Axi Niu
Orcid: 0000-0001-5238-9917
  According to our database1,
  Axi Niu
  authored at least 24 papers
  between 2021 and 2026.
  
  
Collaborative distances:
Collaborative distances:
Timeline
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On csauthors.net:
Bibliography
  2026
Inter-view dual-domain guided stable diffusion for real-world stereo image super-resolution.
    
  
    Expert Syst. Appl., 2026
    
  
  2025
    IEEE Trans. Circuits Syst. Video Technol., July, 2025
    
  
Modeling optical imaging pipeline and learning contrastive-based representation for hybrid-corrupted image restoration.
    
  
    Multim. Syst., June, 2025
    
  
A multi-scale feature cross-dimensional interaction network for stereo image super-resolution.
    
  
    Multim. Syst., April, 2025
    
  
    Knowl. Based Syst., 2025
    
  
  2024
    IEEE Trans. Aerosp. Electron. Syst., August, 2024
    
  
    IEEE Trans. Broadcast., June, 2024
    
  
    Pattern Recognit., March, 2024
    
  
    Multim. Tools Appl., March, 2024
    
  
    Trans. Mach. Learn. Res., 2024
    
  
    Knowl. Based Syst., 2024
    
  
    Proceedings of the IEEE International Conference on Acoustics, 2024
    
  
MoDA: Leveraging Motion Priors from Videos for Advancing Unsupervised Domain Adaptation in Semantic Segmentation.
    
  
    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024
    
  
  2023
DifAugGAN: A Practical Diffusion-style Data Augmentation for GAN-based Single Image Super-resolution.
    
  
    CoRR, 2023
    
  
    CoRR, 2023
    
  
    IEEE Access, 2023
    
  
CDPMSR: Conditional Diffusion Probabilistic Models for Single Image Super-Resolution.
    
  
    Proceedings of the IEEE International Conference on Image Processing, 2023
    
  
  2022
MS2Net: Multi-Scale and Multi-Stage Feature Fusion for Blurred Image Super-Resolution.
    
  
    IEEE Trans. Circuits Syst. Video Technol., 2022
    
  
On the Pros and Cons of Momentum Encoder in Self-Supervised Visual Representation Learning.
    
  
    CoRR, 2022
    
  
Noise Augmentation Is All You Need For FGSM Fast Adversarial Training: Catastrophic Overfitting And Robust Overfitting Require Different Augmentation.
    
  
    CoRR, 2022
    
  
Decoupled Adversarial Contrastive Learning for Self-supervised Adversarial Robustness.
    
  
    Proceedings of the Computer Vision - ECCV 2022, 2022
    
  
Dual Temperature Helps Contrastive Learning Without Many Negative Samples: Towards Understanding and Simplifying MoCo.
    
  
    Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022
    
  
  2021
    Pattern Recognit., 2021