Enhancing Defect Classification for the ASE Dataset through Progressive Alignment with VLM-LLM Feature Fusion

Leveraging the zero-shot capabilities of vision-language models (VLMs) and large language models (LLMs) to enhance defect classification performance on the ASE dataset, which suffers from insufficient training data and monotonic visual patterns, by extracting and fusing complementary features across modalities.