<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Yan Zhong</style></author><author><style face="normal" font="default" size="100%">Yang, Chenxi</style></author><author><style face="normal" font="default" size="100%">Suyuan Zhao</style></author><author><style face="normal" font="default" size="100%">Tingting Jiang</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Semi-Supervised Blind Quality Assessment with Confidence-quantifiable Pseudo-label Learning for Authentic Images</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the 42nd International Conference on Machine Learning</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2025</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">https://openreview.net/pdf?id=038rEwbChh</style></url></web-urls></urls><publisher><style face="normal" font="default" size="100%">PMLR 267</style></publisher><pub-location><style face="normal" font="default" size="100%">Vancouver, Canada</style></pub-location><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">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's superior performance on real-world distorted image datasets, offering a more standardized semi-supervised learning paradigm without requiring additional supervision or network complexity.</style></abstract></record></records></xml>