6 papers
ReCon: Region-Controllable Data Augmentation with Rectification and Alignment for Object Detection
Haowei Zhu, Tianxiang Pan, Rui Qin +2
The scale and quality of datasets are crucial for training robust perception models. However, obtaining large-scale annotated data is both costly and time-consuming. Generative mod…
Accelerating Diffusion-based Super-Resolution with Dynamic Time-Spatial Sampling
Rui Qin, Qijie Wang, Ming Sun +3
Diffusion models have gained attention for their success in modeling complex distributions, achieving impressive perceptual quality in SR tasks. However, existing diffusion-based S…
Semantic Hierarchical Prompt Tuning for Parameter-Efficient Fine-Tuning
Haowei Zhu, Fangyuan Zhang, Rui Qin +3
As the scale of vision models continues to grow, Visual Prompt Tuning (VPT) has emerged as a parameter-efficient transfer learning technique, noted for its superior performance com…
A New Dataset and Framework for Real-World Blurred Images Super-Resolution
Rui Qin, Ming Sun, Chao Zhou +1
Recent Blind Image Super-Resolution (BSR) methods have shown proficiency in general images. However, we find that the efficacy of recent methods obviously diminishes when employed…
Blind Image Super-resolution with Rich Texture-Aware Codebooks
Rui Qin, Ming Sun, Fangyuan Zhang +2
Blind super-resolution (BSR) methods based on high-resolution (HR) reconstruction codebooks have achieved promising results in recent years. However, we find that a codebook based…
Scene Text Image Super-Resolution via Content Perceptual Loss and Criss-Cross Transformer Blocks
Rui Qin, Bin Wang, Yu-Wing Tai
Text image super-resolution is a unique and important task to enhance readability of text images to humans. It is widely used as pre-processing in scene text recognition. However,…