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| Hauptverfasser: | , , , , , , , , , , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2026
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| Schlagworte: | |
| Online-Zugang: | https://arxiv.org/abs/2604.09023 |
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| _version_ | 1866910118519504896 |
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| author | Pang, Jiahua Li, Ying Cao, Dongpu Luo, Jingcai Zheng, Yanuo Yunfan, Bao Lei, Yujie Yuan, Rui Tian, Yuxi Yuan, Guojin Chen, Hongchang Zheng, Zhi Liu, Yongchun |
| author_facet | Pang, Jiahua Li, Ying Cao, Dongpu Luo, Jingcai Zheng, Yanuo Yunfan, Bao Lei, Yujie Yuan, Rui Tian, Yuxi Yuan, Guojin Chen, Hongchang Zheng, Zhi Liu, Yongchun |
| contents | Multi-task visual anomaly detection is critical for car-related manufacturing quality assessment. However, existing methods remain task-specific, hindered by the absence of a unified benchmark for multi-task evaluation. To fill in this gap, We present the CAD Dataset, a large-scale and comprehensive benchmark designed for car-related multi-task visual anomaly detection. The dataset contains over 100
images crossing 7 vehicle domains and 3 tasks, providing models a comprehensive view for car-related anomaly detection. It is the first car-related anomaly dataset specialized for multi-task learning(MTL), while combining synthesis data augmentation for few-shot anomaly images. We implement a multi-task baseline and conduct extensive empirical studies. Results show MTL promotes task interaction and knowledge transfer, while also exposing challenging conflicts between tasks. The CAD dataset serves as a standardized platform to drive future advances in car-related multi-task visual anomaly detection. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2604_09023 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | CAD 100K: A Comprehensive Multi-Task Dataset for Car Related Visual Anomaly Detection Pang, Jiahua Li, Ying Cao, Dongpu Luo, Jingcai Zheng, Yanuo Yunfan, Bao Lei, Yujie Yuan, Rui Tian, Yuxi Yuan, Guojin Chen, Hongchang Zheng, Zhi Liu, Yongchun Computer Vision and Pattern Recognition Multi-task visual anomaly detection is critical for car-related manufacturing quality assessment. However, existing methods remain task-specific, hindered by the absence of a unified benchmark for multi-task evaluation. To fill in this gap, We present the CAD Dataset, a large-scale and comprehensive benchmark designed for car-related multi-task visual anomaly detection. The dataset contains over 100 images crossing 7 vehicle domains and 3 tasks, providing models a comprehensive view for car-related anomaly detection. It is the first car-related anomaly dataset specialized for multi-task learning(MTL), while combining synthesis data augmentation for few-shot anomaly images. We implement a multi-task baseline and conduct extensive empirical studies. Results show MTL promotes task interaction and knowledge transfer, while also exposing challenging conflicts between tasks. The CAD dataset serves as a standardized platform to drive future advances in car-related multi-task visual anomaly detection. |
| title | CAD 100K: A Comprehensive Multi-Task Dataset for Car Related Visual Anomaly Detection |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2604.09023 |