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|a 10.1109/TIP.2024.3457236
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|a eng
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|a Liu, Binhui
|e verfasserin
|4 aut
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|a Cross-Attention Regression Flow for Defect Detection
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|c 2024
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|a Date Revised 20.09.2024
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|a published: Print-Electronic
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|a Citation Status PubMed-not-MEDLINE
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|a Defect detection from images is a crucial and challenging topic of industry scenarios due to the scarcity and unpredictability of anomalous samples. However, existing defect detection methods exhibit low detection performance when it comes to small-size defects. In this work, we propose a Cross-Attention Regression Flow (CARF) framework to model a compact distribution of normal visual patterns for separating outliers. To retain rich scale information of defects, we build an interactive cross-attention pattern flow module to jointly transform and align distributions of multi-layer features, which is beneficial for detecting small-size defects that may be annihilated in high-level features. To handle the complexity of multi-layer feature distributions, we introduce a layer-conditional autoregression module to improve the fitting capacity of data likelihoods on multi-layer features. By transforming the multi-layer feature distributions into a latent space, we can better characterize normal visual patterns. Extensive experiments on four public datasets and our collected industrial dataset demonstrate that the proposed CARF outperforms state-of-the-art methods, particularly in detecting small-size defects
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|a Journal Article
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|a Guo, Tianchu
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| 700 |
1 |
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|a Luo, Bin
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Cui, Zhen
|e verfasserin
|4 aut
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| 700 |
1 |
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|a Yang, Jian
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t IEEE transactions on image processing : a publication of the IEEE Signal Processing Society
|d 1992
|g 33(2024) vom: 16., Seite 5183-5193
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|u http://dx.doi.org/10.1109/TIP.2024.3457236
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