10.18420/giljt2026_04"},"language":"en","primary_location":{"id":"pmh:doi:10.18420/giljt2026_04","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Text/Conference Paper"},"type":"article","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.18420/giljt2026_04","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5061813638","display_name":"Arnab Ghosh Chowdhury","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Chowdhury, Arnab Ghosh","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5125510274","display_name":"Amos Smith","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Smith, Amos","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5125549480","display_name":"Martin Atzmueller","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Atzmueller, Martin","raw_affiliation_strings":[],"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5061813638"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.34112732,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"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/T11448","display_name":"Face recognition and analysis","score":0.753000020980835,"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/T11448","display_name":"Face recognition and analysis","score":0.753000020980835,"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":0.11680000275373459,"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/T10616","display_name":"Smart Agriculture and AI","score":0.025699999183416367,"subfield":{"id":"https://openalex.org/subfields/1110","display_name":"Plant Science"},"field":{"id":"https://openalex.org/fields/11","display_name":"Agricultural and Biological Sciences"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.7461000084877014},{"id":"https://openalex.org/keywords/block","display_name":"Block (permutation group theory)","score":0.708899974822998},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.6266999840736389},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.5008999705314636},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.45660001039505005},{"id":"https://openalex.org/keywords/channel","display_name":"Channel (broadcasting)","score":0.37450000643730164},{"id":"https://openalex.org/keywords/pixel","display_name":"Pixel","score":0.3734000027179718},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.3652999997138977},{"id":"https://openalex.org/keywords/contextual-image-classification","display_name":"Contextual image classification","score":0.35019999742507935}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7918000221252441},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.7461000084877014},{"id":"https://openalex.org/C2777210771","wikidata":"https://www.wikidata.org/wiki/Q4927124","display_name":"Block (permutation group theory)","level":2,"score":0.708899974822998},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6983000040054321},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.6266999840736389},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.5008999705314636},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.48350000381469727},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.45660001039505005},{"id":"https://openalex.org/C127162648","wikidata":"https://www.wikidata.org/wiki/Q16858953","display_name":"Channel (broadcasting)","level":2,"score":0.37450000643730164},{"id":"https://openalex.org/C160633673","wikidata":"https://www.wikidata.org/wiki/Q355198","display_name":"Pixel","level":2,"score":0.3734000027179718},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.3652999997138977},{"id":"https://openalex.org/C75294576","wikidata":"https://www.wikidata.org/wiki/Q5165192","display_name":"Contextual image classification","level":3,"score":0.35019999742507935},{"id":"https://openalex.org/C159620131","wikidata":"https://www.wikidata.org/wiki/Q1938983","display_name":"Spatial analysis","level":2,"score":0.3479999899864197},{"id":"https://openalex.org/C186967261","wikidata":"https://www.wikidata.org/wiki/Q5082128","display_name":"Mobile device","level":2,"score":0.3398999869823456},{"id":"https://openalex.org/C5339829","wikidata":"https://www.wikidata.org/wiki/Q1425977","display_name":"Machine vision","level":2,"score":0.33820000290870667},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.3379000127315521},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.3377000093460083},{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.31859999895095825},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.30160000920295715},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.2987000048160553},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.29429998993873596},{"id":"https://openalex.org/C2779227376","wikidata":"https://www.wikidata.org/wiki/Q6505497","display_name":"Layer (electronics)","level":2,"score":0.2903999984264374},{"id":"https://openalex.org/C31510193","wikidata":"https://www.wikidata.org/wiki/Q1192553","display_name":"Facial recognition system","level":3,"score":0.28459998965263367},{"id":"https://openalex.org/C9417928","wikidata":"https://www.wikidata.org/wiki/Q1070689","display_name":"Image processing","level":3,"score":0.28130000829696655},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.27549999952316284},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.2687999904155731}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:doi:10.18420/giljt2026_04","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Text/Conference Paper"},{"id":"doi:10.18420/giljt2026_04","is_oa":true,"landing_page_url":"https://doi.org/10.18420/giljt2026_04","pdf_url":null,"source":{"id":"https://openalex.org/S7407052918","display_name":"Gesellschaft f\u00fcr Informatik (GI)","issn_l":null,"issn":[],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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-journal"}],"best_oa_location":{"id":"doi:10.18420/giljt2026_04","is_oa":true,"landing_page_url":"https://doi.org/10.18420/giljt2026_04","pdf_url":null,"source":{"id":"https://openalex.org/S7407052918","display_name":"Gesellschaft f\u00fcr Informatik (GI)","issn_l":null,"issn":[],"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"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":"article-journal"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/2","display_name":"Zero hunger","score":0.7323107719421387}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Convolutional":[0,57,144],"neural":[1],"networks":[2],"(CNNs)":[3],"perform":[4],"a":[5,20,85,90,122,158],"pivotal":[6],"role":[7],"in":[8,42,52,98,105,179,211],"agricultural":[9,213],"vision":[10,50,214],"applications.":[11],"The":[12],"CNN-based":[13],"BlazeBlock":[14,38,140,178,210],"has":[15],"been":[16],"previously":[17],"proposed":[18,208],"as":[19],"building":[21],"block":[22],"of":[23,37,66,75,175,206],"lightweight":[24],"BlazeFace":[25],"models":[26],"for":[27,191,200],"face":[28],"detection":[29],"tasks":[30],"on":[31,94,110,188],"mobile":[32,46],"GPUs.":[33],"Thus,":[34],"the":[35,56,63,73,95,111,118,128,143,153,173,176,204,207],"utilization":[36],"is":[39,102,114],"often":[40],"advantageous":[41],"order":[43],"to":[44,88,116],"offer":[45],"and":[47,78,157,182,194,197],"edge":[48],"computing":[49],"services":[51],"precision":[53],"farming.":[54],"Moreover,":[55,107],"Block":[58,145],"Attention":[59],"Module":[60,149],"(CBAM)":[61],"enhances":[62],"representational":[64],"power":[65],"CNNs":[67],"by":[68,151],"emphasizing":[69],"important":[70],"features":[71],"through":[72],"use":[74],"channel":[76,155],"attention":[77,80,92,131,156],"spatial":[79,91,99,130,160],"modules.":[81],"Although":[82],"CBAM":[83,154],"uses":[84,142],"convolutional":[86],"layer":[87],"generate":[89],"map":[93],"feature":[96,112],"descriptor":[97,113],"attention,":[100],"it":[101],"spatially":[103],"local":[104],"nature.":[106],"employing":[108],"self-attention":[109,124],"beneficial":[115],"learn":[117],"global":[119],"representation.":[120],"Hence,":[121],"MobileViT-based":[123,159],"module":[125],"can":[126,163],"substitute":[127],"prior":[129],"module.":[132],"In":[133],"this":[134],"paper,":[135],"we":[136],"present":[137],"an":[138],"attention-based":[139,177],"that":[141],"with":[146],"Spatial":[147],"Self-Attention":[148],"(CBwSSAM)":[150],"harnessing":[152],"self-attention.":[161],"CBwSSAM":[162],"be":[164],"integrated":[165],"into":[166],"any":[167],"CNN":[168],"architecture":[169],"seamlessly.":[170],"We":[171],"analyze":[172],"effectiveness":[174],"image":[180,192],"classification":[181,193],"semantic":[183,201],"segmentation.":[184],"Our":[185],"evaluation":[186],"results":[187],"six":[189],"datasets":[190,199],"transfer":[195],"learning,":[196],"three":[198],"segmentation":[202],"exemplify":[203],"efficacy":[205],"enhanced":[209],"such":[212],"application":[215],"contexts.":[216]},"counts_by_year":[],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2026-02-11T00:00:00"}
