Task-Specific Normalization for Continual Learning of Blind Image Quality Models

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.