most citedVision-Language Consistency Guided Multi-modal Prompt Learning for Blind AI Generated Image Quality Assessment

1 citations · 2 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CV2025

Cross-Modal Scene Semantic Alignment for Image Complexity Assessment

Yuqing Luo, Yixiao Li, Jiang Liu +7

Image complexity assessment (ICA) is a challenging task in perceptual evaluation due to the subjective nature of human perception and the inherent semantic diversity in real-world…

cs.CV2025

Perception-oriented Bidirectional Attention Network for Image Super-resolution Quality Assessment

Yixiao Li, Xiaoyuan Yang, Guanghui Yue +6

Many super-resolution (SR) algorithms have been proposed to increase image resolution. However, full-reference (FR) image quality assessment (IQA) metrics for comparing and evaluat…

cs.CV2025★ 1 cited

CLIP-DQA: Blindly Evaluating Dehazed Images from Global and Local Perspectives Using CLIP

Yirui Zeng, Jun Fu, Hadi Amirpour +5

Blind dehazed image quality assessment (BDQA), which aims to accurately predict the visual quality of dehazed images without any reference information, is essential for the evaluat…

cs.CV2024★ 1 cited

Vision-Language Consistency Guided Multi-modal Prompt Learning for Blind AI Generated Image Quality Assessment

Jun Fu, Wei Zhou, Qiuping Jiang +2

Recently, textual prompt tuning has shown inspirational performance in adapting Contrastive Language-Image Pre-training (CLIP) models to natural image quality assessment. However,…

cs.MM2024

Deep Bi-directional Attention Network for Image Super-Resolution Quality Assessment

Yixiao Li, Xiaoyuan Yang, Jun Fu +2

There has emerged a growing interest in exploring efficient quality assessment algorithms for image super-resolution (SR). However, employing deep learning techniques, especially d…