The recent rapid development of deep learning has laid a milestone in industrial image
anomaly detection (IAD). In this paper, we provide a comprehensive review of deep learning …

Deep learning breakthrough stimulates new research trends in civil infrastructure inspection,
whereas the lack of quality-guaranteed, human-annotated, free-of-charge, and publicly …

Anomaly inspection plays an important role in industrial manufacture. Existing anomaly
inspection methods are limited in their performance due to insufficient anomaly data …

Despite the great success of GANs in images translation with different conditioned inputs
such as semantic segmentation and edge map, generating high-fidelity images with …

Image inpainting is an underdetermined inverse problem, which naturally allows diverse
contents to fill up the missing or corrupted regions realistically. Prevalent approaches using …

The performances of defect inspection have been severely hindered by insufficient defect
images in industries, which can be alleviated by generating more samples as data …

Anomaly detection (AD) aims to identify defective images and localize their defects (if any).
Ideally AD models should be able to detect defects over many image classes; without relying …

Limited data usually cause deep neural networks to hold poor performance after training,
and many generative models are proposed to synthesize data to improve the performance of …

As the scale of vision models continues to grow, the emergence of Visual Prompt Tuning
(VPT) as a parameter-efficient transfer learning technique has gained attention due to its …

The defect diagnosis of modern infrastructures is crucial to public safety. In this work, we
propose an unsupervised domain adaptive crack recognition framework. To fulfill the …