iBet uBet web content aggregator. Adding the entire web to your favor.
iBet uBet web content aggregator. Adding the entire web to your favor.



Link to original content: https://api.openalex.org/works/doi:10.1109/CVPRW.2016.57
{"id":"https://openalex.org/W2963391479","doi":"https://doi.org/10.1109/cvprw.2016.57","title":"Deep End2End Voxel2Voxel Prediction","display_name":"Deep End2End Voxel2Voxel Prediction","publication_year":2016,"publication_date":"2016-06-01","ids":{"openalex":"https://openalex.org/W2963391479","doi":"https://doi.org/10.1109/cvprw.2016.57","mag":"2963391479"},"language":"en","primary_location":{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvprw.2016.57","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},"type":"article","type_crossref":"proceedings-article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1511.06681","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5016319243","display_name":"Du Tran","orcid":"https://orcid.org/0000-0001-9673-7194"},"institutions":[{"id":"https://openalex.org/I4210166639","display_name":"Dartmouth Hospital","ror":"https://ror.org/02j3qj605","country_code":"GB","type":"healthcare","lineage":["https://openalex.org/I4210166639"]},{"id":"https://openalex.org/I107672454","display_name":"Dartmouth College","ror":"https://ror.org/049s0rh22","country_code":"US","type":"education","lineage":["https://openalex.org/I107672454"]},{"id":"https://openalex.org/I2252078561","display_name":"Meta (Israel)","ror":"https://ror.org/02388em19","country_code":"IL","type":"company","lineage":["https://openalex.org/I2252078561","https://openalex.org/I4210114444"]}],"countries":["GB","IL","US"],"is_corresponding":false,"raw_author_name":"Du Tran","raw_affiliation_strings":["Dartmouth College","Facebook AI Research"],"affiliations":[{"raw_affiliation_string":"Dartmouth College","institution_ids":["https://openalex.org/I4210166639","https://openalex.org/I107672454"]},{"raw_affiliation_string":"Facebook AI Research","institution_ids":["https://openalex.org/I2252078561"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044186270","display_name":"Lubomir Bourdev","orcid":null},"institutions":[{"id":"https://openalex.org/I134446601","display_name":"Berkeley College","ror":"https://ror.org/02xewxa75","country_code":"US","type":"education","lineage":["https://openalex.org/I134446601"]},{"id":"https://openalex.org/I95457486","display_name":"University of California, Berkeley","ror":"https://ror.org/01an7q238","country_code":"US","type":"education","lineage":["https://openalex.org/I95457486"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lubomir Bourdev","raw_affiliation_strings":["UC Berkeley"],"affiliations":[{"raw_affiliation_string":"UC Berkeley","institution_ids":["https://openalex.org/I134446601","https://openalex.org/I95457486"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5089960673","display_name":"Rob Fergus","orcid":null},"institutions":[{"id":"https://openalex.org/I2252078561","display_name":"Meta (Israel)","ror":"https://ror.org/02388em19","country_code":"IL","type":"company","lineage":["https://openalex.org/I2252078561","https://openalex.org/I4210114444"]}],"countries":["IL"],"is_corresponding":false,"raw_author_name":"Rob Fergus","raw_affiliation_strings":["Facebook AI Research"],"affiliations":[{"raw_affiliation_string":"Facebook AI Research","institution_ids":["https://openalex.org/I2252078561"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5082736347","display_name":"Lorenzo Torresani","orcid":null},"institutions":[{"id":"https://openalex.org/I4210166639","display_name":"Dartmouth Hospital","ror":"https://ror.org/02j3qj605","country_code":"GB","type":"healthcare","lineage":["https://openalex.org/I4210166639"]},{"id":"https://openalex.org/I107672454","display_name":"Dartmouth College","ror":"https://ror.org/049s0rh22","country_code":"US","type":"education","lineage":["https://openalex.org/I107672454"]}],"countries":["GB","US"],"is_corresponding":false,"raw_author_name":"Lorenzo Torresani","raw_affiliation_strings":["Dartmouth College"],"affiliations":[{"raw_affiliation_string":"Dartmouth College","institution_ids":["https://openalex.org/I4210166639","https://openalex.org/I107672454"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5054437548","display_name":"Manohar Paluri","orcid":null},"institutions":[{"id":"https://openalex.org/I2252078561","display_name":"Meta (Israel)","ror":"https://ror.org/02388em19","country_code":"IL","type":"company","lineage":["https://openalex.org/I2252078561","https://openalex.org/I4210114444"]}],"countries":["IL"],"is_corresponding":false,"raw_author_name":"Manohar Paluri","raw_affiliation_strings":["Facebook AI Research"],"affiliations":[{"raw_affiliation_string":"Facebook AI Research","institution_ids":["https://openalex.org/I2252078561"]}]}],"institution_assertions":[],"countries_distinct_count":3,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":4.468,"has_fulltext":true,"fulltext_origin":"ngrams","cited_by_count":109,"citation_normalized_percentile":{"value":0.999863,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T10531","display_name":"Stereo Vision and Depth Estimation","score":0.9994,"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/T10531","display_name":"Stereo Vision and Depth Estimation","score":0.9994,"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 Action Recognition and Pose Estimation","score":0.9986,"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":"Single Image Super-Resolution Techniques","score":0.9966,"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/optical-flow","display_name":"Optical flow","score":0.5541983},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep Learning","score":0.542662},{"id":"https://openalex.org/keywords/video-enhancement","display_name":"Video Enhancement","score":0.536051},{"id":"https://openalex.org/keywords/visual-servoing","display_name":"Visual Servoing","score":0.522801},{"id":"https://openalex.org/keywords/depth-estimation","display_name":"Depth Estimation","score":0.519967},{"id":"https://openalex.org/keywords/tweaking","display_name":"Tweaking","score":0.49215788}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.87114954},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7531375},{"id":"https://openalex.org/C34736171","wikidata":"https://www.wikidata.org/wiki/Q918333","display_name":"Preprocessor","level":2,"score":0.70625114},{"id":"https://openalex.org/C54170458","wikidata":"https://www.wikidata.org/wiki/Q663554","display_name":"Voxel","level":2,"score":0.5682925},{"id":"https://openalex.org/C155542232","wikidata":"https://www.wikidata.org/wiki/Q736111","display_name":"Optical flow","level":3,"score":0.5541983},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5468079},{"id":"https://openalex.org/C89600930","wikidata":"https://www.wikidata.org/wiki/Q1423946","display_name":"Segmentation","level":2,"score":0.53980637},{"id":"https://openalex.org/C2780200862","wikidata":"https://www.wikidata.org/wiki/Q4453309","display_name":"Tweaking","level":2,"score":0.49215788},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.47746873},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.449496},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.42361867},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.33928204},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.16179612},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"is_oa":false,"landing_page_url":"https://doi.org/10.1109/cvprw.2016.57","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false},{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/1511.06681","pdf_url":"https://arxiv.org/pdf/1511.06681","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false}],"best_oa_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/1511.06681","pdf_url":"https://arxiv.org/pdf/1511.06681","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":["Cornell University"],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false},"sustainable_development_goals":[{"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9","score":0.49}],"grants":[],"datasets":[],"versions":[],"referenced_works_count":34,"referenced_works":["https://openalex.org/W1513100184","https://openalex.org/W1522734439","https://openalex.org/W1686810756","https://openalex.org/W1849277567","https://openalex.org/W1903029394","https://openalex.org/W1923404803","https://openalex.org/W1930528368","https://openalex.org/W2010399676","https://openalex.org/W2016053056","https://openalex.org/W2039125691","https://openalex.org/W2063153269","https://openalex.org/W2068611653","https://openalex.org/W2097117768","https://openalex.org/W2097342496","https://openalex.org/W2099806072","https://openalex.org/W2102605133","https://openalex.org/W2119799051","https://openalex.org/W2123477621","https://openalex.org/W2129524746","https://openalex.org/W2131747574","https://openalex.org/W2134670479","https://openalex.org/W2155541015","https://openalex.org/W2156303437","https://openalex.org/W2163605009","https://openalex.org/W2165095705","https://openalex.org/W2171740948","https://openalex.org/W24089286","https://openalex.org/W2951234442","https://openalex.org/W2962835968","https://openalex.org/W2963317244","https://openalex.org/W2963542991","https://openalex.org/W3141200356","https://openalex.org/W4294375521","https://openalex.org/W764651262"],"related_works":["https://openalex.org/W4323777416","https://openalex.org/W4232914159","https://openalex.org/W2970801625","https://openalex.org/W2968428037","https://openalex.org/W2762689969","https://openalex.org/W2521174057","https://openalex.org/W2505446473","https://openalex.org/W2286567226","https://openalex.org/W2152101763","https://openalex.org/W1981619058"],"abstract_inverted_index":{"Over":[0],"the":[1,13,30,40,63,131,138],"last":[2],"few":[3],"years":[4],"deep":[5,57,110],"learning":[6],"methods":[7,205],"have":[8,27],"emerged":[9],"as":[10,62],"one":[11],"of":[12,32,42,50,91,130,179],"most":[14,24],"prominent":[15],"approaches":[16],"for":[17],"video":[18,33,155,162,175,208],"analysis.":[19],"However,":[20],"so":[21],"far":[22],"their":[23,182],"successful":[25,81],"applications":[26],"been":[28],"in":[29,68,77,206],"area":[31],"classification":[34],"and":[35,93,161,181,200],"detection,":[36],"i.e.,":[37,122],"problems":[38,170],"involving":[39],"prediction":[41],"a":[43,48,73,109,125],"single":[44],"class":[45],"label":[46],"or":[47,97],"handful":[49],"output":[51,124],"variables":[52],"per":[53],"video.":[54,132],"Furthermore,":[55],"while":[56],"networks":[58,166],"are":[59,171],"commonly":[60],"recognized":[61],"best":[64],"models":[65],"to":[66,79,116,118,123,145,188,198],"use":[67],"these":[69,105,169,207],"domains,":[70],"there":[71],"is":[72],"widespread":[74],"perception":[75],"that":[76,137],"order":[78],"yield":[80],"results":[82,148],"they":[83,193],"often":[84],"require":[85,186],"time-consuming":[86],"architecture":[87,113,141],"search,":[88],"manual":[89],"tweaking":[90],"parameters":[92],"computationally":[94,203],"intensive":[95],"preprocessing":[96,180],"post-processing":[98,187],"methods.":[99],"In":[100],"this":[101],"paper":[102],"we":[103,135],"challenge":[104],"views":[106],"by":[107],"presenting":[108],"3D":[111],"convolutional":[112],"trained":[114,172],"end":[115,117],"perform":[119],"voxel-level":[120],"prediction,":[121],"variable":[126],"at":[127],"every":[128],"voxel":[129],"Most":[133],"importantly,":[134],"show":[136],"same":[139],"exact":[140],"can":[142],"be":[143],"used":[144],"achieve":[146,189],"competitive":[147],"on":[149,168],"three":[150,165],"widely":[151],"different":[152],"voxel-prediction":[153],"tasks:":[154],"semantic":[156],"segmentation,":[157],"optical":[158],"flow":[159],"estimation,":[160],"coloring.":[163],"The":[164],"learned":[167],"from":[173],"raw":[174],"without":[176],"any":[177],"form":[178],"outputs":[183],"do":[184],"not":[185],"outstanding":[190],"performance.":[191],"Thus,":[192],"offer":[194],"an":[195],"efficient":[196],"alternative":[197],"traditional":[199],"much":[201],"more":[202],"expensive":[204],"domains.":[209]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W2963391479","counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":16},{"year":2021,"cited_by_count":11},{"year":2020,"cited_by_count":12},{"year":2019,"cited_by_count":19},{"year":2018,"cited_by_count":21},{"year":2017,"cited_by_count":20},{"year":2016,"cited_by_count":4}],"updated_date":"2024-10-05T10:09:32.181902","created_date":"2019-07-30"}