VRL-IQA: Visual Representation Learning for Image Quality Assessment

Article Properties
  • DOI (url)
    10.1109/access.2023.3340266
  • Publication Date
    2024/01/01
  • Journal
    IEEE Access
  • Indian UGC (journal)
  • Refrences
    74
  • Muhammad Azeem Aslam School of Information Engineering, Xi’an Eurasia University, Xi’an, China ORCID (unauthenticated)
  • Xu Wei Changchun Institute of Optics, Fine Mechanics and Physics, Chinese Academy of Sciences, Changchun, China
  • Nisar Ahmed Department of Computer Engineering, University of Engineering and Technology, Lahore, Lahore, Pakistan ORCID (unauthenticated)
  • Gulshan Saleem Department of Computer Science, Lahore Garrison University, Lahore, Pakistan
  • Tuba Amin Department of Computer Science, Government College University Faisalabad, Faisalabad, Pakistan
  • Hui Caixue School of Information Engineering, Xi’an Eurasia University, Xi’an, China
Cite
Aslam, Muhammad Azeem, et al. “VRL-IQA: Visual Representation Learning for Image Quality Assessment”. IEEE Access, vol. 12, 2024, pp. 2458-73, https://doi.org/10.1109/access.2023.3340266.
Aslam, M. A., Wei, X., Ahmed, N., Saleem, G., Amin, T., & Caixue, H. (2024). VRL-IQA: Visual Representation Learning for Image Quality Assessment. IEEE Access, 12, 2458-2473. https://doi.org/10.1109/access.2023.3340266
Aslam MA, Wei X, Ahmed N, Saleem G, Amin T, Caixue H. VRL-IQA: Visual Representation Learning for Image Quality Assessment. IEEE Access. 2024;12:2458-73.
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Refrences
Title Journal Journal Categories Citations Publication Date
MixMAE: Mixed and masked autoencoder for efficient pretraining of hierarchical vision transformers 2022
ImageNet-21K pretraining for the masses 2021
A large-scale study of representation learning with the visual task adaptation benchmark 2019
Neural architecture search with reinforcement learning 2016
Unsupervised representation learning with deep convolutional generative adversarial networks 2015