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Developing data-efficient instance detection models that can handle rareobject categories remains a key challenge in computer vision. However, existingresearch often overlooks data collection strategies and evaluation metricstailored to real-world scenarios involving neural networks. In this study, wesystematically investigate data collection and augmentation techniques focusedon object occlusion, aiming to mimic occlusion relationships observed inpractical applications. Surprisingly, we find that even a simple occlusionmechanism is sufficient to achieve strong performance when introducing newobject categories. Notably, by adding just 15 images of a new category to alarge-scale training dataset containing over half a million images acrosshundreds of categories, the model achieves 95\% accuracy on an unseen test setwith thousands of instances of the new category.

Adding New Categories in Object Detection Using Few-Shot Copy-Paste