1 citations · 1 across the 5 of their papers we have counts for
5 papers
MEPA: Multi-Scale Representation Alignment for Visual Autoregressive Modeling with Mixture of Experts
Nuoyan Zhou, Zhijun Tu, Lei Yu +4
Visual AutoRegressive modeling (VAR) has pioneered a coarse-to-fine multi-scale autoregressive generative paradigm, demonstrating strong capabilities in image generation. However,…
Mixture of Ranks with Degradation-Aware Routing for One-Step Real-World Image Super-Resolution
Xiao He, Zhijun Tu, Kun Cheng +4
The demonstrated success of sparsely-gated Mixture-of-Experts (MoE) architectures, exemplified by models such as DeepSeek and Grok, has motivated researchers to investigate their a…
Effective Diffusion Transformer Architecture for Image Super-Resolution
Kun Cheng, Lei Yu, Zhijun Tu +7
Recent advances indicate that diffusion models hold great promise in image super-resolution. While the latest methods are primarily based on latent diffusion models with convolutio…
One Step Diffusion-based Super-Resolution with Time-Aware Distillation
Xiao He, Huaao Tang, Zhijun Tu +8
Diffusion-based image super-resolution (SR) methods have shown promise in reconstructing high-resolution images with fine details from low-resolution counterparts. However, these a…
Bridging Generative and Discriminative Models for Unified Visual Perception with Diffusion Priors
Shiyin Dong, Mingrui Zhu, Kun Cheng +2
The remarkable prowess of diffusion models in image generation has spurred efforts to extend their application beyond generative tasks. However, a persistent challenge exists in la…