From the 1 of 3 linked papers with an AI index.
4 papers
IQA-T1: Tool-based Visual Evidence Reasoning for Image Quality Assessment
Jinjian Wu, Jiaqi Tang, Wei Wei +5
The paper introduces IQA-T1, a framework that combines multimodal large language models with specialized visual analysis tools to generate explicit evidence (e.g., noise residual m…
Towards Syn-to-Real IQA: A Novel Perspective on Reshaping Synthetic Data Distributions
Aobo Li, Jinjian Wu, Yongxu Liu +2
Blind Image Quality Assessment (BIQA) has advanced significantly through deep learning, but the scarcity of large-scale labeled datasets remains a challenge. While synthetic data o…
Language-Guided Visual Perception Disentanglement for Image Quality Assessment and Conditional Image Generation
Zhichao Yang, Leida Li, Pengfei Chen +2
Contrastive vision-language models, such as CLIP, have demonstrated excellent zero-shot capability across semantic recognition tasks, mainly attributed to the training on a large-s…
Bridging the Synthetic-to-Authentic Gap: Distortion-Guided Unsupervised Domain Adaptation for Blind Image Quality Assessment
Aobo Li, Jinjian Wu, Yongxu Liu +1
The annotation of blind image quality assessment (BIQA) is labor-intensive and time-consuming, especially for authentic images. Training on synthetic data is expected to be benefic…