PCPD: Partial Complementary Policy Distillation for Multi-Dimensional AIGC Image Quality Assessment

Currently, AI generated content (AIGC) for image generation has achieved impressive results. Since the training process of AIGC models often requires evaluating the quality of the generated images, a high performance Image Quality Assessment (IQA) model is urgently needed. However, due to the exceptionally high quality of current AIGC images, IQA models trained based on degradation injection levels tend to yield spuriously high scores, making it difficult to accurately assess AIGC images. To address this issue, the common practice is to construct dedicated datasets of AIGC images with human-annotated quality scores and train IQA models on them. Nevertheless, the evaluation metrics for AIGC generated images are inherently multi-dimensional. Relying solely on Supervised Fine Tuning and Reinforcement Learning for model training often leads to conflicting training gradients across different dimensions, preventing the model from achieving optimal performance. To address this, we propose Partial Complementary Policy Distillation. This approach partitions the multiple dimensions into two mutually overlapping subsets. By leveraging tool calling and skill self-evolution, we guide the model to induce solutions for resolving these dimensional conflicts, ultimately internalizing these solutions into the model parameters. Extensive experiments demonstrate that our model achieves state-of-the-art (SOTA) performance on AIGC image evaluation tasks compared to other IQA models, effectively resolving the issue of dimensional conflicts.