33 citations · 35 across the 3 of their papers we have counts for
5 papers
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-Bench+: A Benchmark for Multi-modal Foundation Models on Low-level Vision from Single Images to Pairs
Zicheng Zhang, Haoning Wu, Erli Zhang +2
The rapid development of Multi-modality Large Language Models (MLLMs) has navigated a paradigm shift in computer vision, moving towards versatile foundational models. However, eval…
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…
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…