33 citations · 69 across the 40 of their papers we have counts for
20 papers · 1 filter
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…
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…
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…
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…
MISC: Ultra-low Bitrate Image Semantic Compression Driven by Large Multimodal Model
Chunyi Li, Guo Lu, Donghui Feng +6
With the evolution of storage and communication protocols, ultra-low bitrate image compression has become a highly demanding topic. However, existing compression algorithms must sa…
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…
AesBench: An Expert Benchmark for Multimodal Large Language Models on Image Aesthetics Perception
Yipo Huang, Quan Yuan, Xiangfei Sheng +6
With collective endeavors, multimodal large language models (MLLMs) are undergoing a flourishing development. However, their performances on image aesthetics perception remain inde…
Towards A Better Metric for Text-to-Video Generation
Jay Zhangjie Wu, Guian Fang, Haoning Wu +11
Generative models have demonstrated remarkable capability in synthesizing high-quality text, images, and videos. For video generation, contemporary text-to-video models exhibit imp…
Q-Refine: A Perceptual Quality Refiner for AI-Generated Image
Chunyi Li, Haoning Wu, Zicheng Zhang +7
With the rapid evolution of the Text-to-Image (T2I) model in recent years, their unsatisfactory generation result has become a challenge. However, uniformly refining AI-Generated I…