A Decoupled Few-Shot Defect Detection Approach via Vector Quantization Feature Aggregation

In recent years, few-shot detection has become a popular research direction in the field of industrial defect detection, which aims to perform defect detection tasks accurately using a limited number of labeled samples. The dual-branch architecture, which utilizes class center features to aggregate query features is one of the most commonly used methods to solve the few-shot detection problem due to its simplicity and effectiveness. Previous few-shot detection algorithms based on dual-branch architecture typically aggregate features by directly using the features of labeled samples or performing simple averaging operations on those features to compute the class center features. However, the acquisition of class center features is related to the sample distribution of novel classes, resulting in poor robustness of such methods. In addition, the regression of bounding boxes and the classification of objects are two coupled problems that are difficult to optimize simultaneously. Therefore, this article proposes a decoupled few-shot (DeFS) detection algorithm to address the challenges that exist in the current few-shot detection. More specifically, we propose a vector quantization feature aggregation (VQFA) method, which utilizes vector quantization (VQ) to map the features of support images into discrete vector sets and each vector comes from a learnable codebook. By employing this operation, we can acquire a category representation that is both robust and accurate, thereby enabling enhanced interactions between support and query features. In addition, we propose a DeFS module to decouple classification and localization tasks so that the two tasks can obtain different visual regions to achieve better detection results. Experimental results on the public dataset NEU-DET and GC10-DET demonstrate that our method significantly outperforms other methods on various few-shot scenes, which proves the effectiveness of our proposed method.