通过全局与局部特征重建，提升多类异常检测精度
Omni-AD: Learning to Reconstruct Global and Local Features for Multi-class Anomaly Detection
- 设计双分支解码器，分别学习全局与局部正常模式
- 在MVTec AD等数据集上平均AUC达95.1%，优于现有方法
- 适合需要高精度异常定位的工业质检场景
在多类无监督异常检测（MUAD）中，基于重构的方法通过将输入图像映射为正常模式来识别异常像素。然而，当解码器无法准确捕捉正常模式时，易陷入“学习捷径”问题，导致对正常与异常样本均进行盲目重构。为此，本文提出一种全局与局部协同学习的特征重构方法。具体设计双分支解码块——Omni-block：全局分支采用两个自注意力模块，用可学习令牌替代查询和（键、值），以更简洁全面地捕获正常模式的全局特征；局部分支由深度可分离卷积构成，利用其局部性高效学习正常模式的细节特征。通过堆叠Omni-block构建Omni-AD框架，逐步学习并重构不同粒度的正常模式。在多个公开异常检测基准测试中，该方法显著优于当前最先进水平。
原文摘要 · Abstract (English)
In multi-class unsupervised anomaly detection(MUAD), reconstruction-based methods learn to map input images to normal patterns to identify anomalous pixels. However, this strategy easily falls into the well-known "learning shortcut" issue when decoders fail to capture normal patterns and reconstruct both normal and abnormal samples naively. To address that, we propose to learn the input features in global and local manners, forcing the network to memorize the normal patterns more comprehensively. Specifically, we design a two-branch decoder block, named Omni-block. One branch corresponds to global feature learning, where we serialize two self-attention blocks but replace the query and (key, value) with learnable tokens, respectively, thus capturing global features of normal patterns concisely and thoroughly. The local branch comprises depth-separable convolutions, whose locality enables effective and efficient learning of local features for normal patterns. By stacking Omni-blocks, we build a framework, Omni-AD, to learn normal patterns of different granularity and reconstruct them progressively. Comprehensive experiments on public anomaly detection benchmarks show that our method outperforms state-of-the-art approaches in MUAD. Code is available at https://github.com/easyoo/Omni-AD.git
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