LIQA：终身盲图像质量评估
LIQA: Lifelong Blind Image Quality Assessment
摘要：
现有的盲图像质量评估（BIQA）方法大多是一次性设计的，不能适应不可见的失真，这极大地限制了BIQA模型在实际场景中的部署和应用。为了解决这个问题，我们提出了一种新的终身盲图像质量评估（LIQA）的方法，旨在实现BIQA的终身学习。在不访问以前的训练数据的情况下，我们提出的LIQA不仅可以学习新的失真，而且还可以减轻对所看到的失真的灾难性遗忘。具体来说，我们采用分裂和合并蒸馏策略训练一个单头网络，使任务不可知的预测。在分裂阶段，我们首先采用一个特定的失真发生器，以获得每个看到的失真的伪特征。然后，我们使用一个辅助的多头回归网络来生成每个看到的失真的预测质量。在合并阶段，我们重放与伪标签配对的伪特征，以提取多个头部的知识，这可以构建最终回归的单个头部。实验结果表明，该方法可以处理不同失真类型甚至数据集的连续移位。更重要的是，我们的LIQA模型可以实现稳定的性能，即使任务序列很长。
英文摘要：
Existing blind image quality assessment (BIQA) methods are mostly designed in a disposable way and cannot evolve with unseen distortions adaptively, which greatly limits the deployment and application of BIQA models in real-world scenarios. To address this problem, we propose a novel Lifelong blind Image Quality Assessment (LIQA) approach, targeting to achieve the lifelong learning of BIQA. Without accessing to previous training data, our proposed LIQA can not only learn new distortions, but also mitigate the catastrophic forgetting of seen distortions. Specifically, we adopt the Split-and-Merge distillation strategy to train a single-head network that makes task-agnostic predictions. In the split stage, we first employ a distortion-specific generator to obtain the pseudo features of each seen distortion. Then, we use an auxiliary multi-head regression network to generate the predicted quality of each seen distortion. In the merge stage, we replay the pseudo features paired with pseudo labels to distill the knowledge of multiple heads, which can build the final regressed single head. Experimental results demonstrate that the proposed LIQA method can handle the continuous shifts of different distortion types and even datasets. More importantly, our LIQA model can achieve stable performance even if the task sequence is long.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
