In the realm of practical Anomaly Detection (AD) tasks, manual labeling of anomalous pixels proves to be a costly endeavor. Consequently, many AD methods are crafted as one-class classifiers, tailored for training sets completely devoid of anomalies, ensuring a more cost-effective approach. While some pioneering work has demonstrated heightened AD accuracy by incorporating real anomaly samples in training, this enhancement comes at the price of labor-intensive labeling processes. This paper strikes the balance between AD accuracy and labeling expenses by introducing ADClick, a novel Interactive Image Segmentation (IIS) algorithm. ADClick efficiently generates "ground-truth" anomaly masks for real defective images, leveraging innovative residual features and meticulously crafted language prompts. Notably, ADClick showcases a significantly elevated generalization capacity compared to existing state-of-the-art IIS approaches. Functioning as an anomaly labeling tool, ADClick generates high-quality anomaly labels (AP =94.1% on MVTec AD) based on only 3 to 5 manual click annotations per training image. Furthermore, we extend the capabilities of ADClick into ADClick-Seg, an enhanced model designed for anomaly detection and localization. By fine-tuning the ADClick-Seg model using the weak labels inferred by ADClick, we establish the state-of-the-art performances in supervised AD tasks (AP =86.4% on MVTec AD and AP =78.4%, PRO =98.6% on KSDD2).
@article{arxiv.2407.03130,
title = {Towards Efficient Pixel Labeling for Industrial Anomaly Detection and Localization},
author = {Hanxi Li and Jingqi Wu and Lin Yuanbo Wu and Hao Chen and Deyin Liu and Chunhua Shen},
journal= {arXiv preprint arXiv:2407.03130},
year = {2024}
}