50 citations · 241 across the 52 of their papers we have counts for
52 papers
VQualA 2025 Challenge on Visual Quality Comparison for Large Multimodal Models: Methods and Results
Hanwei Zhu, Haoning Wu, Zicheng Zhang +26
This paper presents a summary of the VQualA 2025 Challenge on Visual Quality Comparison for Large Multimodal Models (LMMs), hosted as part of the ICCV 2025 Workshop on Visual Quali…
Scaling-up Perceptual Video Quality Assessment
Ziheng Jia, Zicheng Zhang, Zeyu Zhang +12
The data scaling law has been shown to significantly enhance the performance of large multi-modal models (LMMs) across various downstream tasks. However, in the domain of perceptua…
Q-Adapt: Adapting LMM for Visual Quality Assessment with Progressive Instruction Tuning
Yiting Lu, Xin Li, Haoning Wu +3
The rapid advancement of Large Multi-modal Foundation Models (LMM) has paved the way for the possible Explainable Image Quality Assessment (EIQA) with instruction tuning from two p…
Image Quality Assessment: From Human to Machine Preference
Chunyi Li, Yuan Tian, Xiaoyue Ling +9
Image Quality Assessment (IQA) based on human subjective preferences has undergone extensive research in the past decades. However, with the development of communication protocols,…
Q-SiT: Teaching LMMs for Image Quality Scoring and Interpreting
Zicheng Zhang, Haoning Wu, Ziheng Jia +2
Image quality scoring and interpreting are two fundamental components of Image Quality Assessment (IQA). The former quantifies image quality, while the latter enables descriptive q…
VQA: Visual Question Answering for Video Quality Assessment
Ziheng Jia, Zicheng Zhang, Jiaying Qian +7
The advent and proliferation of large multi-modal models (LMMs) have introduced new paradigms to computer vision, transforming various tasks into a unified visual question answerin…
R-Bench: Are your Large Multimodal Model Robust to Real-world Corruptions?
Chunyi Li, Jianbo Zhang, Zicheng Zhang +8
The outstanding performance of Large Multimodal Models (LMMs) has made them widely applied in vision-related tasks. However, various corruptions in the real world mean that images…
Explore the Hallucination on Low-level Perception for MLLMs
Yinan Sun, Zicheng Zhang, Haoning Wu +4
The rapid development of Multi-modality Large Language Models (MLLMs) has significantly influenced various aspects of industry and daily life, showcasing impressive capabilities in…
Q-Bench-Video: Benchmarking the Video Quality Understanding of LMMs
Zicheng Zhang, Ziheng Jia, Haoning Wu +8
With the rising interest in research on Large Multi-modal Models (LMMs) for video understanding, many studies have emphasized general video comprehension capabilities, neglecting t…
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…
LongVideoBench: A Benchmark for Long-context Interleaved Video-Language Understanding
Haoning Wu, Dongxu Li, Bei Chen +1
Large multimodal models (LMMs) are processing increasingly longer and richer inputs. Albeit the progress, few public benchmark is available to measure such development. To mitigate…
CMC-Bench: Towards a New Paradigm of Visual Signal Compression
Chunyi Li, Xiele Wu, Haoning Wu +7
Ultra-low bitrate image compression is a challenging and demanding topic. With the development of Large Multimodal Models (LMMs), a Cross Modality Compression (CMC) paradigm of Ima…
A-Bench: Are LMMs Masters at Evaluating AI-generated Images?
Zicheng Zhang, Haoning Wu, Chunyi Li +7
How to accurately and efficiently assess AI-generated images (AIGIs) remains a critical challenge for generative models. Given the high costs and extensive time commitments require…
Adaptive Image Quality Assessment via Teaching Large Multimodal Model to Compare
Hanwei Zhu, Haoning Wu, Yixuan Li +7
While recent advancements in large multimodal models (LMMs) have significantly improved their abilities in image quality assessment (IQA) relying on absolute quality rating, how to…
Dual-Branch Network for Portrait Image Quality Assessment
Wei Sun, Weixia Zhang, Yanwei Jiang +8
Portrait images typically consist of a salient person against diverse backgrounds. With the development of mobile devices and image processing techniques, users can conveniently ca…
Light-VQA+: A Video Quality Assessment Model for Exposure Correction with Vision-Language Guidance
Xunchu Zhou, Xiaohong Liu, Yunlong Dong +6
Recently, User-Generated Content (UGC) videos have gained popularity in our daily lives. However, UGC videos often suffer from poor exposure due to the limitations of photographic…
NTIRE 2024 Quality Assessment of AI-Generated Content Challenge
Xiaohong Liu, Xiongkuo Min, Guangtao Zhai +111
This paper reports on the NTIRE 2024 Quality Assessment of AI-Generated Content Challenge, which will be held in conjunction with the New Trends in Image Restoration and Enhancemen…
Enhancing Blind Video Quality Assessment with Rich Quality-aware Features
Wei Sun, Linhan Cao, Jun Jia +4
Blind video quality assessment (BVQA) is a highly challenging task due to the intrinsic complexity of video content and visual distortions, especially given the high popularity of…
G-Refine: A General Quality Refiner for Text-to-Image Generation
Chunyi Li, Haoning Wu, Hongkun Hao +7
With the evolution of Text-to-Image (T2I) models, the quality defects of AI-Generated Images (AIGIs) pose a significant barrier to their widespread adoption. In terms of both perce…
AIS 2024 Challenge on Video Quality Assessment of User-Generated Content: Methods and Results
Marcos V. Conde, Saman Zadtootaghaj, Nabajeet Barman +33
This paper reviews the AIS 2024 Video Quality Assessment (VQA) Challenge, focused on User-Generated Content (UGC). The aim of this challenge is to gather deep learning-based method…
NTIRE 2024 Challenge on Short-form UGC Video Quality Assessment: Methods and Results
Xin Li, Kun Yuan, Yajing Pei +65
This paper reviews the NTIRE 2024 Challenge on Shortform UGC Video Quality Assessment (S-UGC VQA), where various excellent solutions are submitted and evaluated on the collected da…
AIGIQA-20K: A Large Database for AI-Generated Image Quality Assessment
Chunyi Li, Tengchuan Kou, Yixuan Gao +10
With the rapid advancements in AI-Generated Content (AIGC), AI-Generated Images (AIGIs) have been widely applied in entertainment, education, and social media. However, due to the…
LMM-PCQA: Assisting Point Cloud Quality Assessment with LMM
Zicheng Zhang, Haoning Wu, Yingjie Zhou +7
Although large multi-modality models (LMMs) have seen extensive exploration and application in various quality assessment studies, their integration into Point Cloud Quality Assess…
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…