English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT

Released

Conference Paper

Improving Feature Stability during Upsampling - Spectral Artifacts and the Importance of Spatial Context

MPS-Authors
/persons/resource/persons180612

Keuper,  Margret
Computer Vision and Machine Learning, MPI for Informatics, Max Planck Society;

External Resource

https://rdcu.be/d2ndn
(Publisher version)

Fulltext (restricted access)
There are currently no full texts shared for your IP range.
Fulltext (public)

2311.17524v2.pdf
(Preprint), 36MB

Supplementary Material (public)
There is no public supplementary material available
Citation

Agnihotri, S., Grabinski, J., & Keuper, M. (2024). Improving Feature Stability during Upsampling - Spectral Artifacts and the Importance of Spatial Context. In A. Leonardis, E. Ricci, S. Roth, O. Russakovsky, T. Sattler, & G. Varol (Eds.), Computer Vision -- ECCV 2024 (pp. 357-376). Berlin: Springer. doi:10.1007/978-3-031-73636-0_21.


Cite as: https://hdl.handle.net/21.11116/0000-000F-D878-7
Abstract
There is no abstract available