Progressive Alignment with VLM-LLM Feature to Augment Defect Classification for the ASE Dataset

arXiv:2404.05183v1 Announce Type: cross
Abstract: Traditional defect classification approaches are facing with two barriers. (1) Insufficient training data and unstable data quality. Collecting sufficient defective sample is expensive and time-costing, consequently leading to dataset variance. It introduces the difficulty on recognition and learning. (2) Over-dependence on visual modality. When the image pattern and texture is monotonic for all defect classes in a given dataset, the performance of conventional AOI system cannot be guaranteed. In scenarios where image quality is compromised …

abstract alignment arxiv classification cs.cv cs.lg data data quality dataset feature llm quality recognition sample training training data type variance visual vlm