Papers › Towards Efficient Pixel Labeling for Industrial Anomaly Detection and Localization

Towards Efficient Pixel Labeling for Industrial Anomaly Detection and Localization

3 Jul 2024arXiv:2407.03130archive 2025-07-28

Hanxi Li, Jingqi Wu, Lin Yuanbo Wu, Hao Chen, Deyin Liu, Chunhua Shen

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).

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Tasks

Anomaly Detection Image Segmentation Semantic Segmentation Supervised Anomaly Detection

Results from the paper archive 2025-07-28

Task	Dataset	Model	Metric	Value	Rank at snapshot	Leaderboard	Report
Anomaly Detection	MVTec AD	ADClick	Detection AUROC	99.7	#11 of 148	Archive leaderboard	report
Anomaly Detection	MVTec AD	ADClick	Segmentation AP	82.9	#11 of 148	Archive leaderboard	report
Anomaly Detection	MVTec AD	ADClick	Segmentation AUPRO	97.8	#11 of 148	Archive leaderboard	report
Anomaly Detection	MVTec AD	ADClick	Segmentation AUROC	99.2	#11 of 148	Archive leaderboard	report
Supervised Anomaly Detection	MVTec AD	ADClick	Detection AUROC	99.6	#3 of 8	Archive leaderboard	report
Supervised Anomaly Detection	MVTec AD	ADClick	Segmentation AP	86.4	#3 of 8	Archive leaderboard	report
Supervised Anomaly Detection	MVTec AD	ADClick	Segmentation AUPRO	98.2	#3 of 8	Archive leaderboard	report
Supervised Anomaly Detection	MVTec AD	ADClick	Segmentation AUROC	99.6	#3 of 8	Archive leaderboard	report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

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