ZERO 2.2: Sharper Text-Prompt Accuracy and Improved Korean Prompt Understanding
Blog post from Superb AI
Superb AI has released ZERO 2.2, an updated industry-focused Vision Foundation Model available through the Labson Superb Platform and AWS Marketplace, designed to detect previously unseen objects using text prompts or boxed image examples without collecting data, labeling images, or retraining models. The update improves text-based detection accuracy, reduces missed detections, supports Korean and Japanese prompts, recognizes descriptive phrases such as color and clothing attributes, and provides clearer confidence-score separation to simplify threshold selection in operational settings. Example-image prompting enables users to identify visually similar or unnamed products by selecting one instance, supporting applications in manufacturing inspection, logistics, retail analytics, and security monitoring. Superb AI reports that the model was trained on roughly 900,000 curated industrial images and outperformed open models across 37 industrial datasets, while its underlying approach won first place in CVPR 2026’s Foundational Few-Shot Object Detection Challenge with an mAP of 53.9. Users can test the model without installation on the Labson platform or deploy it as a usage-based Amazon SageMaker endpoint through AWS Marketplace.
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