6 papers
Bridging Restoration and Generation in One-step Diffusion for Real-World Image Super-Resolution
Shyang-En Weng, Yi-Cheng Liao, Yu-Syuan Xu +3
Pretrained diffusion models have revolutionized real-world image super-resolution (Real-ISR), but their iterative sampling is computationally prohibitive, driving efforts to distil…
Prompting4Debugging: Red-Teaming Text-to-Image Diffusion Models by Finding Problematic Prompts
Zhi-Yi Chin, Chieh-Ming Jiang, Ching-Chun Huang +2
Text-to-image diffusion models, e.g. Stable Diffusion (SD), lately have shown remarkable ability in high-quality content generation, and become one of the representatives for the r…
Learning Ordinal Degradation Representations with Textual Priors for Diffusion-Based Blind Image Super-Resolution
Yi-Cheng Liao, Shyang-En Weng, Yu-Syuan Xu +4
Blind image super-resolution (Blind SR) has achieved remarkable perceptual quality via generative priors. However, lacking clear degradation representations such as varying severit…
RC-AutoCalib: An End-to-End Radar-Camera Automatic Calibration Network
Van-Tin Luu, Yon-Lin Cai, Vu-Hoang Tran +3
This paper presents a groundbreaking approach - the first online automatic geometric calibration method for radar and camera systems. Given the significant data sparsity and measur…
Boosting Diffusion Guidance via Learning Degradation-Aware Models for Blind Super Resolution
Shao-Hao Lu, Ren Wang, Ching-Chun Huang +1
Recently, diffusion-based blind super-resolution (SR) methods have shown great ability to generate high-resolution images with abundant high-frequency detail, but the detail is oft…
Masking Improves Contrastive Self-Supervised Learning for ConvNets, and Saliency Tells You Where
Zhi-Yi Chin, Chieh-Ming Jiang, Ching-Chun Huang +2
While image data starts to enjoy the simple-but-effective self-supervised learning scheme built upon masking and self-reconstruction objective thanks to the introduction of tokeniz…