Bi-Grid Reconstruction for Image Anomaly Detection

TL;DR

GRAD excels in overall accuracy and in discerning subtle differences, demonstrating its superiority over existing methods, and its robust representation capabilities also allow it to handle multiple classes with a single model.

Abstract

In image anomaly detection, significant advancements have been made using un- and self-supervised methods with datasets containing only normal samples. However, these approaches often struggle with fine-grained anomalies. This paper introduces \textbf{GRAD}: Bi-\textbf{G}rid \textbf{R}econstruction for Image \textbf{A}nomaly \textbf{D}etection, which employs two continuous grids to enhance anomaly detection from both normal and abnormal perspectives. In this work: 1) Grids as feature repositories that improve generalization and mitigate the Identical Shortcut (IS) issue; 2) An abnormal feature grid that refines normal feature boundaries, boosting detection of fine-grained defects; 3) The Feature Block Paste (FBP) module, which synthesizes various anomalies at the feature level for quick abnormal grid deployment. GRAD's robust representation capabilities also allow it to handle multiple classes with a single model. Evaluations on datasets like MVTecAD, VisA, and GoodsAD show significant performance improvements in fine-grained anomaly detection. GRAD excels in overall accuracy and in discerning subtle differences, demonstrating its superiority over existing methods.

Figures (5)

• Figure 1: In the comparison of complex products and fine-grained anomalies, our model shows significant advantages over other models.
• Figure 2: Overall framework of our GRAD. The input samples are first processed by a pre-trained feature extractor to obtain initial features (the subsequent FBP module is only activated during the training of the anomaly grid). These features are then mapped to 2D coordinates through the coordinate mapping module. Based on these coordinates, sampling is performed from the normal and anomaly grid. The sampling results are fused and refined through the feature refinement module to produce the final reconstructed features. The comparison between these reconstructed features and the initial features yields the final anomaly detection results. (PS: The abnormal grid and normal grid have their top-left corner markers offset from each other, indicating that they also alternate during training.
• Figure 3: (a) Multiple anomaly patterns yield similar feature maps from the pretrained extractor. (b) Our FBP module can transform normal images into abnormal ones in the feature space.
• Figure 4: Qualitative results of GRAD on three datasets. Each row of the figure represents anomaly images, corresponding ground truths, results from different methods. Notably, even for extremely subtle anomalies in categories such as Macaroni2, Drink_Bottle, and Food_Bottle, our model has provided precise localization results.
• Figure 5: Comparing inference speed (FPS), I-AUROC, and memory occupancy on GoodsAD showcases the comprehensive performance of our model.