4 citations · 7 across the 6 of their papers we have counts for
6 papers · 1 filter
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…
FakeScope: Large Multimodal Expert Model for Transparent AI-Generated Image Forensics
Yixuan Li, Yu Tian, Yipo Huang +4
The rapid and unrestrained advancement of generative artificial intelligence (AI) presents a double-edged sword. While enabling unprecedented creativity, it also facilitates the ge…
AI-generated Image Quality Assessment in Visual Communication
Yu Tian, Yixuan Li, Baoliang Chen +3
Assessing the quality of artificial intelligence-generated images (AIGIs) plays a crucial role in their application in real-world scenarios. However, traditional image quality asse…
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…
FakeBench: Probing Explainable Fake Image Detection via Large Multimodal Models
Yixuan Li, Xuelin Liu, Xiaoyang Wang +4
The ability to distinguish whether an image is generated by artificial intelligence (AI) is a crucial ingredient in human intelligence, usually accompanied by a complex and dialect…
Deep Shape-Texture Statistics for Completely Blind Image Quality Evaluation
Yixuan Li, Peilin Chen, Hanwei Zhu +3
Opinion-Unaware Blind Image Quality Assessment (OU-BIQA) models aim to predict image quality without training on reference images and subjective quality scores. Thereinto, image st…