Anomaly localization is a practical technology for improving industrial
production line efficiency. Due to anomalies are manifold and hard to be
collected, existing unsupervised researches are usually equipped with anomaly
synthesis methods. However, most of them are biased towards structural defects
synthesis while ignoring the underlying logical constraints. To fill the gap
and boost anomaly localization performance, we propose an edge manipulation
based anomaly synthesis framework, named LogicAL, that produces photo-realistic
both logical and structural anomalies. We introduce a logical anomaly
generation strategy that is adept at breaking logical constraints and a
structural anomaly generation strategy that complements to the structural
defects synthesis. We further improve the anomaly localization performance by
introducing edge reconstruction into the network structure. Extensive
experiments on the challenge MVTecLOCO, MVTecAD, VisA and MADsim datasets
verify the advantage of proposed LogicAL on both logical and structural anomaly
localization.

通过引入边缘操作的异常合成框架，命名为 LogicAL，本研究提出了一种产生逼真的逻辑和结构异常的方法，填补了现有无监督研究在逻辑约束上的不足，并通过引入边缘重构进一步提高了异常定位性能。在 MVTecLOCO、MVTecAD、VisA 和 MADsim 数据集上进行了广泛实验，验证了 LogicAL 在逻辑和结构异常定位上的优势。

LogicAL：面向无监督异常定位的逻辑异常合成

LogicAL: Towards logical anomaly synthesis for unsupervised anomaly  localization

Anomaly synthesis is one of the effective methods to augment abnormal samples
for training. However, current anomaly synthesis methods predominantly rely on
texture information as input, which limits the fidelity of synthesized abnormal
samples. Because texture information is insufficient to correctly depict the
pattern of anomalies, especially for logical anomalies. To surmount this
obstacle, we present the AnomalyXFusion framework, designed to harness
multi-modality information to enhance the quality of synthesized abnormal
samples. The AnomalyXFusion framework comprises two distinct yet synergistic
modules: the Multi-modal In-Fusion (MIF) module and the Dynamic Dif-Fusion
(DDF) module. The MIF module refines modality alignment by aggregating and
integrating various modality features into a unified embedding space, termed
X-embedding, which includes image, text, and mask features. Concurrently, the
DDF module facilitates controlled generation through an adaptive adjustment of
X-embedding conditioned on the diffusion steps. In addition, to reveal the
multi-modality representational power of AnomalyXFusion, we propose a new
dataset, called MVTec Caption. More precisely, MVTec Caption extends 2.2k
accurate image-mask-text annotations for the MVTec AD and LOCO datasets.
Comprehensive evaluations demonstrate the effectiveness of AnomalyXFusion,
especially regarding the fidelity and diversity for logical anomalies. Project
page: http:github.com/hujiecpp/MVTec-Caption

为了增强合成异常样本的准确性，研究提出了 AnomalyXFusion 框架，该框架利用多模态信息提高异常样本的质量，包括图像、文本和掩码特征，并通过聚合和整合各种模态特征到统一的嵌入空间以及利用自适应调整来进行受控生成。

异常融合：多模态异常扩散合成

AnomalyXFusion: Multi-modal Anomaly Synthesis with Diffusion

Recently, 3D anomaly detection, a crucial problem involving fine-grained
geometry discrimination, is getting more attention. However, the lack of
abundant real 3D anomaly data limits the scalability of current models. To
enable scalable anomaly data collection, we propose a 3D anomaly synthesis
pipeline to adapt existing large-scale 3Dmodels for 3D anomaly detection.
Specifically, we construct a synthetic dataset, i.e., Anomaly-ShapeNet, basedon
ShapeNet. Anomaly-ShapeNet consists of 1600 point cloud samples under 40
categories, which provides a rich and varied collection of data, enabling
efficient training and enhancing adaptability to industrial scenarios.
Meanwhile,to enable scalable representation learning for 3D anomaly
localization, we propose a self-supervised method, i.e., Iterative Mask
Reconstruction Network (IMRNet). During training, we propose a geometry-aware
sample module to preserve potentially anomalous local regions during point
cloud down-sampling. Then, we randomly mask out point patches and sent the
visible patches to a transformer for reconstruction-based self-supervision.
During testing, the point cloud repeatedly goes through the Mask Reconstruction
Network, with each iteration's output becoming the next input. By merging and
contrasting the final reconstructed point cloud with the initial input, our
method successfully locates anomalies. Experiments show that IMRNet outperforms
previous state-of-the-art methods, achieving 66.1% in I-AUC on Anomaly-ShapeNet
dataset and 72.5% in I-AUC on Real3D-AD dataset. Our dataset will be released
at this https URL

为了解决现有模型的可扩展性问题，本文提出了一种基于现有大规模 3D 模型的 3D 异常合成流程，以适应 3D 异常检测。同时，为了实现可扩展的 3D 异常定位表示学习，本文提出了一种自我监督方法 —— 迭代掩蔽重建网络（IMRNet），并在 Anomaly-ShapeNet 数据集和 Real3D-AD 数据集上取得了显著优于先前方法的成果。