50 citations · 127 across the 13 of their papers we have counts for
13 papers
Q-Ground: Image Quality Grounding with Large Multi-modality Models
Chaofeng Chen, Sensen Yang, Haoning Wu +6
Recent advances of large multi-modality models (LMM) have greatly improved the ability of image quality assessment (IQA) method to evaluate and explain the quality of visual conten…
Towards Open-ended Visual Quality Comparison
Haoning Wu, Hanwei Zhu, Zicheng Zhang +11
Comparative settings (e.g. pairwise choice, listwise ranking) have been adopted by a wide range of subjective studies for image quality assessment (IQA), as it inherently standardi…
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…
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…
Q-Bench: A Benchmark for General-Purpose Foundation Models on Low-level Vision
Haoning Wu, Zicheng Zhang, Erli Zhang +8
The rapid evolution of Multi-modality Large Language Models (MLLMs) has catalyzed a shift in computer vision from specialized models to general-purpose foundation models. Neverthel…
TOPIQ: A Top-down Approach from Semantics to Distortions for Image Quality Assessment
Chaofeng Chen, Jiadi Mo, Jingwen Hou +5
Image Quality Assessment (IQA) is a fundamental task in computer vision that has witnessed remarkable progress with deep neural networks. Inspired by the characteristics of the hum…
GCFAgg: Global and Cross-view Feature Aggregation for Multi-view Clustering
Weiqing Yan, Yuanyang Zhang, Chenlei Lv +4
Multi-view clustering can partition data samples into their categories by learning a consensus representation in unsupervised way and has received more and more attention in recent…
Towards Explainable In-the-Wild Video Quality Assessment: A Database and a Language-Prompted Approach
Haoning Wu, Erli Zhang, Liang Liao +6
The proliferation of in-the-wild videos has greatly expanded the Video Quality Assessment (VQA) problem. Unlike early definitions that usually focus on limited distortion types, VQ…
Towards Robust Text-Prompted Semantic Criterion for In-the-Wild Video Quality Assessment
Haoning Wu, Liang Liao, Annan Wang +5
The proliferation of videos collected during in-the-wild natural settings has pushed the development of effective Video Quality Assessment (VQA) methodologies. Contemporary supervi…
Exploring Opinion-unaware Video Quality Assessment with Semantic Affinity Criterion
Haoning Wu, Liang Liao, Jingwen Hou +6
Recent learning-based video quality assessment (VQA) algorithms are expensive to implement due to the cost of data collection of human quality opinions, and are less robust across…
Exploring Video Quality Assessment on User Generated Contents from Aesthetic and Technical Perspectives
Haoning Wu, Erli Zhang, Liang Liao +6
The rapid increase in user-generated-content (UGC) videos calls for the development of effective video quality assessment (VQA) algorithms. However, the objective of the UGC-VQA pr…