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Task-Specific Normalization for Continual Learning of Blind Image Quality Models
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DOI：10.1109/TIP.2024.3371349.png)
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摘要
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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 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.
Keyword:
Blind image quality assessment
continual learning
task-specific normalization
期刊
IF:
13.7
论文数:
1.0W
被引数:
8.4W
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PSYCHOLOGICAL REVIEW
PSYCHOLOGICAL REVIEWIF5.8
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