The proposed method can be extended to handle other types of industrial datasets with similar challenges by adapting the progressive feature alignment (PFA) block and cross-modality attention fusion (CMAF) module to suit the specific characteristics of the new dataset. For datasets with sparse training data, the PFA block can be modified to prioritize the alignment of features from different modalities based on the available samples. By gradually increasing the training data and aligning the features in a progressive manner, the model can effectively learn from limited data. Additionally, the TDA (Task-specific Data Augmentation) strategy can be tailored to generate synthetic data that mimics the patterns and characteristics of the new dataset, further enhancing the model's ability to generalize.
In the case of datasets with monotonic visual patterns, the VLM-LLM prompting approach can be enhanced by incorporating domain-specific knowledge and prompts that are tailored to capture the unique features of the dataset. By designing prompts that focus on the specific visual patterns present in the data, the model can extract more relevant information and improve its defect classification performance. Furthermore, fine-tuning the VLM and LLM models on a diverse range of industrial datasets with varying visual patterns can help the model adapt to different data distributions and improve its generalization capabilities.