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:10.48550/ARXIV.2210.00411
{"id":"https://openalex.org/W4302305460","doi":"https://doi.org/10.48550/arxiv.2210.00411","title":"Self-Supervised Monocular Depth Estimation: Solving the Edge-Fattening Problem","display_name":"Self-Supervised Monocular Depth Estimation: Solving the Edge-Fattening Problem","publication_year":2022,"publication_date":"2022-01-01","ids":{"openalex":"https://openalex.org/W4302305460","doi":"https://doi.org/10.48550/arxiv.2210.00411"},"language":"en","primary_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2210.00411","pdf_url":null,"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":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false},"type":"preprint","type_crossref":"posted-content","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/abs/2210.00411","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100387354","display_name":"Xingyu Chen","orcid":"https://orcid.org/0000-0003-1164-9537"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Xingyu","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100666490","display_name":"Ruonan Zhang","orcid":"https://orcid.org/0000-0003-0030-6758"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Ruonan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104082980","display_name":"Ji Jiang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Jiang, Ji","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100322552","display_name":"Yan Wang","orcid":"https://orcid.org/0000-0001-9324-4191"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Yan","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100447691","display_name":"Ge Li","orcid":"https://orcid.org/0000-0003-0140-0949"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Ge","raw_affiliation_strings":[],"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5103873055","display_name":"Thomas H. Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Thomas H.","raw_affiliation_strings":[],"affiliations":[]}],"institution_assertions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":0,"max":61},"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"primary_topic":{"id":"https://openalex.org/T13114","display_name":"Autofocusing in Microscopy and Photography","score":0.9939,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T13114","display_name":"Autofocusing in Microscopy and Photography","score":0.9939,"subfield":{"id":"https://openalex.org/subfields/2214","display_name":"Media Technology"},"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/T10531","display_name":"Stereo Vision and Depth Estimation","score":0.9885,"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/T10638","display_name":"Digital Image Correlation Techniques","score":0.9873,"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/margin","display_name":"Margin (machine learning)","score":0.65008014},{"id":"https://openalex.org/keywords/monocular-depth-estimation","display_name":"Monocular Depth Estimation","score":0.645617},{"id":"https://openalex.org/keywords/depth-estimation","display_name":"Depth Estimation","score":0.590997},{"id":"https://openalex.org/keywords/autofocusing","display_name":"Autofocusing","score":0.53647},{"id":"https://openalex.org/keywords/inverse-modeling","display_name":"Inverse Modeling","score":0.534896},{"id":"https://openalex.org/keywords/3d-shape-measurement","display_name":"3D Shape Measurement","score":0.517484},{"id":"https://openalex.org/keywords/monocular","display_name":"Monocular","score":0.4957944}],"concepts":[{"id":"https://openalex.org/C162307627","wikidata":"https://www.wikidata.org/wiki/Q204833","display_name":"Enhanced Data Rates for GSM Evolution","level":2,"score":0.7270808},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.7202657},{"id":"https://openalex.org/C774472","wikidata":"https://www.wikidata.org/wiki/Q6760393","display_name":"Margin (machine learning)","level":2,"score":0.65008014},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6354642},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.60180587},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.5738363},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.5450239},{"id":"https://openalex.org/C65909025","wikidata":"https://www.wikidata.org/wiki/Q1945033","display_name":"Monocular","level":2,"score":0.4957944},{"id":"https://openalex.org/C112789634","wikidata":"https://www.wikidata.org/wiki/Q18207010","display_name":"False positives and false negatives","level":3,"score":0.45028785},{"id":"https://openalex.org/C31972630","wikidata":"https://www.wikidata.org/wiki/Q844240","display_name":"Computer vision","level":1,"score":0.41815224},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.38770348},{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.37844923},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.33367687},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10943785},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2210.00411","pdf_url":null,"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":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false},{"is_oa":false,"landing_page_url":"https://api.datacite.org/dois/10.48550/arxiv.2210.00411","pdf_url":null,"source":{"id":"https://openalex.org/S4393179698","display_name":"DataCite API","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I4210145204","host_organization_name":"DataCite","host_organization_lineage":["https://openalex.org/I4210145204"],"host_organization_lineage_names":["DataCite"],"type":"metadata"},"license":null,"license_id":null,"version":null}],"best_oa_location":{"is_oa":true,"landing_page_url":"https://arxiv.org/abs/2210.00411","pdf_url":null,"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":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false},"sustainable_development_goals":[],"grants":[],"datasets":[],"versions":[],"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W4292605373","https://openalex.org/W4226316650","https://openalex.org/W3123215897","https://openalex.org/W2997457842","https://openalex.org/W2951146195","https://openalex.org/W2183246718","https://openalex.org/W2153600354","https://openalex.org/W2099261052","https://openalex.org/W1973412793","https://openalex.org/W1557094818"],"abstract_inverted_index":{"Self-supervised":[0],"monocular":[1],"depth":[2],"estimation":[3],"(MDE)":[4],"models":[5],"universally":[6],"suffer":[7],"from":[8,94],"the":[9,34,42,51,80,88,96,102,109,114],"notorious":[10],"edge-fattening":[11,44,83],"issue.":[12,45],"Triplet":[13],"loss,":[14],"as":[15],"a":[16,65,141,151],"widespread":[17],"metric":[18],"learning":[19],"strategy,":[20],"has":[21],"largely":[22],"succeeded":[23],"in":[24,38,55],"many":[25],"computer":[26],"vision":[27],"applications.":[28],"In":[29],"this":[30],"paper,":[31],"we":[32,63,86],"redesign":[33],"patch-based":[35],"triplet":[36,53,129],"loss":[37,54,130],"MDE":[39,56],"to":[40,71,75,150],"alleviate":[41],"ubiquitous":[43],"We":[46],"show":[47,113],"two":[48,118],"drawbacks":[49],"of":[50,82,116,154],"raw":[52],"and":[57,91,127],"demonstrate":[58],"our":[59,134],"problem-driven":[60],"redesigns.":[61],"First,":[62],"present":[64],"min.":[66],"operator":[67],"based":[68],"strategy":[69],"applied":[70],"all":[72,137],"negative":[73],"samples,":[74],"prevent":[76],"well-performing":[77],"negatives":[78],"sheltering":[79],"error":[81],"negatives.":[84,110],"Second,":[85],"split":[87],"anchor-positive":[89],"distance":[90,93],"anchor-negative":[92],"within":[95],"original":[97],"triplet,":[98],"which":[99],"directly":[100],"optimizes":[101],"positives":[103],"without":[104],"any":[105],"mutual":[106],"effect":[107],"with":[108],"Extensive":[111],"experiments":[112],"combination":[115],"these":[117],"small":[119],"redesigns":[120],"can":[121],"achieve":[122],"unprecedented":[123],"results:":[124],"Our":[125],"powerful":[126],"versatile":[128],"not":[131],"only":[132],"makes":[133],"model":[135],"outperform":[136],"previous":[138],"SoTA":[139],"by":[140],"large":[142,152],"margin,":[143],"but":[144],"also":[145],"provides":[146],"substantial":[147],"performance":[148],"boosts":[149],"number":[153],"existing":[155],"models,":[156],"while":[157],"introducing":[158],"no":[159],"extra":[160],"inference":[161],"computation":[162],"at":[163],"all.":[164]},"cited_by_api_url":"https://api.openalex.org/works?filter=cites:W4302305460","counts_by_year":[],"updated_date":"2024-10-23T01:16:36.840816","created_date":"2022-10-06"}