{"id":"https://openalex.org/W3168889618","doi":"https://doi.org/10.1109/icme51207.2021.9428329","title":"Learnable Sampling 3D Convolution for Video Enhancement and Action Recognition","display_name":"Learnable Sampling 3D Convolution for Video Enhancement and Action Recognition","publication_year":2021,"publication_date":"2021-06-09","ids":{"openalex":"https://openalex.org/W3168889618","doi":"https://doi.org/10.1109/icme51207.2021.9428329","mag":"3168889618"},"language":"en","primary_location":{"id":"doi:10.1109/icme51207.2021.9428329","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme51207.2021.9428329","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Multimedia and Expo (ICME)","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/A5034954588","display_name":"Shuyang Gu","orcid":"https://orcid.org/0000-0003-4535-2280"},"institutions":[{"id":"https://openalex.org/I126520041","display_name":"University of Science and Technology of China","ror":"https://ror.org/04c4dkn09","country_code":"CN","type":"education","lineage":["https://openalex.org/I126520041","https://openalex.org/I19820366"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Shuyang Gu","raw_affiliation_strings":["University of Science and Technology of China"],"affiliations":[{"raw_affiliation_string":"University of Science and Technology of China","institution_ids":["https://openalex.org/I126520041"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5074566006","display_name":"Jianmin Bao","orcid":null},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Jianmin Bao","raw_affiliation_strings":["Microsoft Research Asia"],"affiliations":[{"raw_affiliation_string":"Microsoft Research Asia","institution_ids":["https://openalex.org/I4210113369"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5009733291","display_name":"Dong Chen","orcid":"https://orcid.org/0000-0002-7692-7529"},"institutions":[{"id":"https://openalex.org/I4210113369","display_name":"Microsoft Research Asia (China)","ror":"https://ror.org/0300m5276","country_code":"CN","type":"company","lineage":["https://openalex.org/I1290206253","https://openalex.org/I4210113369"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Dong Chen","raw_affiliation_strings":["Microsoft Research Asia"],"affiliations":[{"raw_affiliation_string":"Microsoft Research Asia","institution_ids":["https://openalex.org/I4210113369"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5034954588"],"corresponding_institution_ids":["https://openalex.org/I126520041"],"apc_list":null,"apc_paid":null,"fwci":0.1453,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.49608196,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":93},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11227","display_name":"Diabetic Foot Ulcer Assessment and Management","score":0.9958000183105469,"subfield":{"id":"https://openalex.org/subfields/2712","display_name":"Endocrinology, Diabetes and Metabolism"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T11227","display_name":"Diabetic Foot Ulcer Assessment and Management","score":0.9958000183105469,"subfield":{"id":"https://openalex.org/subfields/2712","display_name":"Endocrinology, Diabetes and Metabolism"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10531","display_name":"Advanced Vision and Imaging","score":0.995199978351593,"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":0.9926999807357788,"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":0.7583666443824768},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7358676195144653},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.7161028385162354},{"id":"https://openalex.org/keywords/computer-vision","display_name":"Computer vision","score":0.6046314835548401},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5599445700645447},{"id":"https://openalex.org/keywords/interpolation","display_name":"Interpolation (computer graphics)","score":0.5594823360443115},{"id":"https://openalex.org/keywords/sampling","display_name":"Sampling (signal processing)","score":0.5380686521530151},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4731617569923401},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4568125009536743},{"id":"https://openalex.org/keywords/fuse","display_name":"Fuse (electrical)","score":0.4322969615459442},{"id":"https://openalex.org/keywords/motion","display_name":"Motion (physics)","score":0.15277177095413208},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.12178903818130493},{"id":"https://openalex.org/keywords/filter","display_name":"Filter (signal processing)","score":0.09746474027633667}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7583666443824768},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7358676195144653},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.7161028385162354},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.6046314835548401},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5599445700645447},{"id":"https://openalex.org/C137800194","wikidata":"https://www.wikidata.org/wiki/Q11713455","display_name":"Interpolation (computer graphics)","level":3,"score":0.5594823360443115},{"id":"https://openalex.org/C140779682","wikidata":"https://www.wikidata.org/wiki/Q210868","display_name":"Sampling (signal processing)","level":3,"score":0.5380686521530151},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4731617569923401},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4568125009536743},{"id":"https://openalex.org/C141353440","wikidata":"https://www.wikidata.org/wiki/Q182221","display_name":"Fuse (electrical)","level":2,"score":0.4322969615459442},{"id":"https://openalex.org/C104114177","wikidata":"https://www.wikidata.org/wiki/Q79782","display_name":"Motion (physics)","level":2,"score":0.15277177095413208},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.12178903818130493},{"id":"https://openalex.org/C106131492","wikidata":"https://www.wikidata.org/wiki/Q3072260","display_name":"Filter (signal processing)","level":2,"score":0.09746474027633667},{"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/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"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/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icme51207.2021.9428329","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icme51207.2021.9428329","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2021 IEEE International Conference on Multimedia and Expo (ICME)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/11","display_name":"Sustainable cities and communities","score":0.5099999904632568}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W764651262","https://openalex.org/W1983364832","https://openalex.org/W2149516292","https://openalex.org/W2557227117","https://openalex.org/W2586480386","https://openalex.org/W2601564443","https://openalex.org/W2619947201","https://openalex.org/W2769654144","https://openalex.org/W2891446678","https://openalex.org/W2905346068","https://openalex.org/W2906021645","https://openalex.org/W2938390807","https://openalex.org/W2938555542","https://openalex.org/W2949258649","https://openalex.org/W2963091558","https://openalex.org/W2963093735","https://openalex.org/W2963524571","https://openalex.org/W2963811979","https://openalex.org/W2964251418","https://openalex.org/W2966926453","https://openalex.org/W2986833982","https://openalex.org/W3102015846","https://openalex.org/W6730015810","https://openalex.org/W6755013148","https://openalex.org/W6756811790","https://openalex.org/W6757491820","https://openalex.org/W6761835442","https://openalex.org/W6955071965"],"related_works":["https://openalex.org/W3000097931","https://openalex.org/W2354322770","https://openalex.org/W4237547500","https://openalex.org/W1570848052","https://openalex.org/W2373192430","https://openalex.org/W4239268388","https://openalex.org/W1537496349","https://openalex.org/W4243305035","https://openalex.org/W2379407973","https://openalex.org/W3088721469"],"abstract_inverted_index":{"A":[0],"key":[1],"challenge":[2],"in":[3,117],"video":[4,136,138,140],"enhancement":[5],"and":[6,50,121,142],"action":[7,143],"recognition":[8,144],"is":[9],"to":[10,57,70,88,92],"fuse":[11],"useful":[12],"information":[13],"from":[14],"neighboring":[15,24],"frames.":[16,100],"Recent":[17],"works":[18],"suggest":[19],"establishing":[20],"accurate":[21],"correspondences":[22],"between":[23],"frames":[25,56],"before":[26],"fusing":[27,51],"temporal":[28],"information.":[29],"However,":[30],"the":[31,37,59,72,127,146],"generated":[32],"results":[33],"heavily":[34],"depend":[35],"on":[36,62,95,135],"quality":[38],"of":[39,74,148],"correspondence":[40],"estimation.":[41],"This":[42],"paper":[43],"proposes":[44],"a":[45,67],"more":[46],"robust":[47],"solution:":[48],"sampling":[49,79,128],"multi-level":[52],"features":[53],"across":[54,99],"neighborhood":[55],"generate":[58],"results.":[60],"Based":[61],"this":[63],"idea,":[64],"we":[65],"introduce":[66],"new":[68,123],"module":[69],"improve":[71],"capability":[73],"3D":[75,80,89,114,119],"convolution,":[76,90],"namely,":[77],"learnable":[78,85],"convolution":[81,115],"(LS3D-Conv).":[82],"We":[83],"add":[84],"2D":[86],"offsets":[87,102],"aiming":[91],"sample":[93],"locations":[94],"spatial":[96],"feature":[97,131],"maps":[98],"The":[101,109,133],"can":[103,111],"be":[104],"learned":[105],"for":[106],"specific":[107],"tasks.":[108],"LS3D-Conv":[110],"flexibly":[112],"replace":[113],"layers":[116],"existing":[118],"networks":[120],"get":[122],"architectures,":[124],"which":[125],"learns":[126],"at":[129],"multiple":[130],"levels.":[132],"experiments":[134],"interpolation,":[137],"super-resolution,":[139],"denoising,":[141],"demonstrate":[145],"effectiveness":[147],"our":[149],"approach.":[150]},"counts_by_year":[{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}