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

• URL: http://arxiv.org/abs/2107.13429v3
• Date: Mon, 19 Feb 2024 15:36:23 GMT
• Title: Task-Specific Normalization for Continual Learning of Blind Image Quality Models
• Authors: Weixia Zhang and Kede Ma and Guangtao Zhai and Xiaokang Yang
• Abstract summary: We present a simple yet effective continual learning method for blind image quality assessment (BIQA) 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. 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.
• Score: 105.03239956378465
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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