Výsledky vyhledávání - "Zhang, Xingwu"

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Small Object Few-shot Segmentation for Vision-based Industrial Inspection

Autor: Zhang, Zilong, Niu, Chang, Zhao, Zhibin, Zhang, Xingwu, Chen, Xuefeng

Vision-based industrial inspection (VII) aims to locate defects quickly and accurately. Supervised learning under a close-set setting and industrial anomaly detection, as two common paradigms in VII, face different problems in practical applications.

Externí odkaz: http://arxiv.org/abs/2407.21351

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CA2: Class-Agnostic Adaptive Feature Adaptation for One-class Classification

Autor: Zhang, Zilong, Zhao, Zhibin, Meng, Deyu, Zhang, Xingwu, Chen, Xuefeng

One-class classification (OCC), i.e., identifying whether an example belongs to the same distribution as the training data, is essential for deploying machine learning models in the real world. Adapting the pre-trained features on the target dataset

Externí odkaz: http://arxiv.org/abs/2309.01483

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Industrial Anomaly Detection with Domain Shift: A Real-world Dataset and Masked Multi-scale Reconstruction

Autor: Zhang, Zilong, Zhao, Zhibin, Zhang, Xingwu, Sun, Chuang, Chen, Xuefeng

Industrial anomaly detection (IAD) is crucial for automating industrial quality inspection. The diversity of the datasets is the foundation for developing comprehensive IAD algorithms. Existing IAD datasets focus on the diversity of data categories,

Externí odkaz: http://arxiv.org/abs/2304.02216

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