{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/towards-efficient-pixel-labeling-for","title":"Towards Efficient Pixel Labeling for Industrial Anomaly Detection and Localization","arxiv_id":"2407.03130","date":"2024-07-03","proceeding":null,"authors":["Hanxi Li","Jingqi Wu","Lin Yuanbo Wu","Hao Chen","Deyin Liu","Chunhua Shen"],"abstract":"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).","url_abs":"https://arxiv.org/abs/2407.03130v2","url_pdf":"https://arxiv.org/pdf/2407.03130v2.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[],"tasks":[{"task_slug":"anomaly-detection","task_name":"Anomaly Detection"},{"task_slug":"image-segmentation","task_name":"Image Segmentation"},{"task_slug":"semantic-segmentation","task_name":"Semantic Segmentation"},{"task_slug":"supervised-anomaly-detection","task_name":"Supervised Anomaly Detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[{"leaderboard":"/sota/anomaly-detection-on-mvtec-ad","task":"Anomaly Detection","dataset":"MVTec AD","model":"ADClick","rank_in_archive_order":11,"of":148,"metrics":{"Detection AUROC":"99.7","Segmentation AP":"82.9","Segmentation AUPRO":"97.8","Segmentation AUROC":"99.2"},"uses_additional_data":true},{"leaderboard":"/sota/supervised-anomaly-detection-on-mvtec-ad","task":"Supervised Anomaly Detection","dataset":"MVTec AD","model":"ADClick","rank_in_archive_order":3,"of":8,"metrics":{"Detection AUROC":"99.6","Segmentation AP":"86.4","Segmentation AUPRO":"98.2","Segmentation AUROC":"99.6"},"uses_additional_data":true}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}