@conference {39573,
	title = {Semi-Supervised Blind Quality Assessment with Confidence-quantifiable Pseudo-label Learning for Authentic Images},
	booktitle = {Proceedings of the 42nd International Conference on Machine Learning},
	year = {2025},
	publisher = {PMLR 267},
	organization = {PMLR 267},
	address = {Vancouver, Canada},
	abstract = {This paper presents CPL-IQA, a novel semi-supervised blind image quality assessment (BIQA) framework for authentic distortion scenarios. To address the challenge of limited labeled data in IQA area, our approach leverages confidence-quantifiable pseudo-label learning to effectively utilize unlabeled authentically distorted images. The framework operates through a preprocessing stage and two training phases: first converting MOS labels to vector labels via entropy minimization, followed by an iterative process that alternates between model training and label optimization. The key innovations of CPL-IQA include a manifold assumption-based label optimization strategy and a confidence learning method for pseudo-labels, which enhance reliability and mitigate outlier effects. Experimental results demonstrate the framework{\textquoteright}s superior performance on real-world distorted image datasets, offering a more standardized semi-supervised learning paradigm without requiring additional supervision or network complexity.},
	url = {https://openreview.net/pdf?id=038rEwbChh},
	author = {Yan Zhong and Yang, Chenxi and Suyuan Zhao and Tingting Jiang}
}
