Multi-Attribute Continual Learning for Blind Image Quality Assessment

Blind image quality assessment (BIQA) has evolved into a critical task in visual computing, requiring effective evaluation across multiple quality attributes such as brightness, sharpness, contrast, and colorfulness. Traditional BIQA methods based on single-task learning often suffer from catastrophic forgetting and struggle to generalize across diverse IQ attributes. To address these challenges, we propose a novel Multi-attribute Continual Learning framework, MaC-BIQA, which integrates Gated Attention Mechanism and Knowledge Graph Embedding (KGE) with the Learning without Forgetting (LwF) approach. Specifically, the Gated Attention Mechanism dynamically adjusts attention distribution by focusing on task-specific key regions, while the integration of Knowledge Graph Embedding (KGE) complements LwF by improving the understanding of inter-task relationships, ensuring that relevant information from previous tasks is preserved more effectively, even as the model learns new tasks. Together, they enhance the model’s adaptability and robustness in handling complex multi-attribute tasks, effectively mitigating catastrophic forgetting and significantly improving overall performance in multi-task environments. Extensive experiments on the KonIQ-10K and SPAQ datasets show that our method significantly reduces forgetting rates and improves robustness and generalization across multiple quality attributes. This study presents a key technical contribution by addressing the limitations of catastrophic forgetting and offering a scalable, adaptive solution for real-world multi-attribute BIQA.