Attention Guided Anomaly Localization in Images

Venkataramanan, S.; Peng, Kuan-Chuan; Singh, Rajat Vikram; Mahalanobis, Abhijit

doi:10.1007/978-3-030-58520-4_29

Cited by 159 publications

(153 citation statements)

References 45 publications

(56 reference statements)

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“…For [9], we chose the best performance for each dataset category among the four models augmented using their method. For [10], we used the results of an unsupervised version among the various methods considered. For our method, we chose the best performance for each dataset category among the six models applied.…”

Section: ) Existing Methods Versus Our Approachmentioning

confidence: 99%

“…Consequently, previous approaches have attempted to enhance the models for a better reconstruction performance. For example, well-known models were combined [1], cross-channel interactions were enabled [3], a deep convolutional generative adversarial network was used [4], a de-noising technique was proposed [5], pixel-level method and patch-level method were mixed [8], an attention technique was utilized [10], [13], and encoder-decoderencoder sub-networks were employed [15]. In [9], the reconstruction performance is improved by iteratively updating the input image using a gradient descent.…”

Section: Related Work a Unsupervised Anomaly Localizationmentioning

confidence: 99%

“…This method aims to generate a reference image by transforming abnormal patterns of a test image, if any, into normal patterns observed in normal training images. Efforts have been made to develop effective models and techniques for achieving better reference images [1], [3], [5], [10], [13].…”

Section: Introductionmentioning

confidence: 99%

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Patch-Level Operation With Adaptive Patch Control for Improving Anomaly Localization

et al. 2021

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In realizing unsupervised pixel-precise anomaly localization by utilizing a generative model, a reference image must be generated (for comparison with an input image) by transforming abnormal patterns of an input image, if any, into normal patterns. In this study, a patch-level operation with adaptive patch control is proposed to improve anomaly localization by generating a better reference image. As a way to exploit a generative model, we divide an image into non-overlapped patches of the same size, generate patch-level reference images, and stitch the patch-level reference images into a single reference image. We then conduct anomaly localization by comparing an input image with the stitched, reconstructed image. To effectively apply the patch-level operation, we propose adaptive patch control to determine the number of non-overlapped patches to be applied. For this, we synthesize defective images using normal images and examine how well the candidate methods with different numbers of patches remove the synthesized defects. In the same way, we utilize adaptive patch control to select a promising model among the candidate generative models. Based on experiments conducted using the MVTec Anomaly Detection dataset, we demonstrate that our method outperforms previous existing methods. Under a real-world scenario, our method shows ROC AUC of 0.926, in contrast to the best value of 0.893 from existing studies. Furthermore, we prove the feasibility of the adaptive patch control by showing that the removal of the synthesized defects and the anomaly localization for real defective images are highly correlated.

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Section: ) Existing Methods Versus Our Approachmentioning

confidence: 99%

Section: Related Work a Unsupervised Anomaly Localizationmentioning

confidence: 99%

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Patch-Level Operation With Adaptive Patch Control for Improving Anomaly Localization

et al. 2021

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“…As a consequence, receptive fields are limited, feature complexity is rather low and there is an implicit assumption that the anomalies fit inside one patch. As an alternative, [37] propose to initialize the encoder of their variational AEbased approach with the help of a pre-trained ResNet-18 [38]. Still, segmentations have to be aggregated to facilitate AD, adding an additional layer of complexity to segmentationbased approaches.…”

Section: B Ad Via Transfer Learningmentioning

confidence: 99%

Gaussian Anomaly Detection by Modeling the Distribution of Normal Data in Pretrained Deep Features

Rippel

Mertens

Konig

et al. 2021

IEEE Trans. Instrum. Meas.

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Anomaly Detection (AD) in images is a fundamental computer vision problem and refers to identifying images that deviate significantly from normality. State-of-the-art AD algorithms commonly learn a model of normality from scratch using task specific datasets in either semi-supervised or selfsupervised manner. We follow an alternative approach, and model the distribution of normal data in deep feature representations learned from ImageNet via a multivariate Gaussian (MVG). This lightweight approach achieves a new state of the art in AD on the public MVTec AD dataset. In addition to the empirical benefits, we give a clear motivation for the seemingly simplistic approach via the ties between deep generative and discriminative modeling revealed recently. We further elucidate why ImageNet representations are discriminative in the transfer learning AD setting using Principal Component Analysis. Here, we find that the principal components containing little variance in normal data are the ones crucial for discriminating between normal and anomalous instances, giving an explanation for the unreasonable effectiveness of our approach. We also investigate setting the working point of our approach by selecting acceptable False Positive Rate thresholds based on the MVG assumption as well as the resistance of our approach to unlabeled anomalies in the dataset. Finally, we investigate whether our approach is prone to exploiting spurious correlations using explainable AI techniques. Code is publicly available at https://github.com/ORippler/gaussian-ad-mvtec.

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“…We also evaluate RN18 and WRN50 on STC, comparing with the four last reported methods for anomaly localization, including SSIM-AE [16], 2 -AE [26], CAVGA-R u [30] and SPADE [18]. The results of AUROC are listed in Table 2.…”

Section: Comparison With State-of-the-artmentioning

confidence: 99%

A Semantic-Enhanced Method Based On Deep SVDD for Pixel-Wise Anomaly Detection

Chen

Shao

2021

2021 IEEE International Conference on Multimedia and Expo (ICME)

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Detecting the anomalous information in multimedia is valuable to many computer vision applications. Recently, many pixel-wise methods modeling by deep learning model have been presented, which can be divided in reconstruction-based and distance-based methods. However, reconstruction-based methods suffer from the low precision of pixel reconstructions. Distance-based methods extract the hierarchical features by a pre-trained model, in order to estimate the anomalies by distances between normal and anomalous features. Nevertheless, multi-level features are ignored in these methods, and semantic information is not considered which is important to enhance the description of anomalies. To overcome the problems, we propose a novel semantic-enhanced anomaly detection method based on deep Support Vector Data Description (SVDD). A new semantic correlation module (SCB) is introduced to enhance the semantic information of the feature representations by cosine similarity. Meanwhile, the multi-level architecture is utilized to estimate the final pixel-wise anomaly score. Experimental results demonstrate the proposed method outperforms state-of-the-art methods on MVTec and STC dataset.

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Attention Guided Anomaly Localization in Images

Cited by 159 publications

References 45 publications

Patch-Level Operation With Adaptive Patch Control for Improving Anomaly Localization

Patch-Level Operation With Adaptive Patch Control for Improving Anomaly Localization

Gaussian Anomaly Detection by Modeling the Distribution of Normal Data in Pretrained Deep Features

A Semantic-Enhanced Method Based On Deep SVDD for Pixel-Wise Anomaly Detection

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