30 citations · 89 across the 25 of their papers we have counts for
24 papers · 1 filter
XYZFlow:Scaling Multi dimensional Shortcut Flows for Efficient Generative Modeling
Jinxiu Liu, Xuanming Liu, Kangfu Mei +2
High-fidelity image generation faces a trade-off between speed and quality. Diffusion models produce strong visuals but require costly iterative sampling. Existing efficient method…
SymbOmni: Evolving Agentic Omni Models via Symbolic Concept Learning
Jinxiu Liu, Jianru Li, Tanqing Kuang +4
Visual generation is increasingly ubiquitous in diverse domains, from text-to-image/video synthesis to multimodal interactive creation. Yet prevailing monolithic models remain fund…
Streaming Autoregressive Video Generation via Diagonal Distillation
Jinxiu Liu, Xuanming Liu, Kangfu Mei +3
Large pretrained diffusion models have significantly enhanced the quality of generated videos, and yet their use in real-time streaming remains limited. Autoregressive models offer…
Generative Manifold Distillation: Aligning Restoration Trajectories with Natural Image Prior
Yuyang Hu, Mojtaba Sahraee-Ardakan, Arpit Bansal +4
Pre-trained image restoration models often fail on out-of-distribution (OOD) real-world degradations. Adapting to these domains is challenging as real-world data lacks paired groun…
Kernel Density Steering: Inference-Time Scaling via Mode Seeking for Image Restoration
Yuyang Hu, Kangfu Mei, Mojtaba Sahraee-Ardakan +3
Diffusion models show promise for image restoration, but existing methods often struggle with inconsistent fidelity and undesirable artifacts. To address this, we introduce Kernel…
Reference-Guided Identity Preserving Face Restoration
Mo Zhou, Keren Ye, Viraj Shah +5
Preserving face identity is a critical yet persistent challenge in diffusion-based image restoration. While reference faces offer a path forward, existing reference-based methods o…
The Power of Context: How Multimodality Improves Image Super-Resolution
Kangfu Mei, Hossein Talebi, Mojtaba Ardakani +3
Single-image super-resolution (SISR) remains challenging due to the inherent difficulty of recovering fine-grained details and preserving perceptual quality from low-resolution inp…
Bigger is not Always Better: Scaling Properties of Latent Diffusion Models
Kangfu Mei, Zhengzhong Tu, Mauricio Delbracio +3
We study the scaling properties of latent diffusion models (LDMs) with an emphasis on their sampling efficiency. While improved network architecture and inference algorithms have s…
Latent Feature-Guided Diffusion Models for Shadow Removal
Kangfu Mei, Luis Figueroa, Zhe Lin +3
Recovering textures under shadows has remained a challenging problem due to the difficulty of inferring shadow-free scenes from shadow images. In this paper, we propose the use of…
CoDi: Conditional Diffusion Distillation for Higher-Fidelity and Faster Image Generation
Kangfu Mei, Mauricio Delbracio, Hossein Talebi +3
Large generative diffusion models have revolutionized text-to-image generation and offer immense potential for conditional generation tasks such as image enhancement, restoration,…
T1: Scaling Diffusion Probabilistic Fields to High-Resolution on Unified Visual Modalities
Kangfu Mei, Mo Zhou, Vishal M. Patel
Diffusion Probabilistic Field (DPF) models the distribution of continuous functions defined over metric spaces. While DPF shows great potential for unifying data generation of vari…
Bi-Noising Diffusion: Towards Conditional Diffusion Models with Generative Restoration Priors
Kangfu Mei, Nithin Gopalakrishnan Nair, Vishal M. Patel
Conditional diffusion probabilistic models can model the distribution of natural images and can generate diverse and realistic samples based on given conditions. However, oftentime…
VIDM: Video Implicit Diffusion Models
Kangfu Mei, Vishal M. Patel
Diffusion models have emerged as a powerful generative method for synthesizing high-quality and diverse set of images. In this paper, we propose a video generation method based on…
AT-DDPM: Restoring Faces degraded by Atmospheric Turbulence using Denoising Diffusion Probabilistic Models
Nithin Gopalakrishnan Nair, Kangfu Mei, Vishal M. Patel
Although many long-range imaging systems are designed to support extended vision applications, a natural obstacle to their operation is degradation due to atmospheric turbulence. A…
Deep Semantic Statistics Matching (D2SM) Denoising Network
Kangfu Mei, Vishal M. Patel, Rui Huang
The ultimate aim of image restoration like denoising is to find an exact correlation between the noisy and clear image domains. But the optimization of end-to-end denoising learnin…
A comparison of different atmospheric turbulence simulation methods for image restoration
Nithin Gopalakrishnan Nair, Kangfu Mei, Vishal M. Patel
Atmospheric turbulence deteriorates the quality of images captured by long-range imaging systems by introducing blur and geometric distortions to the captured scene. This leads to…
Thermal to Visible Image Synthesis under Atmospheric Turbulence
Kangfu Mei, Yiqun Mei, Vishal M. Patel
In many practical applications of long-range imaging such as biometrics and surveillance, thermal imagining modalities are often used to capture images in low-light and nighttime c…
LTT-GAN: Looking Through Turbulence by Inverting GANs
Kangfu Mei, Vishal M. Patel
In many applications of long-range imaging, we are faced with a scenario where a person appearing in the captured imagery is often degraded by atmospheric turbulence. However, rest…
SDAN: Squared Deformable Alignment Network for Learning Misaligned Optical Zoom
Kangfu Mei, Shenglong Ye, Rui Huang
Deep Neural Network (DNN) based super-resolution algorithms have greatly improved the quality of the generated images. However, these algorithms often yield significant artifacts w…
AttaNet: Attention-Augmented Network for Fast and Accurate Scene Parsing
Qi Song, Kangfu Mei, Rui Huang
Two factors have proven to be very important to the performance of semantic segmentation models: global context and multi-level semantics. However, generating features that capture…
Scale-Aware Network with Regional and Semantic Attentions for Crowd Counting under Cluttered Background
Qiaosi Yi, Yunxing Liu, Aiwen Jiang +3
Crowd counting is an important task that shown great application value in public safety-related fields, which has attracted increasing attention in recent years. In the current res…
Disentangle Perceptual Learning through Online Contrastive Learning
Kangfu Mei, Yao Lu, Qiaosi Yi +3
Pursuing realistic results according to human visual perception is the central concern in the image transformation tasks. Perceptual learning approaches like perceptual loss are em…
Progressive Feature Fusion Network for Realistic Image Dehazing
Kangfu Mei, Aiwen Jiang, Juncheng Li +1
Single image dehazing is a challenging ill-posed restoration problem. Various prior-based and learning-based methods have been proposed. Most of them follow a classic atmospheric s…
An Effective Single-Image Super-Resolution Model Using Squeeze-and-Excitation Networks
Kangfu Mei, Aiwen Jiang, Juncheng Li +2
Recent works on single-image super-resolution are concentrated on improving performance through enhancing spatial encoding between convolutional layers. In this paper, we focus on…