Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Attention Guided Anomaly Localization in Images   Anomaly localization is an important problem in computer vision which
involves localizing anomalous regions within images with applications in
industrial inspection, surveillance, and medical imaging. This task is
challenging due to the small sample size and pixel coverage of the anomaly in
real-world scenarios. Most prior works need to use anomalous training images to
compute a class-specific threshold to localize anomalies. Without the need of
anomalous training images, we propose Convolutional Adversarial Variational
autoencoder with Guided Attention (CAVGA), which localizes the anomaly with a
convolutional latent variable to preserve the spatial information. In the
unsupervised setting, we propose an attention expansion loss where we encourage
CAVGA to focus on all normal regions in the image. Furthermore, in the
weakly-supervised setting we propose a complementary guided attention loss,
where we encourage the attention map to focus on all normal regions while
minimizing the attention map corresponding to anomalous regions in the image.
CAVGA outperforms the state-of-the-art (SOTA) anomaly localization methods on
MVTec Anomaly Detection (MVTAD), modified ShanghaiTech Campus (mSTC) and
Large-scale Attention based Glaucoma (LAG) datasets in the unsupervised setting
and when using only 2% anomalous images in the weakly-supervised setting. CAVGA
also outperforms SOTA anomaly detection methods on the MNIST, CIFAR-10,
Fashion-MNIST, MVTAD, mSTC and LAG datasets.

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The categories of this paper are: nucl-ex
20
The categories of this paper are: acc-phys
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The categories of this paper are: bayes-an
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The categories of this paper are: q-bio
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The categories of this paper are: adap-org
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The categories of this paper are: nlin
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The categories of this paper are: nucl-th
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The categories of this paper are: hep-ex
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The categories of this paper are: math-ph
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The categories of this paper are: atom-ph
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The categories of this paper are: physics
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The categories of this paper are: astro-ph
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The categories of this paper are: solv-int
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The categories of this paper are: stat
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The categories of this paper are: q-alg
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The categories of this paper are: mtrl-th