ZAC : Zero-shot Anomaly-Aware Counting for Industrial Components

Counting industrial components is essential in real-world manufacturing systems, and what matters is not only how many parts there are but how many are usable, since production lines carry defective items alongside intact ones. As manufacturing lines continually introduce new components with distinct defect types, no single model can be trained on every class it will encounter. To address this, we introduce Zero-shot Anomaly-Aware Counting, a task that counts objects while distinguishing their defect states. Traditional object counting models focus on quantity and ignore quality, while industrial anomaly detection models evaluate quality but struggle with dense multi-object scenes. Multimodal large language models can be queried for either, but their performance drops substantially once a query asks which of the objects they have found are defective. These failures share a cause, since existing benchmarks do not combine instance-level defect-state supervision with class-disjoint evaluation on unseen component classes. Therefore, we construct Real-ZAC, a benchmark of real industrial components in dense multi-instance settings. Every instance is annotated with an intact or defective label, and test classes are strictly withheld from training to evaluate zero-shot capabilities. Furthermore, we propose a baseline method that utilizes the multi-instance structure of Real-ZAC. The model suppresses visual patterns shared across instances and emphasizes subtle defect-related deviations, reducing reliance on class-specific appearance and improving generalization to unseen component classes. The code and dataset will be available soon.