3 citations · 5 across the 22 of their papers we have counts for
6 papers · 1 filter
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…
Hi-Mamba: Hierarchical Mamba for Efficient Image Super-Resolution
Junbo Qiao, Jincheng Liao, Wei Li +7
State Space Models (SSM), such as Mamba, have shown strong representation ability in modeling long-range dependency with linear complexity, achieving successful applications from h…
Unleashing the Power of One-Step Diffusion based Image Super-Resolution via a Large-Scale Diffusion Discriminator
Jianze Li, Jiezhang Cao, Zichen Zou +5
Diffusion models have demonstrated excellent performance for real-world image super-resolution (Real-ISR), albeit at high computational costs. Most existing methods are trying to d…
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…
Binarized Diffusion Model for Image Super-Resolution
Zheng Chen, Haotong Qin, Yong Guo +4
Advanced diffusion models (DMs) perform impressively in image super-resolution (SR), but the high memory and computational costs hinder their deployment. Binarization, an ultra-com…