The computational vision community has recently paid attention to continual learning for blind image quality assessment (BIQA). The primary challenge is to combat catastrophic forgetting of previously-seen IQA datasets (i.e., tasks). In this paper, we present a simple yet effective continual learning method for BIQA with improved quality prediction accuracy, plasticity-stability trade-off, and task-order/-length robustness. The key step in our approach is to freeze all convolution filters of a pre-trained deep neural network (DNN) for an explicit promise of stability, and learn task-specific normalization parameters for plasticity. We assign each new task a prediction head, and load the corresponding normalization parameters to produce a quality score. The final quality estimate is computed by a weighted summation of predictions from all heads with a lightweight K-means gating mechanism, without leveraging the test-time oracle. Extensive experiments on six IQA datasets demonstrate the advantages of the proposed method in comparison to previous training techniques for BIQA.

翻译：计算视觉领域近期开始关注连续学习在盲图像质量评估（BIQA）中的应用。其主要挑战在于克服先前已见图像质量评估数据集（即任务）的灾难性遗忘问题。本文提出一种简单而有效的连续学习方法，用于改善BIQA的质量预测精度、可塑性与稳定性权衡，以及对任务顺序和任务长度的鲁棒性。该方法的核心步骤是冻结预训练深度神经网络（DNN）的所有卷积滤波器以明确保证稳定性，同时学习任务特定的归一化参数以实现可塑性。我们为每个新任务分配一个预测头，并加载相应的归一化参数以生成质量分数。最终质量估计通过一个轻量级K均值门控机制对所有预测头的输出进行加权求和计算，而无需利用测试时的先验信息。在六个图像质量评估数据集上的广泛实验表明，与以往BIQA训练技术相比，所提方法具有显著优势。