33 citations · 69 across the 40 of their papers we have counts for
6 papers · 1 filter
Q-Align: Teaching LMMs for Visual Scoring via Discrete Text-Defined Levels
Haoning Wu, Zicheng Zhang, Weixia Zhang +11
The explosion of visual content available online underscores the requirement for an accurate machine assessor to robustly evaluate scores across diverse types of visual contents. W…
Q-Boost: On Visual Quality Assessment Ability of Low-level Multi-Modality Foundation Models
Zicheng Zhang, Haoning Wu, Zhongpeng Ji +9
Recent advancements in Multi-modality Large Language Models (MLLMs) have demonstrated remarkable capabilities in complex high-level vision tasks. However, the exploration of MLLM p…
Iterative Token Evaluation and Refinement for Real-World Super-Resolution
Chaofeng Chen, Shangchen Zhou, Liang Liao +4
Real-world image super-resolution (RWSR) is a long-standing problem as low-quality (LQ) images often have complex and unidentified degradations. Existing methods such as Generative…
Q-Instruct: Improving Low-level Visual Abilities for Multi-modality Foundation Models
Haoning Wu, Zicheng Zhang, Erli Zhang +11
Multi-modality foundation models, as represented by GPT-4V, have brought a new paradigm for low-level visual perception and understanding tasks, that can respond to a broad range o…
Enhancing Diffusion Models with Text-Encoder Reinforcement Learning
Chaofeng Chen, Annan Wang, Haoning Wu +4
Text-to-image diffusion models are typically trained to optimize the log-likelihood objective, which presents challenges in meeting specific requirements for downstream tasks, such…
You Only Train Once: A Unified Framework for Both Full-Reference and No-Reference Image Quality Assessment
Yi Ke Yun, Weisi Lin
Although recent efforts in image quality assessment (IQA) have achieved promising performance, there still exists a considerable gap compared to the human visual system (HVS). One…