3 accepted papers
Despite deep learning-based methods perform remarkably well in surface defect detection recently, the unpredictable shapes and sizes of surface defects and complicated texture background still pose enormous challenges for existing methods. To address these problems, we propose a novel surface defect…
Most previous embedding-based methods for anomaly detection directly utilize the visual features extracted from pretrained CNN network. However, there usually exists a gap of domain between pretrained data and target data in anomaly detection. To alleviate this discrepancy, we introduce ReconFA in t…
Few-shot anomaly classification (FSAC) is a vital task in manufacturing industry. Recent methods focus on utilizing CLIP in zero/few normal shot anomaly detection instead of custom models. However, there is a lack of specific text prompts in anomaly classification and most of them ignore the modalit…