Full metadata record
DC Field | Value | Language |
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dc.contributor.author | Bae, Jinwoo | - |
dc.contributor.author | Hwang, Kyumin | - |
dc.contributor.author | Im, Sunghoon | - |
dc.date.accessioned | 2024-01-10T17:10:11Z | - |
dc.date.available | 2024-01-10T17:10:11Z | - |
dc.date.created | 2024-01-02 | - |
dc.date.issued | 2024-04 | - |
dc.identifier.issn | 0162-8828 | - |
dc.identifier.uri | http://hdl.handle.net/20.500.11750/47602 | - |
dc.description.abstract | Monocular depth estimation has been widely studied, and significant improvements in performance have been recently reported. However, most previous works are evaluated on a few benchmark datasets, such as KITTI datasets, and none of the works provide an in-depth analysis of the generalization performance of monocular depth estimation. In this paper, we deeply investigate the various backbone networks ( |
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dc.language | English | - |
dc.publisher | Institute of Electrical and Electronics Engineers | - |
dc.title | A Study on the Generality of Neural Network Structures for Monocular Depth Estimation | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/TPAMI.2023.3332407 | - |
dc.identifier.wosid | 001180891600019 | - |
dc.identifier.scopusid | 2-s2.0-85177094424 | - |
dc.identifier.bibliographicCitation | IEEE Transactions on Pattern Analysis and Machine Intelligence, v.46, no.4, pp.2224 - 2238 | - |
dc.description.isOpenAccess | FALSE | - |
dc.subject.keywordAuthor | Monocular depth estimation | - |
dc.subject.keywordAuthor | Out-of-Distribution | - |
dc.subject.keywordAuthor | Generalization | - |
dc.subject.keywordAuthor | Transformer | - |
dc.citation.endPage | 2238 | - |
dc.citation.number | 4 | - |
dc.citation.startPage | 2224 | - |
dc.citation.title | IEEE Transactions on Pattern Analysis and Machine Intelligence | - |
dc.citation.volume | 46 | - |
dc.description.journalRegisteredClass | scie | - |
dc.description.journalRegisteredClass | scopus | - |
dc.relation.journalResearchArea | Computer Science; Engineering | - |
dc.relation.journalWebOfScienceCategory | Computer Science, Artificial Intelligence; Engineering, Electrical & Electronic | - |
dc.type.docType | Article | - |
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