{"id":"https://openalex.org/W2887240357","doi":"https://doi.org/10.1145/3240508.3240660","title":"Adaptive Temporal Encoding Network for Video Instance-level Human Parsing","display_name":"Adaptive Temporal Encoding Network for Video Instance-level Human Parsing","publication_year":2018,"publication_date":"2018-10-15","ids":{"openalex":"https://openalex.org/W2887240357","doi":"https://doi.org/10.1145/3240508.3240660","mag":"2887240357"},"language":"en","primary_location":{"id":"doi:10.1145/3240508.3240660","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3240508.3240660","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 26th ACM international conference on Multimedia","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/A5100764004","display_name":"Qixian Zhou","orcid":"https://orcid.org/0009-0007-2582-6991"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Qixian Zhou","raw_affiliation_strings":["Sun Yat-sen University, GuangZhou, China"],"affiliations":[{"raw_affiliation_string":"Sun Yat-sen University, GuangZhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5047878798","display_name":"Xiaodan Liang","orcid":"https://orcid.org/0000-0003-3213-3062"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Xiaodan Liang","raw_affiliation_strings":["Carnegie Mellon University, Pittsburgh, PA, USA"],"affiliations":[{"raw_affiliation_string":"Carnegie Mellon University, Pittsburgh, PA, USA","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089953417","display_name":"Ke Gong","orcid":"https://orcid.org/0000-0002-8036-2348"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Ke Gong","raw_affiliation_strings":["Sun Yat-sen University, GuangZhou, China"],"affiliations":[{"raw_affiliation_string":"Sun Yat-sen University, GuangZhou, China","institution_ids":["https://openalex.org/I157773358"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5100412937","display_name":"Liang Lin","orcid":"https://orcid.org/0000-0003-2248-3755"},"institutions":[{"id":"https://openalex.org/I157773358","display_name":"Sun Yat-sen University","ror":"https://ror.org/0064kty71","country_code":"CN","type":"education","lineage":["https://openalex.org/I157773358"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Liang Lin","raw_affiliation_strings":["Sun Yat-sen University, GuangZhou, China"],"affiliations":[{"raw_affiliation_string":"Sun Yat-sen University, GuangZhou, China","institution_ids":["https://openalex.org/I157773358"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5100764004"],"corresponding_institution_ids":["https://openalex.org/I157773358"],"apc_list":null,"apc_paid":null,"fwci":2.1936,"has_fulltext":false,"cited_by_count":46,"citation_normalized_percentile":{"value":0.91238996,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1527","last_page":"1535"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11714","display_name":"Multimodal Machine Learning 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/T11714","display_name":"Multimodal Machine Learning 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/T10036","display_name":"Advanced Neural Network Applications","score":0.9995999932289124,"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/T10812","display_name":"Human Pose and Action Recognition","score":0.9995999932289124,"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":0.8591480255126953},{"id":"https://openalex.org/keywords/parsing","display_name":"Parsing","score":0.7878604531288147},{"id":"https://openalex.org/keywords/encoding","display_name":"Encoding (memory)","score":0.6753553152084351},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.623090922832489},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.609519362449646},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5634492635726929},{"id":"https://openalex.org/keywords/segmentation","display_name":"Segmentation","score":0.5372258424758911},{"id":"https://openalex.org/keywords/benchmark","display_name":"Benchmark (surveying)","score":0.5162630081176758},{"id":"https://openalex.org/keywords/frame","display_name":"Frame (networking)","score":0.4935115575790405},{"id":"https://openalex.org/keywords/consistency","display_name":"Consistency (knowledge bases)","score":0.4368639290332794},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.3874286711215973},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.36009669303894043}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8591480255126953},{"id":"https://openalex.org/C186644900","wikidata":"https://www.wikidata.org/wiki/Q194152","display_name":"Parsing","level":2,"score":0.7878604531288147},{"id":"https://openalex.org/C125411270","wikidata":"https://www.wikidata.org/wiki/Q18653","display_name":"Encoding (memory)","level":2,"score":0.6753553152084351},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.623090922832489},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.609519362449646},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5634492635726929},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.5372258424758911},{"id":"https://openalex.org/C185798385","wikidata":"https://www.wikidata.org/wiki/Q1161707","display_name":"Benchmark (surveying)","level":2,"score":0.5162630081176758},{"id":"https://openalex.org/C126042441","wikidata":"https://www.wikidata.org/wiki/Q1324888","display_name":"Frame (networking)","level":2,"score":0.4935115575790405},{"id":"https://openalex.org/C2776436953","wikidata":"https://www.wikidata.org/wiki/Q5163215","display_name":"Consistency (knowledge bases)","level":2,"score":0.4368639290332794},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.3874286711215973},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.36009669303894043},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C13280743","wikidata":"https://www.wikidata.org/wiki/Q131089","display_name":"Geodesy","level":1,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"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:10.1145/3240508.3240660","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3240508.3240660","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 26th ACM international conference on Multimedia","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":48,"referenced_works":["https://openalex.org/W764651262","https://openalex.org/W1195044660","https://openalex.org/W1496571393","https://openalex.org/W1903029394","https://openalex.org/W1941318923","https://openalex.org/W1943388055","https://openalex.org/W1962739028","https://openalex.org/W1973255633","https://openalex.org/W2012960460","https://openalex.org/W2074621908","https://openalex.org/W2104408738","https://openalex.org/W2113708607","https://openalex.org/W2121339428","https://openalex.org/W2124592697","https://openalex.org/W2157331557","https://openalex.org/W2197046994","https://openalex.org/W2204578866","https://openalex.org/W2236679687","https://openalex.org/W2272561391","https://openalex.org/W2326050853","https://openalex.org/W2402395722","https://openalex.org/W2412782625","https://openalex.org/W2470139095","https://openalex.org/W2497039038","https://openalex.org/W2519055561","https://openalex.org/W2552900565","https://openalex.org/W2557993245","https://openalex.org/W2560474170","https://openalex.org/W2566030665","https://openalex.org/W2598104261","https://openalex.org/W2598915960","https://openalex.org/W2599765304","https://openalex.org/W2604445072","https://openalex.org/W2610147486","https://openalex.org/W2630837129","https://openalex.org/W2799256316","https://openalex.org/W2962891704","https://openalex.org/W2963019093","https://openalex.org/W2963315052","https://openalex.org/W2963318290","https://openalex.org/W2963441581","https://openalex.org/W2963650529","https://openalex.org/W2963722138","https://openalex.org/W2963758239","https://openalex.org/W2963866581","https://openalex.org/W2963978393","https://openalex.org/W2964303162","https://openalex.org/W2964309882"],"related_works":["https://openalex.org/W2378211422","https://openalex.org/W4321353415","https://openalex.org/W2745001401","https://openalex.org/W2130974462","https://openalex.org/W2028665553","https://openalex.org/W2086519370","https://openalex.org/W4246352526","https://openalex.org/W2121910908","https://openalex.org/W915438175","https://openalex.org/W4230315250"],"abstract_inverted_index":{"Beyond":[0],"the":[1,15,83,94,112,133,138,160,205,215,233],"existing":[2],"single-person":[3],"and":[4,34,63,98,110,173,189,222,244,250],"multiple-person":[5],"human":[6,25,96,100,237],"parsing":[7,26,85,97,238],"tasks":[8],"in":[9,198],"static":[10],"images,":[11],"this":[12],"paper":[13],"makes":[14],"first":[16,77,234],"attempt":[17],"to":[18,81,118,124,144,158],"investigate":[19],"a":[20,48,79,103,149,183,194,223],"more":[21,39],"realistic":[22],"video":[23,199,218,235],"instance-level":[24,84,99,162,236,249],"that":[27,55],"simultaneously":[28],"segments":[29],"out":[30],"each":[31,36,88],"person":[32],"instance":[33,37],"parses":[35],"into":[38,102],"fine-grained":[40],"parts":[41],"(\\eg,":[42],"head,":[43],"leg,":[44],"dress).":[45],"We":[46],"introduce":[47],"novel":[49],"Adaptive":[50],"Temporal":[51],"Encoding":[52],"Network":[53],"(ATEN)":[54],"alternatively":[56,165],"performs":[57],"temporal":[58,127,146,174],"encoding":[59,175],"among":[60,177],"key":[61,73,89,130,152,178],"frames":[62,70,122,172,247],"flow-guided":[64,113],"feature":[65,114,168],"propagation":[66,115,169],"from":[67],"other":[68,134],"consecutive":[69,121],"between":[71,108,170,186],"two":[72],"frames.":[74,131],"Specifically,":[75],"ATEN":[76,136,181],"incorporates":[78],"Parsing-RCNN":[80],"produce":[82],"result":[86],"for":[87],"frame,":[90],"which":[91,154,192,231],"integrates":[92],"both":[93],"global":[95],"segmentation":[101,201,219],"unified":[104],"model.":[105],"To":[106,203],"balance":[107,185],"accuracy":[109,188],"efficiency,":[111,191],"is":[116,193,232],"used":[117,157],"directly":[119],"parse":[120],"according":[123],"their":[125],"identified":[126],"consistency":[128],"with":[129,248],"On":[132],"hand,":[135],"leverages":[137],"convolution":[139],"gated":[140],"recurrent":[141],"units":[142],"(convGRU)":[143],"exploit":[145],"changes":[147],"over":[148,245],"series":[150],"of":[151,207,241],"frames,":[153,179],"are":[155,212],"further":[156],"facilitate":[159],"frame-level":[161,187],"parsing.":[163],"By":[164],"performing":[166],"direct":[167],"consistent":[171],"network":[176],"our":[180,208],"achieves":[182],"good":[184],"time":[190],"common":[195],"crucial":[196],"problem":[197],"object":[200],"research.":[202],"demonstrate":[204],"superiority":[206],"ATEN,":[209],"extensive":[210],"experiments":[211],"conducted":[213],"on":[214],"most":[216],"popular":[217],"benchmark":[220],"(DAVIS)":[221],"newly":[224],"collected":[225],"Video":[226],"Instance-level":[227],"Parsing":[228],"(VIP)":[229],"dataset,":[230],"dataset":[239],"comprised":[240],"404":[242],"sequences":[243],"20k":[246],"pixel-wise":[251],"annotations.":[252]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":4},{"year":2023,"cited_by_count":10},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":9},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":6}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}