21 citations · 30 across the 10 of their papers we have counts for
12 papers
Exploring Scalable Unified Modeling for General Low-Level Vision
Xiangyu Chen, Kaiwen Zhu, Yuandong Pu +7
Low-level vision involves a wide spectrum of tasks, including image restoration, enhancement, stylization, and feature extraction, which differ significantly in both task formulati…
Lumina-OmniLV: A Unified Multimodal Framework for General Low-Level Vision
Yuandong Pu, Le Zhuo, Kaiwen Zhu +7
We present Lunima-OmniLV (abbreviated as OmniLV), a universal multimodal multi-task framework for low-level vision that addresses over 100 sub-tasks across four major categories: i…
Learning A Low-Level Vision Generalist via Visual Task Prompt
Xiangyu Chen, Yihao Liu, Yuandong Pu +4
Building a unified model for general low-level vision tasks holds significant research and practical value. Current methods encounter several critical issues. Multi-task restoratio…
A Preliminary Exploration Towards General Image Restoration
Xiangtao Kong, Jinjin Gu, Yihao Liu +4
Despite the tremendous success of deep models in various individual image restoration tasks, there are at least two major technical challenges preventing these works from being app…
GRIDS: Grouped Multiple-Degradation Restoration with Image Degradation Similarity
Shuo Cao, Yihao Liu, Wenlong Zhang +2
Traditional single-task image restoration methods excel in handling specific degradation types but struggle with multiple degradations. To address this limitation, we propose Group…
Towards Real-world Video Face Restoration: A New Benchmark
Ziyan Chen, Jingwen He, Xinqi Lin +2
Blind face restoration (BFR) on images has significantly progressed over the last several years, while real-world video face restoration (VFR), which is more challenging for more c…
SmartEdit: Exploring Complex Instruction-based Image Editing with Multimodal Large Language Models
Yuzhou Huang, Liangbin Xie, Xintao Wang +8
Current instruction-based editing methods, such as InstructPix2Pix, often fail to produce satisfactory results in complex scenarios due to their dependence on the simple CLIP text…
A Comparative Study of Image Restoration Networks for General Backbone Network Design
Xiangyu Chen, Zheyuan Li, Yuandong Pu +4
Despite the significant progress made by deep models in various image restoration tasks, existing image restoration networks still face challenges in terms of task generality. An i…
Unifying Image Processing as Visual Prompting Question Answering
Yihao Liu, Xiangyu Chen, Xianzheng Ma +4
Image processing is a fundamental task in computer vision, which aims at enhancing image quality and extracting essential features for subsequent vision applications. Traditionally…
HAT: Hybrid Attention Transformer for Image Restoration
Xiangyu Chen, Xintao Wang, Wenlong Zhang +4
Transformer-based methods have shown impressive performance in image restoration tasks, such as image super-resolution and denoising. However, we find that these networks can only…
Towards Efficient SDRTV-to-HDRTV by Learning from Image Formation
Xiangyu Chen, Zheyuan Li, Zhengwen Zhang +6
Modern displays can render video content with high dynamic range (HDR) and wide color gamut (WCG). However, most resources are still in standard dynamic range (SDR). Therefore, tra…
SEAL: A Framework for Systematic Evaluation of Real-World Super-Resolution
Wenlong Zhang, Xiaohui Li, Xiangyu Chen +3
Real-world Super-Resolution (Real-SR) methods focus on dealing with diverse real-world images and have attracted increasing attention in recent years. The key idea is to use a comp…