activity
20222024
most citedTowards Explainable In-the-Wild Video Quality Assessment: A Database and a Language-Prompted Approach

50 citations · 112 across the 9 of their papers we have counts for

collaborators

9 papers

cs.CV2024

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…

cs.CV2024

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…

cs.CV2023★ 33 cited

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…

cs.CV2023

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…

cs.CV2023★ 2 cited

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…

cs.CV2023★ 20 cited

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…

cs.CV2023★ 50 cited

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…

cs.CV2023★ 2 cited

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…

cs.CV2022★ 5 cited

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…