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Cited by 8Machine Learning and Data ClassificationAdversarial Robustness in Machine LearningAdvanced Neural Network Applications

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Learning from Noisy Labels with Coarse-to-Fine Sample Credibility Modeling

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Zhang, Boshen, Li, Yuxi, Tu, Yuanpeng, Peng, Jinlong, Wang, Yabiao, Wu, Cunlin, Xiao, Yang, Zhao, Cairong·2023·Lecture Notes in Computer Science
DOI 10.1007/978-3-031-25063-7_2
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  • For real videos, PhyStruct-Video relies on approximate geometry, materials and forces inferred from vision models and heuristics, which can introduce discrepancies between true and modeled dynamics.

    “For real videos, we rely on approximate geometry, materials and forces inferred from vision models and heuristics [38,39,40,51,53,54,56].This can introduce discrepancies between the true and modeled dynamics.”

    From PhyStruct-Video: Structured Physics State Spaces for Physically Consistent Video Generation (2025) · cites this paper as [40]

    The Introduction and Section 3.2 discuss learning from noisy labels and modeling sample credibility, not physical dynamics in real videos. Searches for approximate geometry, materials, forces, PhyStruct-Video, and discrepancies between true and modeled dynamics found no passage addressing the claim. (LLM, conf=0.78)

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Research works printed but not linked: 55

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Metadata

Authors
Zhang, B., Li, Y., Tu, Y., Peng, J., Wang, Y., Wu, C., Xiao, Y., & Zhao, C.
Affiliations
  • Tongji University (CN)
  • Huazhong University of Science and Technology (CN)
Corresponding author
Wang, Yabiao
ORCID
Venue
Lecture Notes in Computer Science
Publisher
Springer Nature Switzerland
Published
2023
Pages
21-38
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
DOI
10.1007/978-3-031-25063-7_2
OpenAlex
W4320893587
Cited by
8
References
0
Citations by year
2025: 4 · 2024: 2 · 2023: 2
FWCI
0.93
Type
conference paper
Language
en
Open access
Licence
Locations
  • Lecture notes in computer science (publishedVersion)
  • arXiv (Cornell University) (submittedVersion)
  • arXiv (Cornell University) (cc-by)
Topics
  • Physical Sciences › Computer Science › Artificial Intelligence › Machine Learning and Data Classification
  • Physical Sciences › Computer Science › Artificial Intelligence › Adversarial Robustness in Machine Learning
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
Keywords
label noise, label noise robustness, label correction, semantic noise, image classification, text recognition

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