{"id":"https://openalex.org/W3090253482","doi":"https://doi.org/10.1109/icra40945.2020.9196510","title":"Segmenting 2K-Videos at 36.5 FPS with 24.3 GFLOPs: Accurate and Lightweight Realtime Semantic Segmentation Network","display_name":"Segmenting 2K-Videos at 36.5 FPS with 24.3 GFLOPs: Accurate and Lightweight Realtime Semantic Segmentation Network","publication_year":2020,"publication_date":"2020-05-01","ids":{"openalex":"https://openalex.org/W3090253482","doi":"https://doi.org/10.1109/icra40945.2020.9196510","mag":"3090253482"},"language":"en","primary_location":{"id":"doi:10.1109/icra40945.2020.9196510","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra40945.2020.9196510","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","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/A5016313614","display_name":"Dokwan Oh","orcid":"https://orcid.org/0000-0001-9139-8699"},"institutions":[{"id":"https://openalex.org/I2250650973","display_name":"Samsung (South Korea)","ror":"https://ror.org/04w3jy968","country_code":"KR","type":"company","lineage":["https://openalex.org/I2250650973"]}],"countries":["KR"],"is_corresponding":true,"raw_author_name":"Dokwan Oh","raw_affiliation_strings":["System LSI Division, Samsung Electronics, Korea"],"affiliations":[{"raw_affiliation_string":"System LSI Division, Samsung Electronics, Korea","institution_ids":["https://openalex.org/I2250650973"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110223580","display_name":"Daehyun Ji","orcid":null},"institutions":[{"id":"https://openalex.org/I2250650973","display_name":"Samsung (South Korea)","ror":"https://ror.org/04w3jy968","country_code":"KR","type":"company","lineage":["https://openalex.org/I2250650973"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Daehyun Ji","raw_affiliation_strings":["System LSI Division, Samsung Electronics, Korea"],"affiliations":[{"raw_affiliation_string":"System LSI Division, Samsung Electronics, Korea","institution_ids":["https://openalex.org/I2250650973"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033581723","display_name":"Cheolhun Jang","orcid":null},"institutions":[{"id":"https://openalex.org/I2250650973","display_name":"Samsung (South Korea)","ror":"https://ror.org/04w3jy968","country_code":"KR","type":"company","lineage":["https://openalex.org/I2250650973"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Cheolhun Jang","raw_affiliation_strings":["System LSI Division, Samsung Electronics, Korea"],"affiliations":[{"raw_affiliation_string":"System LSI Division, Samsung Electronics, Korea","institution_ids":["https://openalex.org/I2250650973"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5043342026","display_name":"Yoonsuk Hyun","orcid":"https://orcid.org/0000-0001-5047-7139"},"institutions":[{"id":"https://openalex.org/I2250650973","display_name":"Samsung (South Korea)","ror":"https://ror.org/04w3jy968","country_code":"KR","type":"company","lineage":["https://openalex.org/I2250650973"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Yoonsuk Hyun","raw_affiliation_strings":["System LSI Division, Samsung Electronics, Korea"],"affiliations":[{"raw_affiliation_string":"System LSI Division, Samsung Electronics, Korea","institution_ids":["https://openalex.org/I2250650973"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112569875","display_name":"Hong Bae","orcid":null},"institutions":[{"id":"https://openalex.org/I2250650973","display_name":"Samsung (South Korea)","ror":"https://ror.org/04w3jy968","country_code":"KR","type":"company","lineage":["https://openalex.org/I2250650973"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Hong S. Bae","raw_affiliation_strings":["System LSI Division, Samsung Electronics, Korea"],"affiliations":[{"raw_affiliation_string":"System LSI Division, Samsung Electronics, Korea","institution_ids":["https://openalex.org/I2250650973"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112605788","display_name":"Sungju Hwang","orcid":null},"institutions":[{"id":"https://openalex.org/I157485424","display_name":"Korea Advanced Institute of Science and Technology","ror":"https://ror.org/05apxxy63","country_code":"KR","type":"education","lineage":["https://openalex.org/I157485424"]}],"countries":["KR"],"is_corresponding":false,"raw_author_name":"Sungju Hwang","raw_affiliation_strings":["Korea Advanced Institute of Science and Technology, Korea"],"affiliations":[{"raw_affiliation_string":"Korea Advanced Institute of Science and Technology, Korea","institution_ids":["https://openalex.org/I157485424"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5016313614"],"corresponding_institution_ids":["https://openalex.org/I2250650973"],"apc_list":null,"apc_paid":null,"fwci":0.8832,"has_fulltext":false,"cited_by_count":9,"citation_normalized_percentile":{"value":0.76840046,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"3153","last_page":"3160"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10036","display_name":"Advanced Neural Network Applications","score":0.9998000264167786,"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":0.9998000264167786,"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/T10627","display_name":"Advanced Image and Video Retrieval Techniques","score":0.9983999729156494,"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/T11105","display_name":"Advanced Image Processing Techniques","score":0.9983000159263611,"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/computer-science","display_name":"Computer science","score":0.8390110731124878},{"id":"https://openalex.org/keywords/flops","display_name":"FLOPS","score":0.7950190305709839},{"id":"https://openalex.org/keywords/encoder","display_name":"Encoder","score":0.6664019823074341},{"id":"https://openalex.org/keywords/bottleneck","display_name":"Bottleneck","score":0.6535014510154724},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6352471709251404},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.6058933138847351},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5978018641471863},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5809042453765869},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.5365170836448669},{"id":"https://openalex.org/keywords/speedup","display_name":"Speedup","score":0.5074898600578308},{"id":"https://openalex.org/keywords/image-segmentation","display_name":"Image segmentation","score":0.4366539418697357},{"id":"https://openalex.org/keywords/parallel-computing","display_name":"Parallel computing","score":0.4264151453971863},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.3813334107398987},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.21108326315879822},{"id":"https://openalex.org/keywords/embedded-system","display_name":"Embedded system","score":0.14357951283454895}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8390110731124878},{"id":"https://openalex.org/C3826847","wikidata":"https://www.wikidata.org/wiki/Q188768","display_name":"FLOPS","level":2,"score":0.7950190305709839},{"id":"https://openalex.org/C118505674","wikidata":"https://www.wikidata.org/wiki/Q42586063","display_name":"Encoder","level":2,"score":0.6664019823074341},{"id":"https://openalex.org/C2780513914","wikidata":"https://www.wikidata.org/wiki/Q18210350","display_name":"Bottleneck","level":2,"score":0.6535014510154724},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6352471709251404},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.6058933138847351},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5978018641471863},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5809042453765869},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5365170836448669},{"id":"https://openalex.org/C68339613","wikidata":"https://www.wikidata.org/wiki/Q1549489","display_name":"Speedup","level":2,"score":0.5074898600578308},{"id":"https://openalex.org/C124504099","wikidata":"https://www.wikidata.org/wiki/Q56933","display_name":"Image segmentation","level":3,"score":0.4366539418697357},{"id":"https://openalex.org/C173608175","wikidata":"https://www.wikidata.org/wiki/Q232661","display_name":"Parallel computing","level":1,"score":0.4264151453971863},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.3813334107398987},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.21108326315879822},{"id":"https://openalex.org/C149635348","wikidata":"https://www.wikidata.org/wiki/Q193040","display_name":"Embedded system","level":1,"score":0.14357951283454895},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icra40945.2020.9196510","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icra40945.2020.9196510","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Robotics and Automation (ICRA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.7200000286102295,"display_name":"Sustainable cities and communities","id":"https://metadata.un.org/sdg/11"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":104,"referenced_works":["https://openalex.org/W582134693","https://openalex.org/W1512309675","https://openalex.org/W1650736245","https://openalex.org/W1677182931","https://openalex.org/W1686810756","https://openalex.org/W1690739335","https://openalex.org/W1745334888","https://openalex.org/W1821462560","https://openalex.org/W1836465849","https://openalex.org/W1861492603","https://openalex.org/W1901129140","https://openalex.org/W1903029394","https://openalex.org/W1915250530","https://openalex.org/W1923697677","https://openalex.org/W1971410590","https://openalex.org/W1994259434","https://openalex.org/W1996901117","https://openalex.org/W2022508996","https://openalex.org/W2037227137","https://openalex.org/W2095844239","https://openalex.org/W2103328396","https://openalex.org/W2104408738","https://openalex.org/W2116877738","https://openalex.org/W2118984444","https://openalex.org/W2125215748","https://openalex.org/W2150609397","https://openalex.org/W2155893237","https://openalex.org/W2172612767","https://openalex.org/W2183341477","https://openalex.org/W2194775991","https://openalex.org/W2233116163","https://openalex.org/W2274287116","https://openalex.org/W2279098554","https://openalex.org/W2287418003","https://openalex.org/W2340897893","https://openalex.org/W2419448466","https://openalex.org/W2473131906","https://openalex.org/W2525167219","https://openalex.org/W2535516436","https://openalex.org/W2549139847","https://openalex.org/W2560023338","https://openalex.org/W2560540238","https://openalex.org/W2577918148","https://openalex.org/W2581531918","https://openalex.org/W2609701267","https://openalex.org/W2611425310","https://openalex.org/W2612445135","https://openalex.org/W2739879705","https://openalex.org/W2740036778","https://openalex.org/W2787091153","https://openalex.org/W2883780447","https://openalex.org/W2886934227","https://openalex.org/W2921495890","https://openalex.org/W2949117887","https://openalex.org/W2950094539","https://openalex.org/W2950967261","https://openalex.org/W2962772649","https://openalex.org/W2962835968","https://openalex.org/W2963000224","https://openalex.org/W2963125010","https://openalex.org/W2963163009","https://openalex.org/W2963418739","https://openalex.org/W2963446712","https://openalex.org/W2963476860","https://openalex.org/W2963516811","https://openalex.org/W2963918968","https://openalex.org/W2964039533","https://openalex.org/W2964059111","https://openalex.org/W2964118293","https://openalex.org/W2964144363","https://openalex.org/W2964217532","https://openalex.org/W2964309882","https://openalex.org/W2964350391","https://openalex.org/W2987175876","https://openalex.org/W3137695714","https://openalex.org/W3184458996","https://openalex.org/W4293406525","https://openalex.org/W4297775537","https://openalex.org/W4297813944","https://openalex.org/W6617145748","https://openalex.org/W6622659578","https://openalex.org/W6637373629","https://openalex.org/W6637551013","https://openalex.org/W6638523607","https://openalex.org/W6638667902","https://openalex.org/W6639102338","https://openalex.org/W6639824700","https://openalex.org/W6640295612","https://openalex.org/W6674793120","https://openalex.org/W6682137105","https://openalex.org/W6694260854","https://openalex.org/W6695314431","https://openalex.org/W6717372056","https://openalex.org/W6725543821","https://openalex.org/W6731990857","https://openalex.org/W6736942654","https://openalex.org/W6737324727","https://openalex.org/W6737419302","https://openalex.org/W6737664043","https://openalex.org/W6748481559","https://openalex.org/W6749781174","https://openalex.org/W6753767121","https://openalex.org/W6754123467","https://openalex.org/W6763485134"],"related_works":["https://openalex.org/W2058965144","https://openalex.org/W2164382479","https://openalex.org/W2146343568","https://openalex.org/W98480971","https://openalex.org/W2150291671","https://openalex.org/W2013643406","https://openalex.org/W2027972911","https://openalex.org/W2157978810","https://openalex.org/W2597809628","https://openalex.org/W2891987081"],"abstract_inverted_index":{"We":[0,149],"propose":[1],"a":[2],"fast":[3],"and":[4,30,57,74,99,175],"lightweight":[5,60,167],"end-to-end":[6],"convolutional":[7],"network":[8],"architecture":[9,95],"for":[10,44],"real-time":[11],"segmentation":[12],"of":[13,153,172],"high":[14],"resolution":[15],"videos,":[16],"NfS-SegNet,":[17],"that":[18,110],"can":[19],"segement":[20],"2K-videos":[21],"at":[22],"36.5":[23],"FPS":[24],"with":[25,46,84,96,119,155],"24.3":[26],"GFLOPS.":[27],"This":[28],"speed":[29,45],"computation-efficiency":[31],"is":[32,42,104,113],"due":[33],"to":[34,123,138],"following":[35],"reasons:":[36],"1)":[37],"The":[38,89],"encoder":[39,98],"network,":[40],"NfS-Net,":[41],"optimized":[43],"simple":[47],"building":[48],"blocks":[49],"without":[50],"memory-heavy":[51],"operations":[52],"such":[53,63],"as":[54,64],"depthwise":[55],"convolutions,":[56],"outperforms":[58],"state-of-the-art":[59,164],"CNN":[61],"architectures":[62],"SqueezeNet":[65],"[2],":[66],"Mo-":[67],"bileNet":[68],"v1":[69,76],"[3]":[70],"&":[71,78],"v2":[72,79],"[4]":[73],"ShuffleNet":[75],"[5]":[77],"[6]":[80],"on":[81,106,143,160],"image":[82,147],"classification":[83],"significantly":[85],"higher":[86],"speed.":[87,176],"2)":[88],"NfS-":[90],"SegNet":[91],"has":[92],"an":[93],"asymmetric":[94],"deeper":[97],"shallow":[100],"decoder,":[101],"whose":[102],"design":[103],"based":[105],"our":[107],"empirical":[108],"finding":[109],"the":[111,114,124,135,144,151,156],"decoder":[112],"main":[115],"bottleneck":[116],"in":[117,170],"computation":[118],"relatively":[120],"small":[121],"contribution":[122],"final":[125],"performance.":[126],"3)":[127],"Our":[128],"novel":[129],"uncertainty-aware":[130],"knowledge":[131,141],"distillation":[132],"method":[133],"guides":[134],"teacher":[136],"model":[137],"focus":[139],"its":[140],"transfer":[142],"most":[145],"difficult":[146],"regions.":[148],"validate":[150],"performance":[152,165],"NfS-SegNet":[154],"CITYSCAPE":[157],"[1]":[158],"benchmark,":[159],"which":[161],"it":[162],"achieves":[163],"among":[166],"segementation":[168],"models":[169],"terms":[171],"both":[173],"accuracy":[174]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":3},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":1}],"updated_date":"2026-04-03T16:38:21.277918","created_date":"2025-10-10T00:00:00"}