{"id":"https://openalex.org/W4416750059","doi":"https://doi.org/10.1109/iros60139.2025.11247662","title":"SLTNet: Efficient Event-based Semantic Segmentation with Spike-driven Lightweight Transformer-based Networks","display_name":"SLTNet: Efficient Event-based Semantic Segmentation with Spike-driven Lightweight Transformer-based Networks","publication_year":2025,"publication_date":"2025-10-19","ids":{"openalex":"https://openalex.org/W4416750059","doi":"https://doi.org/10.1109/iros60139.2025.11247662"},"language":null,"primary_location":{"id":"doi:10.1109/iros60139.2025.11247662","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros60139.2025.11247662","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":"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/A5008281642","display_name":"Xianlei Long","orcid":"https://orcid.org/0000-0002-2623-8768"},"institutions":[{"id":"https://openalex.org/I158842170","display_name":"Chongqing University","ror":"https://ror.org/023rhb549","country_code":"CN","type":"education","lineage":["https://openalex.org/I158842170"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Xianlei Long","raw_affiliation_strings":["Chongqing University,College of Computer Science,Chongqing,China,400044"],"affiliations":[{"raw_affiliation_string":"Chongqing University,College of Computer Science,Chongqing,China,400044","institution_ids":["https://openalex.org/I158842170"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104247505","display_name":"Xiaxin Zhu","orcid":"https://orcid.org/0000-0003-0849-3885"},"institutions":[{"id":"https://openalex.org/I158842170","display_name":"Chongqing University","ror":"https://ror.org/023rhb549","country_code":"CN","type":"education","lineage":["https://openalex.org/I158842170"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaxin Zhu","raw_affiliation_strings":["Chongqing University,College of Computer Science,Chongqing,China,400044"],"affiliations":[{"raw_affiliation_string":"Chongqing University,College of Computer Science,Chongqing,China,400044","institution_ids":["https://openalex.org/I158842170"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5041522379","display_name":"Fangming Guo","orcid":"https://orcid.org/0000-0002-0093-9398"},"institutions":[{"id":"https://openalex.org/I158842170","display_name":"Chongqing University","ror":"https://ror.org/023rhb549","country_code":"CN","type":"education","lineage":["https://openalex.org/I158842170"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fangming Guo","raw_affiliation_strings":["Chongqing University,College of Computer Science,Chongqing,China,400044"],"affiliations":[{"raw_affiliation_string":"Chongqing University,College of Computer Science,Chongqing,China,400044","institution_ids":["https://openalex.org/I158842170"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5060121586","display_name":"Wanyi Zhang","orcid":"https://orcid.org/0000-0002-3952-0826"},"institutions":[{"id":"https://openalex.org/I158842170","display_name":"Chongqing University","ror":"https://ror.org/023rhb549","country_code":"CN","type":"education","lineage":["https://openalex.org/I158842170"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Wanyi Zhang","raw_affiliation_strings":["Chongqing University,College of Computer Science,Chongqing,China,400044"],"affiliations":[{"raw_affiliation_string":"Chongqing University,College of Computer Science,Chongqing,China,400044","institution_ids":["https://openalex.org/I158842170"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5007926867","display_name":"Qingyi Gu","orcid":"https://orcid.org/0000-0001-8332-5350"},"institutions":[{"id":"https://openalex.org/I4210112150","display_name":"Institute of Automation","ror":"https://ror.org/022c3hy66","country_code":"CN","type":"facility","lineage":["https://openalex.org/I19820366","https://openalex.org/I4210112150"]},{"id":"https://openalex.org/I19820366","display_name":"Chinese Academy of Sciences","ror":"https://ror.org/034t30j35","country_code":"CN","type":"funder","lineage":["https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Qingyi Gu","raw_affiliation_strings":["Chinese Academy of Sciences,Institute of Automation,Beijing,China,100190"],"affiliations":[{"raw_affiliation_string":"Chinese Academy of Sciences,Institute of Automation,Beijing,China,100190","institution_ids":["https://openalex.org/I4210112150","https://openalex.org/I19820366"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100408399","display_name":"Chao Chen","orcid":"https://orcid.org/0000-0003-2094-9734"},"institutions":[{"id":"https://openalex.org/I158842170","display_name":"Chongqing University","ror":"https://ror.org/023rhb549","country_code":"CN","type":"education","lineage":["https://openalex.org/I158842170"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chao Chen","raw_affiliation_strings":["Chongqing University,College of Computer Science,Chongqing,China,400044"],"affiliations":[{"raw_affiliation_string":"Chongqing University,College of Computer Science,Chongqing,China,400044","institution_ids":["https://openalex.org/I158842170"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5101868530","display_name":"Fuqiang Gu","orcid":"https://orcid.org/0000-0002-3408-982X"},"institutions":[{"id":"https://openalex.org/I158842170","display_name":"Chongqing University","ror":"https://ror.org/023rhb549","country_code":"CN","type":"education","lineage":["https://openalex.org/I158842170"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Fuqiang Gu","raw_affiliation_strings":["Chongqing University,College of Computer Science,Chongqing,China,400044"],"affiliations":[{"raw_affiliation_string":"Chongqing University,College of Computer Science,Chongqing,China,400044","institution_ids":["https://openalex.org/I158842170"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":7,"corresponding_author_ids":["https://openalex.org/A5008281642"],"corresponding_institution_ids":["https://openalex.org/I158842170"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.37084734,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"4331","last_page":"4338"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.6323999762535095,"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/T10036","display_name":"Advanced Neural Network Applications","score":0.6323999762535095,"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/T10502","display_name":"Advanced Memory and Neural Computing","score":0.2784999907016754,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/T12808","display_name":"Ferroelectric and Negative Capacitance Devices","score":0.01590000092983246,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"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/segmentation","display_name":"Segmentation","score":0.6934000253677368},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.569100022315979},{"id":"https://openalex.org/keywords/energy-consumption","display_name":"Energy consumption","score":0.4934999942779541},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.47209998965263367},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4603999853134155},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.3982999920845032},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.38749998807907104},{"id":"https://openalex.org/keywords/transformer","display_name":"Transformer","score":0.3763999938964844},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.37599998712539673}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8203999996185303},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.6934000253677368},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6248999834060669},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.569100022315979},{"id":"https://openalex.org/C2780165032","wikidata":"https://www.wikidata.org/wiki/Q16869822","display_name":"Energy consumption","level":2,"score":0.4934999942779541},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.47209998965263367},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4603999853134155},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.3982999920845032},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.38749998807907104},{"id":"https://openalex.org/C66322947","wikidata":"https://www.wikidata.org/wiki/Q11658","display_name":"Transformer","level":3,"score":0.3763999938964844},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.37599998712539673},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.36800000071525574},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.351500004529953},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.34310001134872437},{"id":"https://openalex.org/C186370098","wikidata":"https://www.wikidata.org/wiki/Q442787","display_name":"Energy (signal processing)","level":2,"score":0.3375000059604645},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.323199987411499},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.31450000405311584},{"id":"https://openalex.org/C123657996","wikidata":"https://www.wikidata.org/wiki/Q12271","display_name":"Architecture","level":2,"score":0.3057999908924103},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.30320000648498535},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.30149999260902405},{"id":"https://openalex.org/C2781122975","wikidata":"https://www.wikidata.org/wiki/Q16928266","display_name":"Semantic feature","level":2,"score":0.29899999499320984},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.29820001125335693},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.29809999465942383},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.2955000102519989},{"id":"https://openalex.org/C184337299","wikidata":"https://www.wikidata.org/wiki/Q1437428","display_name":"Semantics (computer science)","level":2,"score":0.28940001130104065},{"id":"https://openalex.org/C48372109","wikidata":"https://www.wikidata.org/wiki/Q3913","display_name":"Binary number","level":2,"score":0.288100004196167},{"id":"https://openalex.org/C2779990667","wikidata":"https://www.wikidata.org/wiki/Q5953266","display_name":"Hybrid neural network","level":3,"score":0.2603999972343445},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.25859999656677246},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2515000104904175},{"id":"https://openalex.org/C2776151529","wikidata":"https://www.wikidata.org/wiki/Q3045304","display_name":"Object detection","level":3,"score":0.25049999356269836}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/iros60139.2025.11247662","is_oa":false,"landing_page_url":"https://doi.org/10.1109/iros60139.2025.11247662","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":[{"id":"https://openalex.org/F4320321543","display_name":"China Postdoctoral Science Foundation","ror":"https://ror.org/0426zh255"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W2130459697","https://openalex.org/W2194775991","https://openalex.org/W2752782242","https://openalex.org/W2963516811","https://openalex.org/W2965867763","https://openalex.org/W3174847012","https://openalex.org/W3188843700","https://openalex.org/W3196426681","https://openalex.org/W4221147914","https://openalex.org/W4306827030","https://openalex.org/W4385732733","https://openalex.org/W4393105211","https://openalex.org/W4394989660","https://openalex.org/W4401415228","https://openalex.org/W4401416199","https://openalex.org/W4402352346","https://openalex.org/W4404238289","https://openalex.org/W4404819792","https://openalex.org/W4405784978","https://openalex.org/W4405785275"],"related_works":[],"abstract_inverted_index":{"Event-based":[0],"semantic":[1,75,90],"segmentation":[2,35],"has":[3],"great":[4],"potential":[5],"in":[6],"autonomous":[7],"driving":[8],"and":[9,25,47,54,144,159,169,178],"robotics":[10],"due":[11],"to":[12,87,98],"the":[13,42,94,100,129,134],"advantages":[14],"of":[15,133],"event":[16],"cameras,":[17],"such":[18],"as":[19],"high":[20,39],"dynamic":[21],"range,":[22],"low":[23,26,130],"latency,":[24],"power":[27],"cost.":[28],"Unfortunately,":[29],"current":[30],"artificial":[31],"neural":[32],"network":[33],"(ANN)-based":[34],"methods":[36,154],"suffer":[37],"from":[38],"computational":[40],"demands,":[41],"requirements":[43],"for":[44,73],"image":[45],"frames,":[46],"massive":[48],"energy":[49,131,167],"consumption,":[50],"limiting":[51],"their":[52],"efficiency":[53],"application":[55],"on":[56,81,117,142],"resource-constrained":[57],"edge/mobile":[58],"platforms.":[59],"To":[60],"address":[61],"these":[62,118],"problems,":[63],"we":[64,105],"introduce":[65],"SLTNet,":[66],"a":[67,123],"Spike-driven":[68],"Lightweight":[69],"Transformer-based":[70],"Network":[71,137],"designed":[72],"event-based":[74],"segmentation.":[76],"Specifically,":[77],"SLTNet":[78,121,149],"is":[79,176],"built":[80],"efficient":[82],"spike-driven":[83,108],"convolution":[84],"blocks":[85,110],"(SCBs)":[86],"extract":[88],"rich":[89],"features":[91],"while":[92,127],"reducing":[93],"model\u2019s":[95],"parameters.":[96],"Then,":[97],"enhance":[99],"long-range":[101],"contextual":[102],"feature":[103],"interaction,":[104],"propose":[106],"novel":[107],"transformer":[109],"(STBs)":[111],"with":[112,163],"binary":[113],"mask":[114],"operations.":[115],"Based":[116],"basic":[119],"blocks,":[120],"employs":[122],"high-efficiency":[124],"single-branch":[125],"architecture":[126],"maintaining":[128],"consumption":[132,168],"Spiking":[135],"Neural":[136],"(SNN).":[138],"Finally,":[139],"extensive":[140],"experiments":[141],"DDD17":[143],"DSEC-Semantic":[145],"datasets":[146],"demonstrate":[147],"that":[148],"outperforms":[150],"state-of-the-art":[151],"(SOTA)":[152],"SNN-based":[153],"by":[155],"at":[156,180],"most":[157],"9.06%":[158],"9.39%":[160],"mIoU,":[161],"respectively,":[162],"extremely":[164],"4.58\u00d7":[165],"lower":[166],"114":[170],"FPS":[171],"inference":[172],"speed.":[173],"Our":[174],"code":[175],"open-sourced":[177],"available":[179],"https://github.com/longxianlei/SLTNet-v1.0.":[181]},"counts_by_year":[],"updated_date":"2026-04-09T08:11:56.329763","created_date":"2025-11-28T00:00:00"}