SeaS: Few-shot Industrial Anomaly Image Generation with Separation and Sharing Fine-tuning
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| Autores principales: | , , , , , |
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| Formato: | Preprint |
| Publicado: |
2024
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| _version_ | 1866915394189524992 |
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| author | Dai, Zhewei Zeng, Shilei Liu, Haotian Li, Xurui Xue, Feng Zhou, Yu |
| author_facet | Dai, Zhewei Zeng, Shilei Liu, Haotian Li, Xurui Xue, Feng Zhou, Yu |
| contents | We introduce SeaS, a unified industrial generative model for automatically creating diverse anomalies, authentic normal products, and precise anomaly masks. While extensive research exists, most efforts either focus on specific tasks, i.e., anomalies or normal products only, or require separate models for each anomaly type. Consequently, prior methods either offer limited generative capability or depend on a vast array of anomaly-specific models. We demonstrate that U-Net's differentiated learning ability captures the distinct visual traits of slightly-varied normal products and diverse anomalies, enabling us to construct a unified model for all tasks. Specifically, we first introduce an Unbalanced Abnormal (UA) Text Prompt, comprising one normal token and multiple anomaly tokens. More importantly, our Decoupled Anomaly Alignment (DA) loss decouples anomaly attributes and binds them to distinct anomaly tokens of UA, enabling SeaS to create unseen anomalies by recombining these attributes. Furthermore, our Normal-image Alignment (NA) loss aligns the normal token to normal patterns, making generated normal products globally consistent and locally varied. Finally, SeaS produces accurate anomaly masks by fusing discriminative U-Net features with high-resolution VAE features. SeaS sets a new benchmark for industrial generation, significantly enhancing downstream applications, with average improvements of $+8.66\%$ pixel-level AP for synthesis-based AD approaches, $+1.10\%$ image-level AP for unsupervised AD methods, and $+12.79\%$ IoU for supervised segmentation models. Code is available at \href{https://github.com/HUST-SLOW/SeaS}{https://github.com/HUST-SLOW/SeaS}. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_14987 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | SeaS: Few-shot Industrial Anomaly Image Generation with Separation and Sharing Fine-tuning Dai, Zhewei Zeng, Shilei Liu, Haotian Li, Xurui Xue, Feng Zhou, Yu Computer Vision and Pattern Recognition We introduce SeaS, a unified industrial generative model for automatically creating diverse anomalies, authentic normal products, and precise anomaly masks. While extensive research exists, most efforts either focus on specific tasks, i.e., anomalies or normal products only, or require separate models for each anomaly type. Consequently, prior methods either offer limited generative capability or depend on a vast array of anomaly-specific models. We demonstrate that U-Net's differentiated learning ability captures the distinct visual traits of slightly-varied normal products and diverse anomalies, enabling us to construct a unified model for all tasks. Specifically, we first introduce an Unbalanced Abnormal (UA) Text Prompt, comprising one normal token and multiple anomaly tokens. More importantly, our Decoupled Anomaly Alignment (DA) loss decouples anomaly attributes and binds them to distinct anomaly tokens of UA, enabling SeaS to create unseen anomalies by recombining these attributes. Furthermore, our Normal-image Alignment (NA) loss aligns the normal token to normal patterns, making generated normal products globally consistent and locally varied. Finally, SeaS produces accurate anomaly masks by fusing discriminative U-Net features with high-resolution VAE features. SeaS sets a new benchmark for industrial generation, significantly enhancing downstream applications, with average improvements of $+8.66\%$ pixel-level AP for synthesis-based AD approaches, $+1.10\%$ image-level AP for unsupervised AD methods, and $+12.79\%$ IoU for supervised segmentation models. Code is available at \href{https://github.com/HUST-SLOW/SeaS}{https://github.com/HUST-SLOW/SeaS}. |
| title | SeaS: Few-shot Industrial Anomaly Image Generation with Separation and Sharing Fine-tuning |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2410.14987 |