1 citations · 3 across the 9 of their papers we have counts for
9 papers
Unleashing the Potential of the Semantic Latent Space in Diffusion Models for Image Dehazing
Zizheng Yang, Hu Yu, Bing Li +3
Diffusion models have recently been investigated as powerful generative solvers for image dehazing, owing to their remarkable capability to model the data distribution. However, th…
Temperature calibration of surface emissivities with an improved thermal image enhancement network
Ning Chu, Siya Zheng, Shanqing Zhang +5
Infrared thermography faces persistent challenges in temperature accuracy due to material emissivity variations, where existing methods often neglect the joint optimization of radi…
VideoMAR: Autoregressive Video Generatio with Continuous Tokens
Hu Yu, Biao Gong, Hangjie Yuan +5
Masked-based autoregressive models have demonstrated promising image generation capability in continuous space. However, their potential for video generation remains under-explored…
SynBoost: A Synergistic Framework for Fast Sampling of Diffusion Models
Hu Yu, Hao Luo, Xueyang Fu +3
Diffusion probabilistic models (DPMs) have demonstrated remarkable success in visual generation. However, their iterative sampling mechanism results in slow inference speeds. While…
FEB-Cache: Frequency-Guided Exposure Bias Reduction for Enhancing Diffusion Transformer Caching
Zhen Zou, Feng Zhao
Diffusion Transformer (DiT) has exhibited impressive generation capabilities but faces great challenges due to its high computational complexity. To address this issue, various met…
Frequency Autoregressive Image Generation with Continuous Tokens
Hu Yu, Hao Luo, Hangjie Yuan +3
Autoregressive (AR) models for image generation typically adopt a two-stage paradigm of vector quantization and raster-scan ``next-token prediction", inspired by its great success…
AnyLogo: Symbiotic Subject-Driven Diffusion System with Gemini Status
Jinghao Zhang, Wen Qian, Hao Luo +2
Diffusion models have made compelling progress on facilitating high-throughput daily production. Nevertheless, the appealing customized requirements are remain suffered from instan…
Uncovering the Text Embedding in Text-to-Image Diffusion Models
Hu Yu, Hao Luo, Fan Wang +1
The correspondence between input text and the generated image exhibits opacity, wherein minor textual modifications can induce substantial deviations in the generated image. While,…
Unmasking Bias in Diffusion Model Training
Hu Yu, Li Shen, Jie Huang +2
Denoising diffusion models have emerged as a dominant approach for image generation, however they still suffer from slow convergence in training and color shift issues in sampling.…