  In this paper, we present a simple yet effective continual learning method
for blind image quality assessment (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
IQA dataset (i.e., task) a prediction head, and load the corresponding
normalization parameters to produce a quality score. The final quality estimate
is computed by black a weighted summation of predictions from all heads with a
lightweight $K$-means gating mechanism. Extensive experiments on six IQA
datasets demonstrate the advantages of the proposed method in comparison to
previous training techniques for BIQA.
