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Cited by 98Advanced Neural Network ApplicationsDomain Adaptation and Few-Shot LearningAdvanced Image and Video Retrieval Techniques

Deep Nets: What have They Ever Done for Vision?

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Yuille, Alan L., Liu, Chenxi·2021·International Journal of Computer Vision
DOI 10.1007/s11263-020-01405-z
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  • The substantial variability of occluders, characterized by their numbers, geometries, positions, and poses, leads to exponential complexity in scene data distribution.

    “To be specific, the substantial variability of occluders, characterized by their numbers, geometries, positions, and poses, leads to exponential complexity in scene data distribution [49, 18].”

    From Learning Environment-Aware Affordance for 3D Articulated Object Manipulation under Occlusions (2023) · cites this paper as [49]

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Cited by

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Authors
Yuille, A. L., & Liu, C.
Affiliations
Johns Hopkins University (US)
Corresponding author
Liu, Chenxi
ORCID
Venue
International Journal of Computer Vision
Publisher
Springer Science and Business Media LLC
Published
2020-11-27
Volume
129
Issue
3
Pages
781-802
ISSN
  • 0920-5691 (ISSN-L)
  • 1573-1405
DOI
10.1007/s11263-020-01405-z
OpenAlex
W3108291730
Cited by
98
References
14
Citations by year
2026: 1 · 2025: 10 · 2024: 14 · 2023: 12 · 2022: 22 · 2021: 14 · 2020: 12 · 2019: 11 · 2018: 2
FWCI
5.63
Type
journal article
Language
en
Open access
Licence
Locations
  • International Journal of Computer Vision (publishedVersion)
  • arXiv (Cornell University) (submittedVersion, other-oa)
  • DSpace@MIT (Massachusetts Institute of Technology) (submittedVersion, cc-by-nc)
  • arXiv (Cornell University)
Funders
  • National Science Foundation (CCF-1231216)
  • Office of Naval Research (N00014-15-1-2356)
Topics
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Computer Science › Artificial Intelligence › Domain Adaptation and Few-Shot Learning
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Image and Video Retrieval Techniques
Keywords
deep neural network, computer vision, combinatorial explosion, human visual system, performance evaluation, benchmarking, scene understanding, deep learning limitations

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