Continuous Learning for Blind Image Quality Assessment with Contrastive Transformer

Most existing blind image quality assessment (BIQA) models focus on improving performance on existing datasets and are weak in adapting to unknown distortion or degradation types. In this paper, we propose a Transformer-based BIQA contrastive continual learning approach to improve model transfer performance. The basic idea is that the model continuously learns from the IQA data stream, integrating new knowledge from the current dataset. At the same time, limited access to previous data using a limited memory budget prevents forgetting the knowledge gained from the dataset of old tasks. We design an attentional contrastive learning strategy based on the Transformer architecture with a designed attentional focus contrastive loss to rebalance the contrastive learning between the new and old tasks, which can consolidate the previously learned representations. In addition, we used a structure similar to the cumulative classifier, balancing the learning of the current task quality score with the quality scores of all observed tasks. Extensive experiments demonstrate the feasibility of the proposed continuous learning approach compared to the standard training techniques of BIQA.