5 papers
SODiff: Semantic-Oriented Diffusion Model for JPEG Compression Artifacts Removal
Tingyu Yang, Jue Gong, Jinpei Guo +3
JPEG, as a widely used image compression standard, often introduces severe visual artifacts when achieving high compression ratios. Although existing deep learning-based restoratio…
Low-bit Model Quantization for Deep Neural Networks: A Survey
Kai Liu, Qian Zheng, Kaiwen Tao +9
With unprecedented rapid development, deep neural networks (DNNs) have deeply influenced almost all fields. However, their heavy computation costs and model sizes are usually unacc…
PassionSR: Post-Training Quantization with Adaptive Scale in One-Step Diffusion based Image Super-Resolution
Libo Zhu, Jianze Li, Haotong Qin +4
Diffusion-based image super-resolution (SR) models have shown superior performance at the cost of multiple denoising steps. However, even though the denoising step has been reduced…
BiDense: Binarization for Dense Prediction
Rui Yin, Haotong Qin, Yulun Zhang +5
Dense prediction is a critical task in computer vision. However, previous methods often require extensive computational resources, which hinders their real-world application. In th…
2DQuant: Low-bit Post-Training Quantization for Image Super-Resolution
Kai Liu, Haotong Qin, Yong Guo +4
Low-bit quantization has become widespread for compressing image super-resolution (SR) models for edge deployment, which allows advanced SR models to enjoy compact low-bit paramete…